In the Rectangle Stabbing problem, input is a set R of axis-parallel rectangles and a set L of axis-parallel lines in the plane. The task is to find a minimum size set L^* ⊆ L such that for every rectangle R ∈ R there is a line 𝓁 ∈ L^* such that 𝓁 intersects R. Gaur et al. [Journal of Algorithms, 2002] gave a polynomial time 2-approximation algorithm, while Dom et al. [WALCOM 2009] and Giannopoulos et al. [EuroCG 2009] independently showed that, assuming FPT ≠ W[1], there is no algorithm with running time f(k)(|L||R|)^O(1) that determines whether there exists an optimal solution with at most k lines. We give the first parameterized approximation algorithm for the problem with a ratio better than 2. In particular we give an algorithm that given R, L, and an integer k runs in time k^O(k)(|L||R|)^O(1) and either correctly concludes that there does not exist a solution with at most k lines, or produces a solution with at most 7k/4 lines. We complement our algorithm by showing that unless FPT = W[1], the Rectangle Stabbing problem does not admit a (5/4-ε)-approximation algorithm running in f(k)(|L||R|)^O(1) time for any function f and ε > 0.

A multipacking in an undirected graph G = (V, E) is a set M ⊆ V such that for every vertex v ∈ V and for every integer r ≥ 1, the ball of radius r around v contains at most r vertices of M, that is, there are at most r vertices in M at a distance at most r from v in G. The Multipacking problem asks whether a graph contains a multipacking of size at least k. For more than a decade, it remained an open question whether the Multipacking problem is NP-complete or solvable in polynomial time, although the problem is known to be polynomial-time solvable for certain graph classes (e.g., strongly chordal graphs, grids, etc). Foucaud, Gras, Perez, and Sikora [Foucaud et al., 2021] [Algorithmica 2021] made a step towards solving the open question by showing that the Multipacking problem is NP-complete for directed graphs and W[1]-hard when parameterized by the solution size. In this paper, we prove that the Multipacking problem is NP-complete on undirected graphs, which answers the open question. Moreover, the problem is W[2]-hard on undirected graphs when parameterized by the solution size. Furthermore, we show that the problem is NP-complete and W[2]-hard (parameterized by solution size) on chordal, bipartite, and claw-free graphs, and remains NP-complete on regular and CONV graphs (intersection graphs of convex sets in the plane). Additionally, the problem is NP-complete and W[2]-hard (parameterized by the solution size) on chordal ∩ 1/2-hyperbolic graphs, which is a superclass of strongly chordal graphs on which the problem is polynomial-time solvable. On the positive side, we present an exact exponential-time algorithm for the Multipacking problem on general graphs that breaks the 2ⁿ barrier, with running time O^*(1.58ⁿ), where n is the number of vertices.

Geometric covering problems typically ask for a small family of geometric objects whose union contains all input points. In this paper we study a more rigid variant, boundary covering, where every point must lie on the boundary of at least one chosen object. Motivated by the framework of Langerman and Morin [Discret. Comput. Geom., 2005] for boundary covering by hyperspheres, we initiate a systematic study of boundary covering by axis-parallel rectangles in the plane. We first consider the discrete setting, where the rectangles must be chosen from a given family. We define Boundary Covering with Discrete Axis-Parallel Rectangles (BCDAPR) as follows: given a point set P ⊆ ℝ², a collection ℛ of axis-parallel rectangles, and an integer k, decide whether P can be covered by the boundaries of at most k rectangles from ℛ. We prove that this discrete boundary-covering problem is W[1]-hard when parameterized by k. This motivates the continuous variant, where we are allowed to place rectangles freely. We define Boundary Covering with Continuous Axis-Parallel Rectangles (BCCAPR) as follows: given a point set P ⊆ ℝ² and an integer k, decide whether P can be covered by the boundaries of at most k axis-parallel rectangles. In contrast to the discrete case, we show that BCCAPR is fixed-parameter tractable parameterized by k, with running time 2^𝒪(k log k) ⋅ n^𝒪(1), where n = |P|. Our results does a fine-grained structural analysis of how k rectangles can interact with the point set. On the hardness side, we show that moving from lines to slightly richer shapes already incurs intractability: we prove NP-completeness for boundary covering by axis-aligned L-shapes, and then lift it to NP-completeness of BCCAPR. For the algorithm we reduce BCCAPR to at most 2^𝒪(k log k) instances of Distinct Domain Monotone ,$-CSP, each solvable in polynomial time.

We revisit connectivity-constrained coverage through a unifying model, Partial Connected Red-Blue Dominating Set (PartialConRBDS). Given a bipartite graph G = (R∪ B,E) with red vertices R and blue vertices B, an auxiliary connectivity graph G_{conn} on R, and integers k,t, the task is to find a set S ⊆ R with |S| ≤ k such that G_{conn}[S] is connected and S dominates at least t blue vertices. This formulation captures connected variants of Maximum Coverage [Hochbaum-Rao, Inf. Proc. Lett., 2020; D'Angelo-Delfaraz, AAMAS 2025], Partial Vertex Cover, and Partial Dominating Set [Khuller et al., SODA 2014; Lamprou et al., TCS 2021] via standard encodings. Limits to parameterized tractability. PartialConRBDS is W[1]-hard parameterized by k even under strong restrictions: it remains hard when G_{conn} is a clique or a star and the incidence graph G is 3-degenerate, or when G is K_{2,2}-free. Inapproximability. For every ε > 0, there is no polynomial-time (1, 1-1/e+ε)-approximation unless 𝖯 = NP. Moreover, under ETH, no algorithm running in f(k)⋅ n^{o(k)} time achieves an g(k)-approximation for k for any computable function g(⋅), or for any ε > 0, a (1-1/e+ε)-approximation for t. Graphical special cases. Partial Connected Dominating Set is W[2]-hard parameterized by k and inherits the same ETH-based f(k)⋅ n^{o(k)} inapproximability bound as above; Partial Connected Vertex Cover is W[1]-hard parameterized by k. These hardness boundaries delineate a natural "sweet spot" for study: within appropriate structural restrictions on the incidence graph, one can still aim for fine-grained (FPT) approximations. Our algorithms. We solve PartialConRBDS exactly by reducing it to Relaxed Directed Steiner Out-Tree in time (2e)^t ⋅ n^{𝒪(1)}. For biclique-free incidences (i.e., when G excludes K_{d,d} as an induced subgraph), we obtain two complementary parameterized schemes: - An Efficient Parameterized Approximation Scheme (EPAS) running in time 2^{𝒪(k² d/ε)}⋅ n^{𝒪(1)} that either returns a connected solution of size at most k covering at least (1-ε)t blue vertices, or correctly reports that no connected size-k solution covers t; and - A Parameterized Approximation Scheme (PAS) running in time 2^{𝒪(kd(k²+log d))}⋅ n^{𝒪(1/ε)} that either returns a connected solution of size at most (1+ε)k covering at least t blue vertices, or correctly reports that no connected size-k solution covers t. Together, these results chart the boundary between hardness and FPT-approximability for connectivity-constrained coverage.

A layout of a graph G is an injective function f : V(G) → ℤ, and the bandwidth of a layout f is (G,f) = maxuv ∈ E(G) |f(u) − f(v)|. The bandwidth (G) of G is the minimum bandwidth of a layout of G. Computing the bandwidth of a graph is a notoriously hard problem: assuming P ≠ NP there is no polynomial time algorithm, even on very restricted classes of trees [Monien, SIAM Journal on Algebraic Discrete Methods, 1986], and no constant factor approximation, even on trees [Dubey et al., JCSS 2011]. Assuming the Exponential Time Hypothesis there is no algorithm with running time f(k)no(k) to determine whether an input graph has bandwidth at most k, even on very restricted classes of trees [Dregi and Lokshtanov, ICALP 2014].

Courcelle’s theorem states that there exists an algorithm that takes as input a graph G of treewidth at most t and a MSO formula φ, and determines whether G satisfies φ in time f(φ,t) · n. It is folklore that the function f contains a tower of exponentials whose height depends as a linear function of the number of quantifier alternations of the input formula φ. A classic reduction of Frick and Grohe shows that, assuming the Exponential Time Hypothesis (ETH), the linear growth of the height of the tower is unavoidable. Nevertheless, there is still a huge gap between existing upper and lower bounds – after all, there is quite a difference between a single exponential and a double exponential running time. In addition, this only gives us a very coarse understanding in the time complexity of Courcelle’s theorem. In this paper, we prove a fine-grained version of Courcelle’s theorem with nearly ETH-tight dependence on the treewidth parameter t and the quantifier structure of φ (specifically, the number of first order and second order variables in each quantifier alternation block).

We clarify the design principles and evaluation choices underlying Syotti, a robust and scalable probe-design tool developed to support large, heterogeneous bacterial datasets with minimal parameter tuning. We highlight Syotti's ability to perform simultaneous large-scale designs and its effectiveness as a reliable alternative when existing tools such as CATCH are not well suited to the problem setting.

We study the problem of Envy-Free Incomplete Connected Fair Division, where exactly p vertices of an undirected graph must be allocated to agents such that each agent receives a connected share and does not envy another agent’s share. Focusing on agents with additive valuations, we show that the problem remains computationally hard when parameterized by p and the number of agents. This result holds even for star graphs and with the input numbers given in unary representation, thereby resolving an open problem posed by Gahlawat and Zehavi (FSTTCS 2023). In stark contrast, we show that if one is willing to tolerate even the slightest amount of envy, then the problem becomes efficient with respect to the natural parameters. Specifically, we design an Efficient Parameterized Approximation Scheme parameterized by p and the number of agent types. Our algorithm works on general graphs and remains efficient even when the input numbers are provided in binary representation.

We prove a robust contraction decomposition theorem for $H$-minor-free graphs, which states that given an $H$-minor-free graph $G$ and an integer $p$, one can partition in polynomial time the vertices of $G$ into $p$ sets $Z_1,\dots,Z_p$ such that $\operatorname{tw}(G/(Z_i \setminus Z')) = O(p + |Z'|)$ for all $i \in [p]$ and $Z' \subseteq Z_i$. Here, $\operatorname{tw}(\cdot)$ denotes the treewidth of a graph and $G/(Z_i \setminus Z')$ denotes the graph obtained from $G$ by contracting all edges with both endpoints in $Z_i \setminus Z'$. Our result generalizes earlier results by Klein [SICOMP 2008] and Demaine et al. [STOC 2011] based on partitioning $E(G)$, and some recent theorems for planar graphs by Marx et al. [SODA 2022], for bounded-genus graphs (more generally, almost-embeddable graphs) by Bandyapadhyay et al. [SODA 2022], and for unit-disk graphs by Bandyapadhyay et al. [SoCG 2022]. The robust contraction decomposition theorem directly results in parameterized algorithms with running time $2^{\widetilde{O}(\sqrt{k})} \cdot n^{O(1)}$ or $n^{O(\sqrt{k})}$ for every vertex/edge deletion problems on $H$-minor-free graphs that can be formulated as Permutation CSP Deletion or 2-Conn Permutation CSP Deletion. Consequently, we obtain the first subexponential-time parameterized algorithms for Subset Feedback Vertex Set, Subset Odd Cycle Transversal, Subset Group Feedback Vertex Set, 2-Conn Component Order Connectivity on $H$-minor-free graphs. For other problems which already have subexponential-time parameterized algorithms on $H$-minor-free graphs (e.g., Odd Cycle Transversal, Vertex Multiway Cut, Vertex Multicut, etc.), our theorem gives much simpler algorithms of the same running time.

In this paper, we suggest to extend the notion of a kernel to permit the kernelization algorithm to be executed in quasi-polynomial time rather than polynomial time. So far, we are only aware of one work that addressed this negatively, showing that some lower bounds on kernel sizes proved for kernelization also hold when quasi-polynomial time complexity is allowed. When we, anyway, deal with an NP-hard problem, sacrificing polynomial time in preprocessing for quasi-polynomial time may often not be a big deal, but, of course, the question is - does it give us more power? The only known work, mentioned above, seems to suggest that the answer is "no". In this paper, we show that this is not the case - in particular, we show that this notion is extremely powerful for derandomization. Some of the most basic kernelization algorithms in the field are based on inherently randomized tools whose derandomization is a huge problem that has remained (and may still remain) open for many decades. Still, some breakthrough advances for derandomization in quasi-polynomial time have been made. Can we harness these advancements to design quasi-polynomial deterministic kernelization algorithms for basic problems in the field? To this end, we revisit the question of deterministic polynomial-time computation of a linear representation of transversal matroids and gammoids, which is a longstanding open problem. We present a deterministic computation of a representation matrix of a transversal matroid in time quasipolynomial in the rank of the matroid, where each entry of the matrix can be represented in quasipolynomial (in the rank of the matroid) bits. As a corollary, we obtain a linear representation of a gammoid in deterministic quasipolynomial time and quasipolynomial bits in the size of the underlying ground set of the gammoid. In turn, as applications of our results, we present deterministic quasi-polynomial time kernels of polynomial size for several central problems in the field.

The Euclidean Traveling Salesman Problem with Neighborhoods (ETSPN) is a well-studied problem in computational geometry and has a wealth of results. In this problem, given a set of geometric neighborhoods (or regions), the goal is to compute a shortest route that visits at least one point of each neighborhood. The problem is a generalization of the standard Euclidean TSP and hence is also NP-hard, even when the neighborhoods are disjoint unit disks or unit squares in the plane. A longstanding open problem on this topic is the existence of PTASes for ETSPN with unit disk (and unit square) neighborhoods. Prior to this work, the best-known approximation factor for unit disks is 6.75, and PTASes are only known for the special case where the unit disks/squares are of bounded depth, i.e., each point lies in at most a constant number of disks.

We prove that the tree independence number of every even-hole-free graph is at most polylogarithmic in its number of vertices. More explicitly, we prove that there exists a constant c > 0 such that for every integer n > 1 every n-vertex even-hole-free graph has a tree decomposition where each bag has stability (independence) number at most clog10 n. This implies that the Maximum Weight Independent Set problem, as well as several other natural algorithmic problems that are known to be NP-hard in general, can be solved in quasipolynomial time if the input graph is even-hole-free. The quasi-polynomial complexity will remain the same even if the exponent of the logarithm is reduced to 1 (which would be asymptotically best possible).

In the Hedge Cut problem, the edges of a graph are partitioned into groups called hedges, and the question is what is the minimum number of hedges to delete to disconnect the graph. Ghaffari, Karger, and Panigrahi [SODA 2017] showed that Hedge Cut can be solved in quasipolynomial-time, raising the hope for a polynomial time algorithm. Jaffke, Lima, Masarik, Pilipczuk, and Souza [SODA 2023] complemented this result by showing that assuming the Exponential Time Hypothesis (ETH), no polynomial-time algorithm exists. In this paper, we show that Hedge Cut is fixed-parameter tractable parameterized by the solution size ℓ by providing an algorithm with running time , which can be upper bounded by Cℓ · (n + m)O (1) for any constant c > 1. This running time captures at the same time the fact that the problem is quasipolynomial-time solvable, and that it is fixed-parameter tractable parameterized by ℓ. We further generalize this algorithm to an algorithm with running time for Hedge k-CUT.

A drawing of an (undirected) graph G is a mapping φ that assigns to each vertex a distinct point in the plane and to each edge uw a continuous curve φ (uυ ) in the plane from φ (η ) to φ(ν ), not passing through the image of any other vertex. Two edges e and f cross in a point p if p € φ (e ) ∩ φ (f ) and p is not the image of a vertex of G. In a drawing no three edges are allowed to cross in the same point. The crossing number of a drawing of G is the number of points p such that some two edges e and f cross in p. In the CROSSING Number problem, the input consists of a graph G and integer k. The task is to determine whether there exists a drawing of G with crossing number at most k, and to output such a drawing if it exists.

In the CAPACITATED d-HlTTING Set problem input is a universe U equipped with a capacity function cap : U → ℕ, and a collection A of subsets of U, each of size at most d. The task is to find a minimum size subset S of U and an assignment φ : A → S such that, for every set A ∈ A we have φ (Α ) ∈ A and for every x ∈ U we have |φ-1(χ)| ≤ cap(x ). Here φ-1(χ) is the collection of sets in A mapped to x by φ. Such a set S is called a capacitated hitting set. When d = 2 the problem is known under the name CAPACITATED VERTEX COVER. In Weighted Capacitated d-HlTTING Set each element of U has a positive integer weight and the goal is to find a capacitated hitting set of minimum weight.

The classic greedy coloring (first-fit) algorithm considers the vertices of an input graph $G$ in a given order and assigns the first available color to each vertex $v$ in $G$. In the {\sc Grundy Coloring} problem, the task is to find an ordering of the vertices that will force the greedy algorithm to use as many colors as possible. In the {\sc Partial Grundy Coloring}, the task is also to color the graph using as many colors as possible. This time, however, we may select both the ordering in which the vertices are considered and which color to assign the vertex. The only constraint is that the color assigned to a vertex $v$ is a color previously used for another vertex if such a color is available. Whether {\sc Grundy Coloring} and {\sc Partial Grundy Coloring} admit fixed-parameter tractable (FPT) algorithms, algorithms with running time $f(k)n^{\OO(1)}$, where $k$ is the number of colors, was posed as an open problem by Zaker and by Effantin et al., respectively. Recently, Aboulker et al. (STACS 2020 and Algorithmica 2022) resolved the question for \Grundycol\ in the negative by showing that the problem is W[1]-hard. For {\sc Partial Grundy Coloring}, they obtain an FPT algorithm on graphs that do not contain $K_{i,j}$ as a subgraph (a.k.a. $K_{i,j}$-free graphs). Aboulker et al.~re-iterate the question of whether there exists an FPT algorithm for {\sc Partial Grundy Coloring} on general graphs and also asks whether {\sc Grundy Coloring} admits an FPT algorithm on $K_{i,j}$-free graphs. We give FPT algorithms for {\sc Partial Grundy Coloring} on general graphs and for {\sc Grundy Coloring} on $K_{i,j}$-free graphs, resolving both the questions in the affirmative. We believe that our new structural theorems for partial Grundy coloring and ``representative-family'' like sets for $K_{i,j}$-free graphs that we use in obtaining our results may have wider algorithmic applications.

We design an algorithm that generates an n-vertex unlabeled chordal graph uniformly at random in expected polynomial time. Along the way, we develop the following two results: (1) an FPT algorithm for counting and sampling labeled chordal graphs with a given automorphism π, parameterized by the number of moved points of π, and (2) a proof that the probability that a random n-vertex labeled chordal graph has a given automorphism π ∈ S_n is at most 1/2^{c max{μ²,n}}, where μ is the number of moved points of π and c is a constant. Our algorithm for sampling unlabeled chordal graphs calls the aforementioned FPT algorithm as a black box with potentially large values of the parameter μ, but the probability of calling this algorithm with a large value of μ is exponentially small.

Efficient query evaluation in databases and solving constraint satisfaction problems (CSPs) are crucial for improving performance in many real-world applications, from large-scale data management to decision-making systems. These problems naturally admit hypergraph representations, and are efficiently solvable using hypertree decomposition techniques, when the decomposition width is small. However, these techniques require finding small-width decompositions efficiently. This remains a significant challenge despite research from both the database and theory communities. In this work we present Ralph (Randomized Approximation using Linear Programming for Hypertree-Decompositions), a fast algorithm to compute low width fractional and generalized hypertree decompositions for input hypergraphs, as well as lower bounds for these widths. We build on the recent breakthrough by Korchemna et al. [FOCS 2024], which introduced the first polynomial time approximation algorithm for fractional (generalized) hypertree width. Our approach combines this theoretical advancement with practical heuristic improvements utilizing (mixed-integer) linear programs. Along the way, we present new algorithms with strong theoretical guarantees. Through empirical evaluation on the nearly 3700 instances of HyperBench, a well established benchmark suite for hypertree decompositions, we find near optimal decompositions for all previously solved instances and low width decompositions for all 500 previously unsolved instances, effectively pushing state-of-the-art.

Abstract. In the disjoint paths problem, the input is an undirected graph [Formula: see text] on [Formula: see text] vertices and a set of [Formula: see text] vertex pairs, [Formula: see text], and the task is to find [Formula: see text] pairwise vertex-disjoint paths such that the [Formula: see text]’th path connects [Formula: see text] to [Formula: see text]. In this paper, we give a parameterized algorithm with running time [Formula: see text] for planar disjoint paths, the variant of the problem where the input graph is required to be planar. Our algorithm is based on the unique linkage/treewidth reduction theorem for planar graphs by Adler et al. [ J. Combin. Theory Ser. B, 122 (2017), pp. 815–843], the algebraic cohomology based technique of Schrijver [ SIAM J. Comput., 23 (1994), pp. 780–788], and one of the key combinatorial insights developed by Cygan et al. [ Proceedings of the 2013 IEEE 54 th Annual Symposium on Foundations of Computer Science, 2013, pp. 197–206] in their algorithm for disjoint paths on directed planar graphs. To the best of our knowledge, our algorithm is the first parameterized algorithm to exploit the fact that the treewidth of the input graph is small, and it does so in a way that is completely different from the use of dynamic programming.

An independent set in a graph \(G\) is a set of pairwise non-adjacent vertices. A graph \(G\) is bipartite if its vertex set can be partitioned into two independent sets. In the Odd Cycle Transversal problem, the input is a graph \(G\) along with a weight function w associating a rational weight with each vertex, and the task is to find a minimum weight vertex subset \(S\) in \(G\) such that \(G-S\) is bipartite; the weight of \(S\) , \(\text{w}(S)=\sum_{v\in S}\text{w}(v)\) . We show that Odd Cycle Transversal is polynomial-time solvable on graphs excluding \(P_{5}\) (a path on five vertices) as an induced subgraph. The problem was previously known to be polynomial-time solvable on \(P_{4}\) -free graphs and NP -hard on \(P_{6}\) -free graphs [Dabrowski, Feghali, Johnson, Paesani, Paulusma and Rzążewski, Algorithmica 2020]. Bonamy, Dabrowski, Feghali, Johnson and Paulusma [Algorithmica 2019] posed the existence of a polynomial-time algorithm on \(P_{5}\) -free graphs as an open problem. This was later re-stated by Rzążewski [Dagstuhl Reports, 9(6): 2019], by Chudnovsky, King, Pilipczuk, Rzążewski, and Spirkl [SIDMA 2021] who gave an algorithm with running time \(n^{O(\sqrt{n})}\) for the problem, and by Agrawal, Lima, Lokshtanov, Saurabh, and Sharma [SODA 2024] who gave a quasi-polynomial time algorithm.

In a disk graph, every vertex corresponds to a disk in $\mathbb{R}^2$ and two vertices are connected by an edge whenever the two corresponding disks intersect. Disk graphs form an important class of geometric intersection graphs, which generalizes both planar graphs and unit-disk graphs. We study a fundamental optimization problem in algorithmic graph theory, Bipartization (also known as Odd Cycle Transversal), on the class of disk graphs. The goal of Bipartization is to delete a minimum number of vertices from the input graph such that the resulting graph is bipartite. A folklore (polynomial-time) $3$-approximation algorithm for Bipartization on disk graphs follows from the classical framework of Goemans and Williamson [Combinatorica'98] for cycle-hitting problems. For over two decades, this result has remained the best known approximation for the problem (in fact, even for Bipartization on unit-disk graphs). In this paper, we achieve the first improvement upon this result, by giving a $(3-α)$-approximation algorithm for Bipartization on disk graphs, for some constant $α>0$. Our algorithm directly generalizes to the broader class of pseudo-disk graphs. Furthermore, our algorithm is robust in the sense that it does not require a geometric realization of the input graph to be given.

$\newcommand{\eps}{\varepsilon}$ We observe that a $(1-\eps)$-approximation algorithm to Independent Set, that works for any induced subgraph of the input graph, can be used, via a polynomial time reduction, to provide a $(1+\eps)$-approximation to Vertex Cover. This basic observation was made before, see [BHR11]. As a consequence, we get a PTAS for VC for unweighted pseudo-disks, QQPTAS for VC for unweighted axis-aligned rectangles in the plane, and QPTAS for MWVC for weighted polygons in the plane. To the best of our knowledge all these results are new.

We give two new approximation algorithms to compute the fractional hypertree width of an input hypergraph. The first algorithm takes as input n-vertex m-edge hypergraph$H$of fractional hypertree width at most$\omega$, runs in polynomial time and produces a tree decomposition of$H$of fractional hypertree width$\mathcal{O}(\omega\log n\log\omega)$, i.e., it is an$\mathcal{O}(\log n\log\omega)$-approximation algorithm. As an immediate corollary this yields poly-nomial time$\mathcal{O}(\log^{2}n\log\omega)$-approximation algorithms for (generalized) hypertree width as well. To the best of our knowledge our algorithm is the first non-trivial polynomial-time approximation algorithm for fractional hypertree width and (generalized) hypertree width, as opposed to algorithms that run in polynomial time only when$\omega$is considered a constant. For hypergraphs where every pair of hyperedges have at most$\eta$vertices in common, the al-gorithm outputs a hypertree decomposition with fractional hypertree width$\mathcal{O}(\eta\omega^{2}\log\omega)$and generalized hypertree width$\mathcal{O}(\eta\omega^{2}\log\omega(\log\eta+\text{log}\omega))$. This ratio is comparable with the recent algorithm of Lanzinger and Razgon [STACS 2024], which produces a hypertree decomposition with generalized hypertree width${\mathcal{O}}(\omega^{2}(\omega+\eta))$, but uses time (at least) exponential in$\eta$and$\omega$. The second algorithm runs in time$n^{\omega}m^{\mathcal{O}(1)}$and pro-duces a tree decomposition of$H$of fractional hypertree width$\mathcal{O}(\omega{\mathrm{l}}\text{og}^{2}\omega)$. This significantly improves over the$(n+m)^{\mathcal{O}(\omega^{3})}$time algorithm of Marx [ACM TALG 2010], which produces a tree decomposition of fractional hyper-tree width$\mathcal{O}(\omega^{3})$, both in terms of running time and the approximation ratio. Our main technical contribution, and the key insight behind both algorithms, is a variant of the classic Menger's Theorem for clique separators in graphs: For every graph$G$, vertex sets$A$and$B$, family$\mathcal{F}$of cliques in$G$, and positive rational$f$, either there exists a sub-family of$\mathcal{O}(f \cdot {\mathrm{l}}\text{og}^{2}n)$cliques in$\mathcal{F}$whose union separates$A$from$B$, or there exist$f\cdot\log\vert \mathcal{F}\vert$paths from$A$to$B$such that no clique in$\mathcal{F}$intersects more than$\log\vert \mathcal{F}\vert$paths.

Classical work on metric space based committee selection problem interprets distance as ``near is better''. In this work, motivated by real-life situations, we interpret distance as ``far is better''. Formally stated, we initiate the study of ``obnoxious'' committee scoring rules when the voters' preferences are expressed via a metric space. To this end, we propose a model where large distances imply high satisfaction and study the egalitarian avatar of the well-known Chamberlin-Courant voting rule and some of its generalizations. For a given integer value $1 \le λ\le k$, the committee size k, a voter derives satisfaction from only the $λ$-th favorite committee member; the goal is to maximize the satisfaction of the least satisfied voter. For the special case of $λ= 1$, this yields the egalitarian Chamberlin-Courant rule. In this paper, we consider general metric space and the special case of a $d$-dimensional Euclidean space. We show that when $λ$ is $1$ and $k$, the problem is polynomial-time solvable in $\mathbb{R}^2$ and general metric space, respectively. However, for $λ= k-1$, it is NP-hard even in $\mathbb{R}^2$. Thus, we have ``double-dichotomy'' in $\mathbb{R}^2$ with respect to the value of λ, where the extreme cases are solvable in polynomial time but an intermediate case is NP-hard. Furthermore, this phenomenon appears to be ``tight'' for $\mathbb{R}^2$ because the problem is NP-hard for general metric space, even for $λ=1$. Consequently, we are motivated to explore the problem in the realm of (parameterized) approximation algorithms and obtain positive results. Interestingly, we note that this generalization of Chamberlin-Courant rules encodes practical constraints that are relevant to solutions for certain facility locations.

In the MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula Φ, and a positive integer k, and the goal is to find an assignment β with at most k variables set to true (also called a weight k-assignment) such that the number of clauses satisfied by β is maximized. Maximum Coverage can be seen as a special case of CC-MaxSat, where the formula Φ is monotone, i.e., does not contain any negative literals. CC-MaxSat and Maximum Coverage are extremely well-studied problems in the approximation algorithms as well as the parameterized complexity literature. Our first conceptual contribution is that CC-MaxSat and Maximum Coverage are equivalent to each other in the context of FPT-Approximation parameterized by k (here, the approximation is in terms of the number of clauses satisfied/elements covered). In particular, we give a randomized reduction from CC-MaxSat to Maximum Coverage running in time 𝒪(1/ε)^{k} ⋅ (m+n)^{𝒪(1)} that preserves the approximation guarantee up to a factor of (1-ε). Furthermore, this reduction also works in the presence of "fairness" constraints on the satisfied clauses, as well as matroid constraints on the set of variables that are assigned true. Here, the "fairness" constraints are modeled by partitioning the clauses of the formula Φ into r different colors, and the goal is to find an assignment that satisfies at least t_j clauses of each color 1 ≤ j ≤ r. Armed with this reduction, we focus on designing FPT-Approximation schemes (FPT-ASes) for Maximum Coverage and its generalizations. Our algorithms are based on a novel combination of a variety of ideas, including a carefully designed probability distribution that exploits sparse coverage functions. These algorithms substantially generalize the results in Jain et al. [SODA 2023] for CC-MaxSat and Maximum Coverage for K_{d,d}-free set systems (i.e., no d sets share d elements), as well as a recent FPT-AS for Matroid Constrained Maximum Coverage by Sellier [ESA 2023] for frequency-d set systems.

We introduce a new framework for the analysis of preprocessing routines for parameterized counting problems. Existing frameworks that encapsulate parameterized counting problems permit the usage of exponential (rather than polynomial) time either explicitly or by implicitly reducing the counting problems to enumeration problems. Thus, our framework is the only one in the spirit of classic kernelization (as well as lossy kernelization). Specifically, we define a compression of a counting problem P into a counting problem Q as a pair of polynomial-time procedures: reduce and lift. Given an instance of P, reduce outputs an instance of Q whose size is bounded by a function f of the parameter, and given the number of solutions to the instance of Q, lift outputs the number of solutions to the instance of P. When P = Q, compression is termed kernelization, and when f is polynomial, compression is termed polynomial compression. Our technical (and other conceptual) contributions can be classified into two categories: Upper Bounds. We prove two theorems: (i) The #Vertex Cover problem parameterized by solution size admits a polynomial kernel; (ii) Every problem in the class of #Planar F-Deletion problems parameterized by solution size admits a polynomial compression. Lower Bounds. We introduce two new concepts of cross-compositions: EXACT-cross-composition and SUM-cross-composition. We prove that if a #P-hard counting problem P EXACT-cross-composes into a parameterized counting problem Q, then Q does not admit a polynomial compression unless the polynomial hierarchy collapses. We conjecture that the same statement holds for SUM-cross-compositions. Then, we prove that: (i) #Min (s,t)-Cut parameterized by treewidth does not admit a polynomial compression unless the polynomial hierarchy collapses; (ii) #Min (s,t)-Cut parameterized by minimum cut size, #Odd Cycle Transversal parameterized by solution size, and #Vertex Cover parameterized by solution size minus maximum matching size, do not admit polynomial compressions unless our conjecture is false.

An independent set in a graph G is a set of pairwise non-adjacent vertices. A graph G is bipartite if its vertex set can be partitioned into two independent sets. In the Odd Cycle Transversal problem, the input is a graph G along with a weight function w associating a rational weight with each vertex, and the task is to find a smallest weight vertex subset S in G such that G — S is bipartite; the weight of . We show that Odd Cycle Transversal admits an algorithm with running time on graphs excluding P5 (a path on five vertices) as an induced subgraph. The problem was previously known to be polynomial time solvable on P4-free graphs and NP-hard on P6-free graphs [Dabrowski, Feghali, Johnson, Paesani, Paulusma and Rzążewski, Algorithmica 2020]. Bonamy, Dabrowski, Feghali, Johnson and Paulusma [Algorithmica 2019] posed the existence of a polynomial time algorithm on P5-free graphs as an open problem, this was later re-stated by Rzążewski [Dagstuhl Reports, 9(6): 2019] and by Chudnovsky, King, Pilipczuk, Rzążewski, and Spirkl [SIDMA 2021], who gave an algorithm with running time . While our time algorithm falls short of completely resolving the complexity status of Odd Cycle Transversal on P5-free graphs it shows that the problem is not NP-hard unless every problem in NP is solvable in quasi-polynomial time.

In the Euclidean Bottleneck Steiner Tree problem, the input consists of a set of n points in ℝ2 called terminals and a parameter k, and the goal is to compute a Steiner tree that spans all the terminals and contains at most k points of ℝ2 as Steiner points such that the maximum edge-length of the Steiner tree is minimized, where the length of a tree edge is the Euclidean distance between its two endpoints. The problem is well-studied and is known to be NP-hard. In this paper, we give a kO(k)nO(1)-time algorithm for Euclidean Bottleneck Steiner Tree, which implies that the problem is fixed-parameter tractable (FPT). This settles an open question explicitly asked by Bae et al. [Algorithmica, 2011], who showed that the ℓ1 and ℓ∞ variants of the problem are FPT. Our approach can be generalized to the problem with ℓp metric for any rational 1 ≤ ρ ≤ ∞, or even other metrics on ℝ2.

A graph G contains a graph H as an induced minor if H can be obtained from G by vertex deletions and edge contractions. The class of H-induced-minor-free graphs generalizes the class of H-minor-free graphs, but unlike H-minor-free graphs, it can contain dense graphs. We show that if an n-vertex m-edge graph G does not contain a graph H as an induced minor, then it has a balanced vertex separator of size , where the OH(·)-notation hides factors depending on H. More precisely, our upper bound for the size of the balanced separator is . We give an algorithm for finding either an induced minor model of H in G or such a separator in randomized polynomial-time. We apply this to obtain subexponential time algorithms on H-induced-minor-free graphs for a large class of problems including maximum independent set, minimum feedback vertex set, 3-coloring, and planarization.

The streaming model was introduced to parameterized complexity independently by Fafianie and Kratsch [MFCS14] and by Chitnis, Cormode, Hajiaghayi and Monemizadeh [SODA15]. Subsequently, it was broadened by Chitnis, Cormode, Esfandiari, Hajiaghayi and Monemizadeh [SPAA15] and by Chitnis, Cormode, Esfandiari, Hajiaghayi, McGregor, Monemizadeh and Vorotnikova [SODA16]. Despite its strong motivation, the applicability of the streaming model to central problems in parameterized complexity has remained, for almost a decade, quite limited. Indeed, due to simple Ω(n)-space lower bounds for many of these problems, the kO(1) · polylog(n)-space requirement in the model is too strict.

We show that the Maximum Weight Independent Set problem (MWIS) can be solved in quasi-polynomial time on H-free graphs (graphs excluding a fixed graph H as an induced subgraph) for every H whose every connected component is a path or a subdivided claw (i.e., a tree with at most three leaves). This completes the dichotomy of the complexity of MWIS in F-free graphs for any finite set F of graphs into NP-hard cases and cases solvable in quasi-polynomial time, and corroborates the conjecture that the cases not known to be NP-hard are actually polynomial-time solvable. The key graph-theoretic ingredient in our result is as follows. Fix an integer t ≥ 1. Let St,t,t be the graph created from three paths on t edges by identifying one endpoint of each path into a single vertex. We show that, given a graph G, one can in polynomial time find either an induced St,t,t in G, or a balanced separator consisting of O(log|V(G)|) vertex neighborhoods in G, or an extended strip decomposition of G (a decomposition almost as useful for recursion for MWIS as a partition into connected components) with each particle of weight multiplicatively smaller than the weight of G. This is a strengthening of a result of Majewski, Masařík, Novotná, Okrasa, Pilipczuk, Rzążewski, and Sokołowski [Transactions on Computation Theory ‍2024] which provided such an extended strip decomposition only after the deletion of O(log|V(G)|) vertex neighborhoods. To reach the final result, we employ an involved branching strategy that relies on the structural lemma presented above.

Abstract We provide a polynomial-time algorithm for b-Coloring on graphs of constant clique-width. This unifies and extends nearly all previously known polynomial time results on graph classes, and answers open questions posed by Campos and Silva (Algorithmica 80(1), 104–115, 2018) and Bonomo et al. (Graphs and Combinatorics 25(2), 153–167, 2009). This constitutes the first result concerning structural parameterizations of this problem. We show that the problem is $$\textsf{FPT}$$ FPT when parameterized by the vertex cover number on general graphs, and on chordal graphs when parameterized by the number of colors. Additionally, we observe that our algorithm for graphs of bounded clique-width can be adapted to solve the Fall Coloring problem within the same runtime bound. The running times of the clique-width based algorithms for $$b$$ b -Coloring and Fall Coloring are tight under the Exponential Time Hypothesis.

In the Min $k$-Cut problem, the input consists of an edge weighted graph $G$ and an integer $k$, and the task is to partition the vertex set into $k$ nonempty sets, such that the total weight of the edges with endpoints in different parts is minimized. When $k$ is part of the input, the problem is NP-complete and hard to approximate within any factor less than 2. Recently, the problem has received significant attention from the perspective of parameterized approximation. Gupta, Lee, and Li [ Proceedings of the 29 th Annual ACM-SIAM Symposium on Discrete Algorithms, A. Czumaj, ed., SIAM, Philadelphia, 2018, pp. 2821–2837] initiated the study of FPT-approximation for the Min $k$-Cut problem and gave a 1.9997-approximation algorithm running in time $2^{\mathcal{O}(k^6)}n^{\mathcal{O}(1)}$. Later, the same set of authors [ Proceedings of the 59 th IEEE Annual Symposium on Foundations of Computer Science, M. Thorup, ed., 2018, pp. 113–123] designed a $(1 +\epsilon)$-approximation algorithm that runs in time $(k/\epsilon)^{\mathcal{O}(k)}n^{k+\mathcal{O}(1)}$ and a 1.81-approximation algorithm running in time $2^{\mathcal{O}(k^2)}n^{\mathcal{O}(1)}$. More, recently, Kawarabayashi and Lin [ Proceedings of the 31 st ACM-SIAM Symposium on Discrete Algorithms, S. Chawla, ed., SIAM, Philadelphia, 2020, pp. 990–999] gave a $(5/3 + \epsilon)$-approximation for Min $k$-Cut running in time $2^{\mathcal{O}(k^2 \log k)}n^{\mathcal{O}(1)}$. In this paper, we give a parameterized approximation algorithm with best possible approximation guarantee and best possible running time dependence on said guarantee (up to the exponential time hypothesis and constants in the exponent). In particular, for every $\epsilon > 0$, the algorithm obtains a $(1 +\epsilon)$-approximate solution in time $(k/\epsilon)^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$. The main ingredients of our algorithm are a simple sparsification procedure, a new polynomial time algorithm for decomposing a graph into highly connected parts, and a new exact algorithm with running time $s^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$ on unweighted (multi-) graphs. Here, $s$ denotes the number of edges in a minimum $k$-cut. The latter two are of independent interest.

We prove a structural theorem for unit-disk graphs, which (roughly) states that given a set \(\mathcal{D}\) of \(n\) unit disks inducing a unit-disk graph \(G_{\mathcal{D}}\) and a number \(p\in[n]\) , one can partition \(\mathcal{D}\) into \(p\) subsets \(\mathcal{D}_{1},\dots,\mathcal{D}_{p}\) such that for every \(i\in[p]\) and every \(\mathcal{D}^{\prime}\subseteq\mathcal{D}_{i}\) , the graph obtained from \(G_{\mathcal{D}}\) by contracting all edges between the vertices in \(\mathcal{D}_{i}\backslash\mathcal{D}^{\prime}\) admits a tree decomposition in which each bag consists of \(O(p+|\mathcal{D}^{\prime}|)\) cliques. Our theorem can be viewed as an analog for unit-disk graphs of the structural theorems for planar graphs and almost-embeddable graphs proved recently by Marx et al. [SODA ’22] and Bandyapadhyay et al. [SODA ’22]. By applying our structural theorem, we give several new combinatorial and algorithmic results for unit-disk graphs. On the combinatorial side, we obtain the first Contraction Decomposition Theorem for unit-disk graphs, resolving an open question in the work by Panolan et al. [SODA ’19]. On the algorithmic side, we obtain a new algorithm for bipartization (also known as odd cycle transversal) on unit-disk graphs, which runs in \(2^{O(\sqrt{k}\log k)}\cdot n^{O(1)}\) time, where \(k\) denotes the solution size. Our algorithm significantly improves the previous slightly subexponential-time algorithm given by Lokshtanov et al. [SODA ’22] which runs in \(2^{O(k^{27/28})}\cdot n^{O(1)}\) time. We also show that the problem cannot be solved in \(2^{o(\sqrt{k})}\cdot n^{O(1)}\) time assuming the Exponential Time Hypothesis, which implies that our algorithm is almost optimal.

We introduce the following submodular generalization of the Shortest Cycle problem. For a nonnegative monotone submodular cost function f defined on the edges (or the vertices) of an undirected graph G , we seek for a cycle C in G of minimum cost 𝖮𝖯𝖳 = f(C) . We give an algorithm that given an n -vertex graph G , parameter ɛ > 0, and the function f represented by an oracle, in time n 𝒪 (log 1/ɛ) finds a cycle C in G with f(C) ≤ (1+ɛ). 𝖮𝖯𝖳. This is in sharp contrast with the non-approximability of the closely related Monotone Submodular Shortest ( s,t -Path problem, which requires exponentially many queries to the oracle for finding an n 2/3-ɛ -approximation Goel et al. [ 7 ], FOCS 2009. We complement our algorithm with a matching lower bound. We show that for every ɛ > 0, obtaining a (1+ɛ)-approximation requires at least n Ω (log 1/ ɛ) queries to the oracle. When the function f is integer-valued, our algorithm yields that a cycle of cost 𝖮𝖯𝖳 can be found in time n 𝒪(log 𝖮𝖯𝖳) . In particular, for 𝖮𝖯𝖳 = n 𝒪(1) this gives a quasipolynomial-time algorithm computing a cycle of minimum submodular cost. Interestingly, while a quasipolynomial-time algorithm often serves as a good indication that a polynomial time complexity could be achieved, we show a lower bound that n 𝒪(log n ) queries are required even when 𝖮𝖯𝖳= 𝒪( n ). We also consider special cases of monotone submodular functions, corresponding to the number of different color classes needed to cover a cycle in an edge-colored multigraph G . For special cases of the corresponding minimization problem, we obtain fixed-parameter tractable algorithms and polynomial-time algorithms, when restricted to certain classes of inputs.

In the Minimum Bisection problem input is a graph G and the goal is to partition the vertex set into two parts A and B, such that ||A|-|B|| ≤ 1 and the number k of edges between A and B is minimized. The problem is known to be NP-hard, and assuming the Unique Games Conjecture even NP-hard to approximate within a constant factor [Khot and Vishnoi, J.ACM'15]. On the other hand, a 𝒪(log n)-approximation algorithm [Räcke, STOC'08] and a parameterized algorithm [Cygan et al., ACM Transactions on Algorithms'20] running in time k^𝒪(k) n^𝒪(1) is known. The Minimum Bisection problem can be viewed as a clustering problem where edges represent similarity and the task is to partition the vertices into two equally sized clusters while minimizing the number of pairs of similar objects that end up in different clusters. Motivated by a number of egregious examples of unfair bias in AI systems, many fundamental clustering problems have been revisited and re-formulated to incorporate fairness constraints. In this paper we initiate the study of the Minimum Bisection problem with fairness constraints. Here the input is a graph G, positive integers c and k, a function χ:V(G) → {1, …, c} that assigns a color χ(v) to each vertex v in G, and c integers r_1,r_2,⋯,r_c. The goal is to partition the vertex set of G into two almost-equal sized parts A and B with at most k edges between them, such that for each color i ∈ {1, …, c}, A has exactly r_i vertices of color i. Each color class corresponds to a group which we require the partition (A, B) to treat fairly, and the constraints that A has exactly r_i vertices of color i can be used to encode that no group is over- or under-represented in either of the two clusters. We first show that introducing fairness constraints appears to make the Minimum Bisection problem qualitatively harder. Specifically we show that unless FPT=W[1] the problem admits no f(c)n^𝒪(1) time algorithm even when k = 0. On the other hand, our main technical contribution shows that is that this hardness result is simply a consequence of the very strict requirement that each color class i has exactly r_i vertices in A. In particular we give an f(k,c,ε)n^𝒪(1) time algorithm that finds a balanced partition (A, B) with at most k edges between them, such that for each color i ∈ [c], there are at most (1±ε)r_i vertices of color i in A. Our approximation algorithm is best viewed as a proof of concept that the technique introduced by [Lampis, ICALP'18] for obtaining FPT-approximation algorithms for problems of bounded tree-width or clique-width can be efficiently exploited even on graphs of unbounded width. The key insight is that the technique of Lampis is applicable on tree decompositions with unbreakable bags (as introduced in [Cygan et al., SIAM Journal on Computing'14]). An important ingredient of our approximation scheme is a combinatorial result that may be of independent interest, namely that for every k, every graph G admits a tree decomposition with adhesions of size at most 𝒪(k), unbreakable bags, and logarithmic depth.

We re-visit the complexity of polynomial time pre-processing (kernelization) for the d-Hitting Set problem. This is one of the most classic problems in Parameterized Complexity by itself, and, furthermore, it encompasses several other of the most well-studied problems in this field, such as Vertex Cover, Feedback Vertex Set in Tournaments (FVST) and Cluster Vertex Deletion (CVD). In fact, d-Hitting Set encompasses any deletion problem to a hereditary property that can be characterized by a finite set of forbidden induced subgraphs. With respect to bit size, the kernelization complexity of d-Hitting Set is essentially settled: there exists a kernel with 𝒪(k^d) bits (𝒪(k^d) sets and 𝒪(k^{d-1}) elements) and this it tight by the result of Dell and van Melkebeek [STOC 2010, JACM 2014]. Still, the question of whether there exists a kernel for d-Hitting Set with fewer elements has remained one of the most major open problems in Kernelization. In this paper, we first show that if we allow the kernelization to be lossy with a qualitatively better loss than the best possible approximation ratio of polynomial time approximation algorithms, then one can obtain kernels where the number of elements is linear for every fixed d. Further, based on this, we present our main result: we show that there exist approximate Turing kernelizations for d-Hitting Set that even beat the established bit-size lower bounds for exact kernelizations - in fact, we use a constant number of oracle calls, each with "near linear" (𝒪(k^{1+ε})) bit size, that is, almost the best one could hope for. Lastly, for two special cases of implicit 3-Hitting set, namely, FVST and CVD, we obtain the "best of both worlds" type of results - (1+ε)-approximate kernelizations with a linear number of vertices. In terms of size, this substantially improves the exact kernels of Fomin et al. [SODA 2018, TALG 2019], with simpler arguments.

We present the first polynomial-time algorithm to exactly compute the number of labeled chordal graphs on $n$ vertices. Our algorithm solves a more general problem: given $n$ and $ω$ as input, it computes the number of $ω$-colorable labeled chordal graphs on $n$ vertices, using $O(n^7)$ arithmetic operations. A standard sampling-to-counting reduction then yields a polynomial-time exact sampler that generates an $ω$-colorable labeled chordal graph on $n$ vertices uniformly at random. Our counting algorithm improves upon the previous best result by Wormald (1985), which computes the number of labeled chordal graphs on $n$ vertices in time exponential in $n$. An implementation of the polynomial-time counting algorithm gives the number of labeled chordal graphs on up to $30$ vertices in less than three minutes on a standard desktop computer. Previously, the number of labeled chordal graphs was only known for graphs on up to $15$ vertices. In addition, we design two approximation algorithms: (1) an approximate counting algorithm that computes a $(1\pm\varepsilon)$-approximation of the number of $n$-vertex labeled chordal graphs, and (2) an approximate sampling algorithm that generates a random labeled chordal graph according to a distribution whose total variation distance from the uniform distribution is at most $\varepsilon$. The approximate counting algorithm runs in $O(n^3\log{n}\log^7(1/\varepsilon))$ time, and the approximate sampling algorithm runs in $O(n^3\log{n}\log^7(1/\varepsilon))$ expected time.

Given an edge-weighted graph, how many minimum $k$-cuts can it have? This is a fundamental question in the intersection of algorithms, extremal combinatorics, and graph theory. It is particularly interesting in that the best known bounds are algorithmic: they stem from algorithms that compute the minimum $k$-cut. In 1994, Karger and Stein obtained a randomized contraction algorithm that finds a minimum $k$-cut in $O(n^{(2-o(1))k})$ time. It can also enumerate all such $k$-cuts in the same running time, establishing a corresponding extremal bound of $O(n^{(2-o(1))k})$. Since then, the algorithmic side of the minimum $k$-cut problem has seen much progress, leading to a deterministic algorithm based on a tree packing result of Thorup, which enumerates all minimum $k$-cuts in the same asymptotic running time, and gives an alternate proof of the $O(n^{(2-o(1))k})$ bound. However, beating the Karger--Stein bound, even for computing a single minimum $k$-cut, has remained out of reach. In this paper, we give an algorithm to enumerate all minimum $k$-cuts in $O(n^{(1.981+o(1))k})$ time, breaking the algorithmic and extremal barriers for enumerating minimum $k$-cuts. To obtain our result, we combine ideas from both the Karger--Stein and Thorup results, and draw a novel connection between minimum $k$-cut and extremal set theory. In particular, we give and use tighter bounds on the size of set systems with bounded dual VC-dimension, which may be of independent interest.

We consider a number of min-max coverage problems. In each problem, the input is an unweighted graph G and an integer k, and possibly some additional information, such as a root vertex r. In the Min-Max Path Cover problem, the task is to cover all vertices of the graph by k walks, minimizing the length of the longest walk. The variant of Min-Max Path Cover in which all walks start and end at the same prescribed root vertex r is called the k-Traveling Salesmen Problem. In the Min-Max Tree Cover problem, the task is to cover all vertices of the graph by k trees, minimizing the size (number of edges) of the largest tree. In the rooted version, Min-Max k-Rooted Tree Cover, the input also contains k roots r1, . . ., rk, and the ith tree must contain the root ri. These four problems are all known to be APX-hard and to admit a constant-factor approximation. In this paper, we initiate the systematic study of these problems on trees and, more generally, on graphs of constant treewidth. As opposed to most graph problems, all four of the above coverage problems remain NP-hard even when G is a tree. We obtain an nO(k)-time exact algorithm for all four problems on graphs of bounded treewidth. Our main contribution is a quasi-polynomial-time approximation scheme (QPTAS) for the k-Traveling Salesmen Problem, Min-Max Path Cover, and Min-Max Tree Cover on graphs of bounded treewidth.

We introduce the following submodular generalization of the SHORTEST CYCLE problem. For a nonnegative monotone submodular cost function f defined on the edges (or the vertices) of an undirected graph G, we seek for a cycle C in G of minimum cost OPT = f(C). We give an algorithm that given an n-vertex graph G, parameter ε > 0, and the function f represented by an oracle, in time n

A vertex set S in a graph G is a minimal separator if there exist vertices u and v that are in distinct connected components of G — S, but in the same connected component of G — S' for every S' ⊂ S. A class F of graphs is called tame if there exists a constant c so that every graph in F on n vertices contains at most O(nc) minimal separators. If there exists a constant c so that every graph in F on n vertices contains at most O(nclog n) minimal separators the class is strongly-quasi-tame. If there exists a constant c > 1 so that F contains n-vertex graphs with at least cn minimal separators for arbitrarily large n then F is called feral. The classification of graph classes into tame or feral has numerous algorithmic consequences, and has recently received considerable attention.

A class F of graphs is called tame if every graph in F on n vertices contains at most nO(1) minimal separators, quasi-tame if every graph in F on n vertices contains at most 2logO(1)(n) minimal separators, and feral if there exists a constant c > 1 so that F contains n-vertex graphs with at least cn minimal separators for arbitrarily large n. The classification of graph classes into (quasi-) tame or feral has numerous algorithmic consequences, and has recently received considerable attention.

We initiate a systematic study of approximation schemes for fundamental optimization problems on disk graphs, a common generalization of both planar graphs and unit-disk graphs. Our main contribution is a general framework for designing efficient polynomial-time approximation schemes (EPTASes) for vertex- deletion problems on disk graphs, which results in EPTASes for many fundamental problems including VERTEX COVER, FEEDBACK VERTEX SET, SMALL CYCLE HITTING (in particular, TRIANGLE HITTING), Pk-VERTEX DELETION for k ∈ {3,4,5}, PATH DELETION, PATHWIDTH 1-DELETION, COMPONENT ORDER CONNECTIVITY, BOUNDED DEGREE DELETION, PSEUDOFOREST DELETION, FINITE-TYPE COMPONENT DELETION, etc. All EPTASes obtained using our framework are robust in the sense that they do not require a realization of the input disk graph (in fact, we allow the input to be any graph, and our algorithms either output a correct approximation solution for the problem or conclude that the input graph is not a disk graph). To the best of our knowledge, prior to this work, the only problems known to admit PTASes or EPTASes on disk graphs are MAXIMUM CLIQUE, INDEPENDENT SET, DOMINATING SET, and VERTEX COVER, among which the existing PTAS [Erlebach et al., SICOMP'05] and EPTAS [Leeuwen, SWAT'06] for VERTEX COVER require a realization of the input disk graph (while ours does not). The core of our framework is a reduction for a broad class of (approximation) vertex-deletion problems from (general) disk graphs to disk graphs of bounded local radius, which is a new invariant of disk graphs introduced in this work. Disk graphs of bounded local radius can be viewed as a “mild” generalization of planar graphs, which preserves certain nice properties of planar graphs. Specifically, we prove that disk graphs of bounded local radius admit the Excluded Grid Minor property and have locally bounded treewidth. This allows existing techniques for designing approximation schemes on planar graphs (e.g., bidimensionality and Baker's technique) to be directly applied to disk graphs of bounded local radius. * The full version of the paper can be accessed at https://arxiv.org/abs/2211.02717

We give an algorithm that takes as input an n-vertex graph G and an integer k, runs in time 2O(k2) nO(1), and outputs a tree decomposition of G of width at most k, if such a decomposition exists. This resolves the long-standing open problem of whether there is a 2o(k3) nO(1) time algorithm for treewidth. In particular, our algorithm is the first improvement on the dependency on k in algorithms for treewidth since the 2O(k3) nO(1) time algorithm given by Bodlaender and Kloks [ICALP 1991] and Lagergren and Arnborg [ICALP 1991].

Given a graph G and an integer k , the Interval Vertex Deletion (IVD) problem asks whether there exists a subset S ⊆ V ( G ) of size at most k such that G-S is an interval graph. This problem is known to be NP -complete (according to Yannakakis at STOC 1978). Originally in 2012, Cao and Marx showed that IVD is fixed parameter tractable: they exhibited an algorithm with running time 10 k n O (1). The existence of a polynomial kernel for IVD remained a well-known open problem in parameterized complexity. In this article, we settle this problem in the affirmative.

Suppose we are given a pair of points $s, t$ and a set $S$ of $n$ geometric objects in the plane, called obstacles. We show that in polynomial time one can construct an auxiliary (multi-)graph $G$ with vertex set $S$ and every edge labeled from $\{0, 1\}$, such that a set $S_d \subseteq S$ of obstacles separates $s$ from $t$ if and only if $G[S_d]$ contains a cycle whose sum of labels is odd. Using this structural characterization of separating sets of obstacles we obtain the following algorithmic results. In the Obstacle-Removal problem the task is to find a curve in the plane connecting s to t intersecting at most q obstacles. We give a $2.3146^qn^{O(1)}$ algorithm for Obstacle-Removal, significantly improving upon the previously best known $q^{O(q^3)} n^{O(1)}$ algorithm of Eiben and Lokshtanov (SoCG'20). We also obtain an alternative proof of a constant factor approximation algorithm for Obstacle-Removal, substantially simplifying the arguments of Kumar et al. (SODA'21). In the Generalized Points-Separation problem, the input consists of the set S of obstacles, a point set A of k points and p pairs $(s_1, t_1),... (s_p, t_p)$ of points from A. The task is to find a minimum subset $S_r \subseteq S$ such that for every $i$, every curve from $s_i$ to $t_i$ intersects at least one obstacle in $S_r$. We obtain $2^{O(p)} n^{O(k)}$-time algorithm for Generalized Points-Separation problem. This resolves an open problem of Cabello and Giannopoulos (SoCG'13), who asked about the existence of such an algorithm for the special case where $(s_1, t_1), ... (s_p, t_p)$ contains all the pairs of points in A. Finally, we improve the running time of our algorithm to $f(p,k) n^{O(\sqrt{k})}$ when the obstacles are unit disks, where $f(p,k) = 2^O(p) k^{O(k)}$, and show that, assuming the Exponential Time Hypothesis (ETH), the running time dependence on $k$ of our algorithms is essentially optimal.

Vertex-deletion problems have been at the heart of parameterized complexity throughout its history. Here, the aim is to determine the minimum size (denoted by modℋ) of a modulator to a graph class ℋ, i.e., a set of vertices whose deletion results in a graph in ℋ. Recent years have seen the development of a research programme where the complexity of modulators is measured in ways other than size. For instance, for a graph class ℋ, the graph parameters elimination distance to ℋ (denoted by edℋ) [Bulian and Dawar, Algorithmica, 2016] and ℋ-treewidth (denoted by twℋ) [Eiben et al. JCSS, 2021] aim to minimize the treedepth and treewidth, respectively, of the “torso” of the graph induced on a modulator to the graph class ℋ. Here, the torso of a vertex set S in a graph G is the graph with vertex set S and an edge between two vertices u, v ∊ S if there is a path between u and v in G whose internal vertices all lie outside S. In this paper, we show that from the perspective of (non-uniform) fixed-parameter tractability (FPT), the three parameters described above give equally powerful parameterizations for every hereditary graph class ℋ that satisfies mild additional conditions. In fact, we show that for every hereditary graph class ℋ satisfying mild additional conditions, with the exception of edℋ parameterized by twℋ, for every pair of these parameters, computing one parameterized by itself or any of the others is FPT-equivalent to the standard vertex-deletion (to ℋ) problem. As an example, we prove that an FPT algorithm for the vertex-deletion problem implies a non-uniform FPT algorithm for computing edℋ and twℋ. The conclusions of non-uniform FPT algorithms being somewhat unsatisfactory, we essentially prove that if ℋ is hereditary, union-closed, CMSO-definable, and (a) the canonical equivalence relation (or any refinement thereof) for membership in the class can be efficiently computed, or (b) the class admits a “strong irrelevant vertex rule”, then there exists a uniform FPT algorithm for edℋ. Using these sufficient conditions, we obtain uniform FPT algorithms for computing edℋ, when ℋ is defined by excluding a finite number of connected (a) minors, or (b) topological minors, or (c) induced subgraphs, or when ℋ is any of bipartite, chordal or interval graphs. For most of these problems, the existence of a uniform FPT algorithm has remained open in the literature. In fact, for some of them, even a non-uniform FPT algorithm was not known. For example, Jansen et al. [STOC 2021] ask for such an algorithm when ℋ is defined by excluding a finite number of connected topological minors. We resolve their question in the affirmative.

We design the first subexponential-time (parameterized) algorithms for several cut and cycle-hitting problems on H-minor free graphs. In particular, we obtain the following results (where k is the solution-size parameter). time algorithms for Edge Bipartization and Odd Cycle Transversal; a time algorithm for Edge Multiway Cut and a time algorithm for Vertex Multiway Cut (with undeletable terminals), where r is the number of terminals to be separated; a time algorithm for Edge Multicut and a time algorithm for Vertex Multicut (with undeletable terminals), where r is the number of terminal pairs to be separated; a time algorithm for Group Feedback Edge Set and a time algorithm for Group Feedback Vertex Set, where g is the size of the group. In addition, our approach also gives time algorithms for all above problems with the exception of time for Edge/Vertex Multicut and time for Group Feedback Edge/Vertex Set. All of our FPT algorithms (the first four items above) are randomized, as they use known randomized kernelization algorithms as sub-routines. We obtain our results by giving a new decomposition theorem on graphs of bounded genus, or more generally, an h-almost-embeddable graph for an arbitrary but fixed constant h. Our new decomposition theorem generalizes known Contraction Decomposition Theorem. Prior studies on this topic exhibited that the classes of planar graphs [Klein, SICOMP, 2008], graphs of bounded genus [Demaine, Hajiaghayi and Mohar, Combinatorica 2010] and H-minor free graphs [Demaine, Hajiaghayi and Kawarabayashi, STOC 2011] admit a Contraction Decomposition Theorem. In particular we show the following. Let G be a graph of bounded genus, or more generally, an h-almost-embeddable graph for an arbitrary but fixed constant h. Then for every p ∊ ℕ, there exist disjoint sets Z1, …, Zp ⊆ V(G) such that for every i ∊ {1, …, p} and every Z′ ⊆ Zi, the treewidth of G/(Zi\Z′) is upper bounded by O(p + |Z′|), where the constant hidden in O(·) depends on h. Here G/(Zi\Z′) denotes the graph obtained from G by contracting every edge with both endpoints in Zi\Z′. When Z′ = , this corresponds to classical Contraction Decomposition Theorem.

One of the most celebrated results in Parameterized Complexity is the Bidimensionality theory of Demaine et al. [J. ACM, 2005], which has yielded, over the past two decades, numerous subexponential-time fixed-parameter tractable (FPT) algorithms for various problems on planar (and H-minor-free) graphs. At the heart of this theory is the proof of sublinear bounds in terms of solution size on the treewidth of a given graph. Inspired by this theory, in recent years, significant efforts have been devoted to design subexponential-time FPT algorithms for problems on geometric graph classes that utilize new treewidth bounds, in particular (but not only) for unit disk graphs [Fomin et al., SODA'12; Fomin et al., DCG'19; Panolan et al., SODA'19; Fomin et al. SoCG'20]. In this paper, we aim to attain such results on disk graphs, a broad class of graphs that generalizes both the classes of planar graphs and unit disk graphs, and thereby unify the aforementioned research frontiers for planar and unit disk graphs. Our main contribution is an approach to design subexponential-time FPT algorithms for problems on disk graphs, which we apply to several well-studied graph problems. At the heart of our approach lie two new combinatorial theorems concerning the treewidth of disk graphs having a realization of bounded ply (or maximum clique size) that are of independent interest. In particular, we prove a stronger version of the following treewidth bound: Let G be a disk graph that has some realization of ply p and no false twins, and M ⊆ V(G) such that G has no triangle with exactly one vertex from M, and G–M has treewidth w. Then, the treewidth of G is . Among our applications are the first subexponential-time FPT algorithms for several problems on disk graphs, including Triangle Hitting, Feedback Vertex Set and Odd Cycle Transversal (OCT). Previously, subexponential-time FPT algorithms for these problems were only known on planar graphs and unit disk graphs (excluding OCT, which was only known to admit such an algorithm on planar graphs). Our algorithms are robust, in particular, they do not require a geometric realization of the input graph (for all aforementioned problems), and they generalize to the weighted and counting versions of all aforementioned problems except for OCT.

We prove that Graph Isomorphism and Canonization in graphs excluding a fixed graph H as a minor can be solved by an algorithm working in time f(H)· nO(1), where f is some function. In other words, we show that these problems are fixed-parameter tractable when parameterized by the size of the excluded minor, with the caveat that the bound on the running time is not necessarily computable. The underlying approach is based on decomposing the graph in a canonical way into unbreakable (intuitively, well-connected) parts, which essentially provides a reduction to the case where the given H-minor-free graph is unbreakable itself. This is complemented by an analysis of unbreakable H-minor-free graphs, which reveals that every such graph can be canonically decomposed into a part that admits few automorphisms and a part that has bounded treewidth.

MOTIVATION: Bait enrichment is a protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes ('baits') are designed, manufactured and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. Metsky et al. demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. RESULTS: We formalize the problem of designing baits by defining the Minimum Bait Cover problem, show that the problem is NP-hard even under very restrictive assumptions, and design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as Syotti. The running time of Syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that Syotti requires only 25 min to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 17% of the data in 72 h. AVAILABILITY AND IMPLEMENTATION: https://github.com/jnalanko/syotti. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract. We prove the following theorem. Given a planar graph [Formula: see text] and an integer [Formula: see text], it is possible in polynomial time to randomly sample a subset [Formula: see text] of vertices of [Formula: see text] with the following properties: [Formula: see text] induces a subgraph of [Formula: see text] of treewidth [Formula: see text], and for every connected subgraph [Formula: see text] of [Formula: see text] on at most [Formula: see text] vertices, the probability that [Formula: see text] covers the whole vertex set of [Formula: see text] is at least [Formula: see text], where [Formula: see text] is the number of vertices of [Formula: see text]. Together with standard dynamic programming techniques for graphs of bounded treewidth, this result gives a versatile technique for obtaining (randomized) subexponential-time parameterized algorithms for problems on planar graphs, usually with running time bound [Formula: see text]. The technique can be applied to problems expressible as searching for a small, connected pattern with a prescribed property in a large host graph; examples of such problems include Directed [Formula: see text]-Path, Weighted [Formula: see text]-Path, Vertex Cover Local Search, and Subgraph Isomorphism, among others. Up to this point, it was open whether these problems could be solved in subexponential parameterized time on planar graphs, because they are not amenable to the classic technique of bidimensionality. Furthermore, all our results hold in fact on any class of graphs that exclude a fixed apex graph as a minor, in particular on graphs embeddable in any fixed surface.

In the Multi-Team Formation problem, we are given a ground set C of n candidates, each of which is characterized by a d-dimensional attribute vector in ℝ^d, and two positive integers α and β satisfying α β ≤ n. The goal is to form α disjoint teams T₁,...,T_α ⊆ C, each of which consists of β candidates in C, such that the total score of the teams is maximized, where the score of a team T is the sum of the h_j maximum values of the j-th attributes of the candidates in T, for all j ∈ {1,...,d}. Our main result is an 2^{2^O(d)} n^O(1)-time algorithm for Multi-Team Formation. This bound is ETH-tight since a 2^{2^{d/c}} n^O(1)-time algorithm for any constant c > 12 can be shown to violate the Exponential Time Hypothesis (ETH). Our algorithm runs in polynomial time for all dimensions up to d = clog log n for a sufficiently small constant c > 0. Prior to our work, the existence of a polynomial time algorithm was an open problem even for d = 3.

In the Dominating Set problem the input is a graph G and an integer k, the task is to determine whether there exists a vertex set S of size at most k so that every vertex not in S has at least one neighbor in S. We consider the parameterized complexity of the Dominating Set problem, parameterized by the solution size k, and the weak closure of the input graph G. Weak closure of graphs was recently introduced by Fox et al. [SIAM J. Comp. 2020 ] and captures sparseness and triadic closure properties found in real world graphs. A graph G is weakly c-closed if for every induced subgraph G' of G, there exists a vertex v ∈ V(G') such that every vertex u in V(G') which is non-adjacent to v has less than c common neighbors with v. The weak closure of G is the smallest integer γ such that G is weakly γ-closed. We give an algorithm for Dominating Set with running time k^O(γ² k³) n^O(1), resolving an open problem of Koana et al. [ISAAC 2020]. One of the ingredients of our algorithm is a proof that the VC-dimension of (the set system defined by the closed neighborhoods of the vertices of) a weakly γ-closed graph is upper bounded by 6γ. This result may find further applications in the study of weakly closed graphs.

In its most traditional setting, the main concern of optimization theory is the search for optimal solutions for instances of a given computational problem. A recent trend of research in artificial intelligence, called solution diversity, has focused on the development of notions of optimality that may be more appropriate in settings where subjectivity is essential. The idea is that instead of aiming at the development of algorithms that output a single optimal solution, the goal is to investigate algorithms that output a small set of sufficiently good solutions that are sufficiently diverse from one another. In this way, the user has the opportunity to choose the solution that is most appropriate to the context at hand. It also displays the richness of the solution space. When combined with techniques from parameterized complexity theory, the paradigm of diversity of solutions offers a powerful algorithmic framework to address problems of practical relevance. In this work, we investigate the impact of this combination in the field of Kemeny Rank Aggregation, a well-studied class of problems lying in the intersection of order theory and social choice theory and also in the field of order theory itself. In particular, we show that KRA is fixed-parameter tractable with respect to natural parameters providing natural formalizations of the notions of diversity and of the notion of a sufficiently good solution. Our main results work both when considering the traditional setting of aggregation over linearly ordered votes, and in the more general setting where votes are partially ordered.

Given two points s and t in the plane and a set of obstacles defined by closed curves, what is the minimum number of obstacles touched by a path connecting s and t? This is a fundamental and well-studied problem arising naturally in computational geometry, graph theory (under the names Min-Color Path and Minimum Label Path), wireless sensor networks (Barrier Resilience) and motion planning (Minimum Constraint Removal). It remains NP-hard even for very simple-shaped obstacles such as unit-length line segments. In this paper we give the first constant factor approximation algorithm for this problem, resolving an open problem of [Chan and Kirkpatrick, TCS, 2014] and [Bandyapadhyay et al., CGTA, 2020]. We also obtain a constant factor approximation for the Minimum Color Prize Collecting Steiner Forest where the goal is to connect multiple request pairs (s1, t1), …, (sk, tk) while minimizing the number of obstacles touched by any (si, ti) path plus a fixed cost of wi for each pair (si, ti) left disconnected. This generalizes the classic Steiner Forest and Prize-Collecting Steiner Forest problems on planar graphs, for which intricate PTASes are known. In contrast, no PTAS is possible for Min-Color Path even on planar graphs since the problem is known to be APX-hard [Eiben and Kanj, TALG, 2020]. Additionally, we show that generalizations of the problem to disconnected obstacles in the plane or connected obstacles in higher dimensions are strongly inapproximable assuming some well-known hardness conjectures.

Over the past decade, many results have focused on the design of parameterized approximation algorithms for W[1]-hard problems. However, there are fundamental problems within the class FPT for which the best known algorithms have seen no progress over the course of the decade; some of them have even been proved not to admit algorithms that run in time 2(k) n(1) under the Exponential Time Hypothesis (ETH) or (c – ∊)k n(1) under the Strong ETH (SETH). In this paper, we expand the study of FPT-approximation and initiate a systematic study of FPT-approximation for problems that are FPT. We design FPT-approximation algorithms for problems that are FPT, with running times that are significantly faster than the corresponding best known FPT-algorithm, and while achieving approximation ratios that are significantly better than what is possible in polynomial time. We present a general scheme to design 2(k) n(1)-time 2-approximation algorithms for cut problems. In particular, we exemplify it for Directed Feedback Vertex Set, Directed Subset Feedback Vertex Set, Directed Odd Cycle Transversal and Undirected Multicut. Further, we extend our scheme to obtain FPT-time (1)-approximation algorithms for weighted cut problems, where the objective is to obtain a solution of size at most k and of minimum weight. Here, we present two approaches. The first approach achieves 2(k) n(1)-time constant-factor approximation, which we exemplify for all problems mentioned in the first bullet. The other leads to an FPT-approximation Scheme (FPT-AS) for Weighted Directed Feedback Vertex Set. Additionally, we present a combinatorial lemma that yields a partition of the vertex set of a graph to roughly equal sized sets so that the removal of each set reduces its treewidth substantially, which may be of independent interest. For several graph problems, use this lemma to design cwn(1)-time (1 + ∊)-approximation algorithms that are faster than known SETH lower bounds, where w is the treewidth of the input graph. Examples of such problems include Vertex Cover, Component Order Connectivity, Bounded-Degree Vertex Deletion and ℱ-Packing for any family ℱ of bounded sized graphs. Lastly, we present a general reduction of problems parameterized by treewidth to their versions parameterized by solution size. Combined with our first scheme, we exemplify it to obtain cwn(1)-time bicriteria approximation algorithms for all problems mentioned in the first bullet.

In this paper we prove an analogue of the classic Bollobás lemma for approximate counting. In fact, we match an analogous result of Fomin et al. [JACM 2016] for decision. This immediately yields, for a number of fundamental problems, parameterized approximate counting algorithms with the same running times as what is obtained for the decision variant using the representative family technique of Fomin et al. [JACM 2016]. For example, we devise an algorithm for approximately counting (a factor (1 ± ∊) approximation algorithm) k-paths in an n-vertex directed graph (#k-Path) running in time (n + m)). This improves over an earlier algorithm of Brand et al. [STOC 2018] that runs in time . Additionally, we obtain an approximate counting analogue of the efficient computation of representative families for product families of Fomin et al. [TALG 2017], again essentially matching the running time for decision. This results in an algorithm with running time for computing a (1 + ∊) approximation of the sum of the coefficients of the multilinear monomials in a degree-k homogeneous n-variate polynomial encoded by a monotone circuit (#Multilinear Monomial Detection). When restricted to monotone circuits (rather than polynomials of non-negative coefficients), this improves upon an earlier algorithm of Pratt [FOCS 2019] that runs in time .

We provide a polynomial-time algorithm for b-Coloring on graphs of constant clique-width. This unifies and extends nearly all previously known polynomial-time results on graph classes, and answers open questions posed by Campos and Silva [Algorithmica, 2018] and Bonomo et al. [Graphs Combin., 2009]. This constitutes the first result concerning structural parameterizations of this problem. We show that the problem is FPT when parameterized by the vertex cover number on general graphs, and on chordal graphs when parameterized by the number of colors. Additionally, we observe that our algorithm for graphs of bounded clique-width can be adapted to solve the Fall Coloring problem within the same runtime bound. The running times of the clique-width based algorithms for b-Coloring and Fall Coloring are tight under the Exponential Time Hypothesis.

Over the past few decades, the study of dense structures from the perspective of approximation algorithms has become a wide area of research. However, from the viewpoint of parameterized algorithm, this area is largely unexplored. In particular, properties of random samples have been successfully deployed to design approximation schemes for a number of fundamental problems on dense structures [Arora et al. FOCS 1995, Goldreich et al. FOCS 1996, Giotis and Guruswami SODA 2006, Karpinksi and Schudy STOC 2009]. In this paper, we fill this gap, and harness the power of random samples as well as structure theory to design kernelization as well as parameterized algorithms on dense structures. In particular, we obtain linear vertex kernels for Edge-Disjoint Paths, Edge Odd Cycle Transversal, Minimum Bisection, d-Way Cut, Multiway Cut and Multicut on everywhere dense graphs. In fact, these kernels are obtained by designing a polynomial-time algorithm when the corresponding parameter is at most Ω(n). Additionally, we obtain a cubic kernel for Vertex-Disjoint Paths on everywhere dense graphs. In addition to kernelization results, we obtain randomized subexponential-time parameterized algorithms for Edge Odd Cycle Transversal, Minimum Bisection, and d-Way Cut. Finally, we show how all of our results (as well as EPASes for these problems) can be de-randomized.

For an integer t, a graph G is called C>t-free if G does not contain any induced cycle on more than t vertices. We prove the following statement: for every pair of integers d and t and a statement φ, there exists an algorithm that, given an n-vertex C>t-free graph G with weights on vertices, finds in time n(log3 n) a maximum-weight vertex subset S such that G[S] has degeneracy at most d and satisfies φ. The running time can be improved to n(log2 n) assuming G is Pt-free, that is, G does not contain an induced path on t vertices. This expands the recent results of the authors [FOCS 2020 and SOSA 2021] on the Maximum Weight Independent Set problem on Pt-free graphs in two directions: by encompassing the more general setting of C>t-free graphs, and by being applicable to a much wider variety of problems, such as Maximum Weight Induced Forest or Maximum Weight Induced Planar Graph.

We show an algorithm that, given an n -vertex graph G and a parameter k , in time 2 O ( k log k ) n O (1) finds a tree decomposition of G with the following properties: — every adhesion of the tree decomposition is of size at most k , and — every bag of the tree decomposition is ( i , i )-unbreakable in G for every 1 ⩽ i ⩽ k . Here, a set X ⊆ V ( G ) is ( a , b )-unbreakable in G if for every separation ( A , B ) of order at most b in G , we have | A \cap X | ⩽ a or | B ∩ X | ⩽ a . The resulting tree decomposition has arguably best possible adhesion size bounds and unbreakability guarantees. Furthermore, the parametric factor in the running time bound is significantly smaller than in previous similar constructions. These improvements allow us to present parameterized algorithms for M INIMUM B ISECTION , S TEINER C UT , and S TEINER M ULTICUT with improved parameteric factor in the running time bound. The main technical insight is to adapt the notion of lean decompositions of Thomas and the subsequent construction algorithm of Bellenbaum and Diestel to the parameterized setting.

Recently, Brand et al. [STOC 2018] gave a randomized mathcal O(4 k m ε -2 -time exponential-space algorithm to approximately compute the number of paths on k vertices in a graph G up to a multiplicative error of 1 ± ε based on exterior algebra. Prior to our work, this has been the state-of-the-art. In this article, we revisit the algorithm by Alon and Gutner [IWPEC 2009, TALG 2010], and obtain the following results: • We present a deterministic 4 k + O (√ k (log k +log 2 ε -1 )) m -time polynomial-space algorithm. This matches the running time of the best known deterministic polynomial-space algorithm for deciding whether a given graph G has a path on k vertices. • Additionally, we present a randomized 4 k +mathcal O(log k (log k +logε -1 )) m -time polynomial-space algorithm. Our algorithm is simple—we only make elementary use of the probabilistic method. Here, n and m are the number of vertices and the number of edges, respectively. Additionally, our approach extends to approximate counting of other patterns of small size (such as q -dimensional p -matchings).

A tournament is a directed graph T such that every pair of vertices is connected by an arc. A feedback vertex set is a set S of vertices in T such that T − S is acyclic. We consider the Feedback Vertex Set problem in tournaments. Here, the input is a tournament T and a weight function w : V ( T ) → N, and the task is to find a feedback vertex set S in T minimizing w ( S ) = ∑ v∈S w ( v ). Rounding optimal solutions to the natural LP-relaxation of this problem yields a simple 3-approximation algorithm. This has been improved to 2.5 by Cai et al. [SICOMP 2000], and subsequently to 7/3 by Mnich et al. [ESA 2016]. In this article, we give the first polynomial time factor 2-approximation algorithm for this problem. Assuming the Unique Games Conjecture, this is the best possible approximation ratio achievable in polynomial time.

The Art Gallery problem is a fundamental visibility problem in Computational Geometry. The input consists of a simple polygon P, (possibly infinite) sets G and C of points within P, and an integer k; the task is to decide if at most k guards can be placed on points in G so that every point in C is visible to at least one guard. In the classic formulation of Art Gallery, G and C consist of all the points within P. Other well-known variants restrict G and C to consist either of all the points on the boundary of P or of all the vertices of P. Recently, three new important discoveries were made: the above mentioned variants of Art Gallery are all W[1]-hard with respect to k [Bonnet and Miltzow, ESA'16], the classic variant has an O(log k)-approximation algorithm [Bonnet and Miltzow, SoCG'17], and it may require irrational guards [Abrahamsen et al., SoCG'17]. Building upon the third result, the classic variant and the case where G consists only of all the points on the boundary of P were both shown to be ∃ℝ-complete [Abrahamsen et al., STOC'18]. Even when both G and C consist only of all the points on the boundary of P, the problem is not known to be in NP. Given the first discovery, the following question was posed by Giannopoulos [Lorentz Center Workshop, 2016]: Is Art Gallery FPT with respect to r, the number of reflex vertices? In light of the developments above, we focus on the variant where G and C consist of all the vertices of P, called Vertex-Vertex Art Gallery. Apart from being a variant of Art Gallery, this case can also be viewed as the classic Dominating Set problem in the visibility graph of a polygon. In this article, we show that the answer to the question by Giannopoulos is positive: Vertex-Vertex Art Gallery is solvable in time r^O(r²)n^O(1). Furthermore, our approach extends to assert that Vertex-Boundary Art Gallery and Boundary-Vertex Art Gallery are both FPT as well. To this end, we utilize structural properties of "almost convex polygons" to present a two-stage reduction from Vertex-Vertex Art Gallery to a new constraint satisfaction problem (whose solution is also provided in this paper) where constraints have arity 2 and involve monotone functions.

Given two points in the plane, a set of obstacles defined by closed curves, and an integer $k$, does there exist a path between the two designated points intersecting at most $k$ of the obstacles? This is a fundamental and well-studied problem arising naturally in computational geometry, graph theory, wireless computing, and motion planning. It remains $\textsf{NP}$-hard even when the obstacles are very simple geometric shapes (e.g., unit-length line segments). In this paper, we show that the problem is fixed-parameter tractable ($\textsf{FPT}$) parameterized by $k$, by giving an algorithm with running time $k^{O(k^3)}n^{O(1)}$. Here $n$ is the number connected areas in the plane drawing of all the obstacles.

We present an algorithm for the extensively studied Long Path and Long Cycle problems on unit disk graphs that runs in time 2^{𝒪(√k)}(n+m). Under the Exponential Time Hypothesis, Long Path and Long Cycle on unit disk graphs cannot be solved in time 2^{o(√k)}(n+m)^𝒪(1) [de Berg et al., STOC 2018], hence our algorithm is optimal. Besides the 2^{𝒪(√k)}(n+m)^𝒪(1)-time algorithm for the (arguably) much simpler Vertex Cover problem by de Berg et al. [STOC 2018] (which easily follows from the existence of a 2k-vertex kernel for the problem), this is the only known ETH-optimal fixed-parameter tractable algorithm on UDGs. Previously, Long Path and Long Cycle on unit disk graphs were only known to be solvable in time 2^{𝒪(√klog k)}(n+m). This algorithm involved the introduction of a new type of a tree decomposition, entailing the design of a very tedious dynamic programming procedure. Our algorithm is substantially simpler: we completely avoid the use of this new type of tree decomposition. Instead, we use a marking procedure to reduce the problem to (a weighted version of) itself on a standard tree decomposition of width 𝒪(√k).

We present an algorithm that takes as input a graph G with weights on the vertices, and computes a maximum weight independent set S of G. If the input graph G excludes a path Pkon k vertices as an induced subgraph, the algorithm runs in time nO(k2log3n). Hence, for every fixed k our algorithm runs in quasi-polynomial time. This resolves in the affirmative an open problem of [Thomassé, SODA'20 invited presentation]. Previous to this work, polynomial time algorithms were only known for P4-free graphs [Corneil et al., DAM'81], P5-free graphs [Lokshtanov et al., SODA'14], and P6-free graphs [Grzesik et al., SODA'19]. For larger values of t, only 2O(√{knlogn})time algorithms [Bacsó et al., Algorithmica'19] and quasipolynomial time approximation schemes [Chudnovsky et al., SODA'20] were known. Thus, our work is the first to offer conclusive evidence that Independent Set on Pk- free graphs is not NP-complete for any integer k. Additionally we show that for every graph H, if there exists a quasi-polynomial time algorithm for Independent Seton C-free graphs for every connected component C of H, then there also exists a quasi-polynomial time algorithm for Independent Set on H-free graphs. This lifts our quasi-polynomial time algorithm to Tk-free graphs, where Tkhas one component that is a Pk, and k-1 components isomorphic to a fork (the unique 5-vertex tree with a degree 3 vertex).

In the Min k-cut problem, input is an edge weighted graph G and an integer k, and the task is to partition the vertex set into k non-empty sets, such that the total weight of the edges with endpoints in different parts is minimized. When k is part of the input, the problem is NP-complete and hard to approximate within any factor less than 2. Recently, the problem has received significant attention from the perspective of parameterized approximation. Gupta et al. [SODA 2018] initiated the study of FPT-approximation for the Min k-Cut problem and gave an 1.9997-approximation algorithm running in time 2O(k6)nO(1). Later, the same set of authors [FOCS 2018] designed an ( 1+ε)-approximation algorithm that runs in time (k/ε)O(k)nk+O(1), and a 1.81-approximation algorithm running in time 2O(k2)nO(1). More, recently, Kawarabayashi and Lin [SODA 2020] gave a (5/3+ε)-approximation for Min k-Cut running in time 2O(k2logk)nO(1). In this paper we give a parameterized approximation algorithm with best possible approximation guarantee, and best possible running time dependence on said guarantee (up to Exponential Time Hypothesis (ETH) and constants in the exponent). In particular, for every , the algorithm obtains a ( 1+ε)-approximate solution in time (k/ε)O(k)nO(1). The main ingredients of our algorithm are: a simple sparsification procedure, a new polynomial time algorithm for decomposing a graph into highly connected parts, and a new exact algorithm with running time sO(k)nO(1)on unweighted (multi-) graphs. Here, s denotes the number of edges in a minimum k-cut. The latter two are of independent interest.

A feedback vertex set in a hypergraph H is a set of vertices S such that deleting S from H results in an acyclic hypergraph. Here, deleting a vertex means removing the vertex and all incident hyperedges, and a hypergraph is acyclic if its vertex-edge incidence graph is acyclic. We study the (parameterized complexity of) the Hypergraph Feedback Vertex Set (HFVS) problem: given as input a hypergraph H and an integer k, determine whether H has a feedback vertex set of size at most k. It is easy to see that this problem generalizes the classic Feedback Vertex Set (FVS) problem on graphs. Remarkably, despite the central role of FVS in parameterized algorithms and complexity, the parameterized complexity of a generalization of FVS to hypergraphs has not been studied previously. In this paper, we fill this void. Our main results are as follows - HFVS is W[2]-hard (as opposed to FVS, which is fixed parameter tractable). - If the input hypergraph is restricted to a linear hypergraph (no two hyperedges intersect in more than one vertex), HFVS admits a randomized algorithm with running time 2^{𝒪(k³log k)}n^{𝒪(1)}. - If the input hypergraph is restricted to a d-hypergraph (hyperedges have cardinality at most d), then HFVS admits a deterministic algorithm with running time d^{𝒪(k)}n^{𝒪(1)}. The algorithm for linear hypergraphs combines ideas from the randomized algorithm for FVS by Becker et al. [J. Artif. Intell. Res., 2000] with the branching algorithm for Point Line Cover by Langerman and Morin [Discrete & Computational Geometry, 2005].

We prove that the Hadwiger number of an $n$-vertex graph $G$ (the maximum size of a clique minor in $G$) cannot be computed in time $n^{o(n)}$, unless the Exponential Time Hypothesis (ETH) fails. This resolves a well-known open question in the area of exact exponential algorithms. The technique developed for resolving the Hadwiger number problem has a wider applicability. We use it to rule out the existence of $n^{o(n)}$-time algorithms (up to ETH) for a large class of computational problems concerning edge contractions in graphs.

In the Split Vertex Deletion (SVD) problem, the input is an n-vertex undirected graph G and a weight function w: V(G) → ℕ, and the objective is to find a minimum weight subset S of vertices such that G-S is a split graph (i.e., there is bipartition of V(G-S) = C ⊎ I such that C is a clique and I is an independent set in G-S). This problem is a special case of 5-Hitting Set and consequently, there is a simple factor 5-approximation algorithm for this. On the negative side, it is easy to show that the problem does not admit a polynomial time (2-δ)-approximation algorithm, for any fixed δ > 0, unless the Unique Games Conjecture fails. We start by giving a simple quasipolynomial time (n^O(log n)) factor 2-approximation algorithm for SVD using the notion of clique-independent set separating collection. Thus, on the one hand SVD admits a factor 2-approximation in quasipolynomial time, and on the other hand this approximation factor cannot be improved assuming UGC. It naturally leads to the following question: Can SVD be 2-approximated in polynomial time? In this work we almost close this gap and prove that for any ε > 0, there is a n^O(log 1/(ε))-time 2(1+ε)-approximation algorithm.

Parameterization above a guarantee is a successful paradigm in Parameterized Complexity. To the best of our knowledge, all fixed-parameter tractable problems in this paradigm share an additive form defined as follows. Given an instance (I,k) of some (parameterized) problem Π with a guarantee g(I), decide whether I admits a solution of size at least (at most) k+g(I). Here, g(I) is usually a lower bound (resp. upper bound) on the maximum (resp. minimum) size of a solution. Since its introduction in 1999 for Max SAT and Max Cut (with g(I) being half the number of clauses and half the number of edges, respectively, in the input), analysis of parameterization above a guarantee has become a very active and fruitful topic of research. We highlight a multiplicative form of parameterization above a guarantee: Given an instance (I,k) of some (parameterized) problem Π with a guarantee g(I), decide whether I admits a solution of size at least (resp. at most) k ⋅ g(I). In particular, we study the Long Cycle problem with a multiplicative parameterization above the girth g(I) of the input graph, and provide a parameterized algorithm for this problem. Apart from being of independent interest, this exemplifies how parameterization above a multiplicative guarantee can arise naturally. We also show that, for any fixed constant ε>0, multiplicative parameterization above g(I)^(1+ε) of Long Cycle yields para-NP-hardness, thus our parameterization is tight in this sense. We complement our main result with the design (or refutation of the existence) of algorithms for other problems parameterized multiplicatively above girth.

In the past decade, the design of fault tolerant data structures for networks has become a central topic of research. Particular attention has been given to the construction of a subgraph H of a given digraph D with as fewest arcs/vertices as possible such that, after the failure of any set F of at most k ≥ 1 arcs, testing whether D-F has a certain property P is equivalent to testing whether H-F has that property. Here, reachability (or, more generally, distance preservation) is the most basic requirement to maintain to ensure that the network functions properly. Given a vertex s ∈ V(D), Baswana et al. [STOC'16] presented a construction of H with O(2^kn) arcs in time O(2^{k}nm) where n=|V(D)| and m= |E(D)| such that for any vertex v ∈ V(D): if there exists a path from s to v in D-F, then there also exists a path from s to v in H-F. Additionally, they gave a tight matching lower bound. While the question of the improvement of the dependency on k arises for special classes of digraphs, an arguably more basic research direction concerns the dependency on n (for reachability between a pair of vertices s,t ∈ V(D)) - which are the largest classes of digraphs where the dependency on n can be made sublinear, logarithmic or even constant? Already for the simple classes of directed paths and tournaments, Ω(n) arcs are mandatory. Nevertheless, we prove that "almost acyclicity" suffices to eliminate the dependency on n entirely for a broad class of dense digraphs called bounded independence digraphs. Also, the dependence in k is only a polynomial factor for this class of digraphs. In fact, our sparsification procedure extends to preserve parity-based reachability. Additionally, it finds notable applications in Kernelization: we prove that the classic Directed Feedback Arc Set (DFAS) problem as well as Directed Edge Odd Cycle Transversal (DEOCT) (which, in sharp contrast to DFAS, is W[1]-hard on general digraphs) admit polynomial kernels on bounded independence digraphs. In fact, for any p ∈ N, we can design a polynomial kernel for the problem of hitting all cycles of length ℓ where (ℓ mod p = 1). As a complementary result, we prove that DEOCT is NP-hard on tournaments by establishing a combinatorial identity between the minimum size of a feedback arc set and the minimum size of an edge odd cycle transversal. In passing, we also improve upon the running time of the sub-exponential FPT algorithm for DFAS in digraphs of bounded independence number given by Misra et at. [FSTTCS 2018], and give the first sub-exponential FPT algorithm for DEOCT in digraphs of bounded independence number.

In a reconfiguration version of an optimization problem $\mathcal{Q}$ the input is an instance of $\mathcal{Q}$ and two feasible solutions $S$ and $T$. The objective is to determine whether there exists a step-by-step transformation between $S$ and $T$ such that all intermediate steps also constitute feasible solutions. In this work, we study the parameterized complexity of the \textsc{Connected Dominating Set Reconfiguration} problem (\textsc{CDS-R)}. It was shown in previous work that the \textsc{Dominating Set Reconfiguration} problem (\textsc{DS-R}) parameterized by $k$, the maximum allowed size of a dominating set in a reconfiguration sequence, is fixed-parameter tractable on all graphs that exclude a biclique $K_{d,d}$ as a subgraph, for some constant $d \geq 1$. We show that the additional connectivity constraint makes the problem much harder, namely, that \textsc{CDS-R} is \textsf{W}$[1]$-hard parameterized by $k+\ell$, the maximum allowed size of a dominating set plus the length of the reconfiguration sequence, already on $5$-degenerate graphs. On the positive side, we show that \textsc{CDS-R} parameterized by $k$ is fixed-parameter tractable, and in fact admits a polynomial kernel on planar graphs.

In this paper, we prove a new scaling lemma for vertex weighted minor free graphs that allows for a smooth trade-off between the weight of a vertex set S and the treewidth of G — S. More precisely, we show the following. There exists an algorithm that given an H-minor free graph G, a weight function w: V(G) → ℚ+ and integers t and s, runs in polynomial time, and outputs a subset S ⊆ V(G) of weight at most d log n · opt(G, w, t)/s such that the treewidth of G – S is at most c·st. Here, d and c are fixed constants that depend only on H, and opt(G, w, t) is the (unknown) minimum weight of a subset U ⊆ V(G) such that the treewidth of G – U is at most t. This lemma immediately yields the first polynomial-time approximation schemes (PTASes) for WEIGHTED Treewidth-η Vertex Deletion, for η > 2, on graphs of bounded genus and the first PTAS for Weighted Feedback vertex Set on H-minor free graphs. These results effortlessly generalize to include weighted edge deletion problems, to all Weighted Connected Planar -Deletion problems, and finally to quasi polynomial time approximation schemes (QPTASes) for all of these problems on H-minor free graphs. For most of these problems even constant factor approximation algorithms, even on planar graphs, were not previously known. Additionally, using the scaling lemma we subsume, simplify and extend the recent framework of Cohen-Addad et al. [STOC 2016] for turning constant factor approximation algorithms for “ubiquitous” problems into PTASes for the same problems on graphs of bounded genus. Specifically, we obtain PTASes for ubiquitous problems without the requirement of having a constant factor approximation. While the statement of the scaling lemma is inspired by an analogous lemma by Cohen-Addad et al. [STOC 2016] for edge contractions on weighted graphs of bounded genus, as well as a scaling lemma by Fomin et al. [SODA 2011] for unweighted graphs, the proof is entirely different. The proof detours via three different linear programming relaxations for the Weighted Treewidth-η Vertex Deletion problems and a strengthening of a recent rounding procedure of Bansal et al. [SODA 2017] enhanced by the classic Klein-Plotkin-Rao Theorem [STOC 1993].

A tournament is a directed graph T such that every pair of vertices is connected by an arc. A feedback vertex set is a set S of vertices in T such that T – S is acyclic. We consider the Feedback Vertex Set problem in tournaments. Here the input is a tournament T and a weight function w: V(T) → ℕ and the task is to find a feedback vertex set S in T minimizing w(S) = ΣvϵSw(v). Rounding optimal solutions to the natural LP-relaxation of this problem yields a simple 3-approximation algorithm. This has been improved to 2.5 by Cai et al. [SICOMP 2000], and subsequently to 7/3 by Mnich et al. [ESA 2016]. In this paper we give the first polynomial time factor 2 approximation algorithm for this problem. Assuming the Unique Games conjecture, this is the best possible approximation ratio achievable in polynomial time.

A directed odd cycle transversal of a directed graph (digraph) D is a vertex set S that intersects every odd directed cycle of D. In the Directed Odd Cycle Transversal (DOCT) problem, the input consists of a digraph D and an integer k. The objective is to determine whether there exists a directed odd cycle transversal of D of size at most k. In this paper, we settle the parameterized complexity of DOCT when parameterized by the solution size k by showing that DOCT does not admit an algorithm with running time unless FPT = W[1]. On the positive side, we give a factor 2 fixed-parameter approximation (FPT approximation) algorithm for the problem. More precisely, our algorithm takes as input D and k, runs in time , and either concludes that D does not have a directed odd cycle transversal of size at most k, or produces a solution of size at most 2k. Finally, assuming gap-ETH, we show that there exists an ϵ > 0 such that DOCT does not admit a factor (1 + ϵ) FPT-approximation algorithm.

In the Topological Minor Deletion (TM-Deletion) problem, the input consists of an undirected graph G, a family of undirected graphs F and an integer k. The task is to determine whether G contains a set of vertices S of size at most k, such that the graph G∖ S obtained from G by removing the vertices of S, contains no graph from F as a topological minor. We give an algorithm forTM-Deletion with running time f(h ⋆,k)· |V(G)|4. Here h ⋆ is the maximum size of a graph in F and f is a computable function of h ⋆ and k. This is the first fixed parameter tractable algorithm (FPT) for the problem. In fact, even for the restricted case of planar inputs the first FPT algorithm was found only recently by Golovach et al. [SODA 2020]. For this case we improve upon the algorithm of Golovach et al. [SODA 2020] by designing an FPT algorithm with explicit dependence on k and h ⋆.

In the Disjoint Paths problem, the input is an undirected graph G on n vertices and a set of k vertex pairs, {s i ,t i } i=1 k , and the task is to find k pairwise vertex-disjoint paths such that the i’th path connects s i to t i . In this paper, we give a parameterized algorithm with running time 2 O(k 2) n O(1) for Planar Disjoint Paths, the variant of the problem where the input graph is required to be planar. Our algorithm is based on the unique linkage/treewidth reduction theorem for planar graphs by Adler et al. [JCTB 2017], the algebraic co-homology based technique developed by Schrijver [SICOMP 1994] for Disjoint Paths on directed planar graphs, and one of the key combinatorial insights developed by Cygan et al. [FOCS 2013] in their algorithm for Disjoint Paths on directed planar graphs. To the best of our knowledge our algorithm is the first parameterized algorithm to exploit that the treewidth of the input graph is small in a way completely different from the use of dynamic programming.

Bidimensionality theory was introduced by [E. D. Demaine et al., J. ACM, 52 (2005), pp. 866--893] as a tool to obtain subexponential time parameterized algorithms on H-minor-free graphs. In [E. D. Demaine and M. Hajiaghayi, Bidimensionality: New connections between FPT algorithms and PTASs, in Proceedings of the 16th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM, Philadelphia, 2005, pp. 590--601] this theory was extended in order to obtain polynomial time approximation schemes (PTASs) for bidimensional problems. In this work, we establish a third meta-algorithmic direction for bidimensionality theory by relating it to the existence of linear kernels for parameterized problems. In particular, we prove that every minor (resp., contraction) bidimensional problem that satisfies a separation property and is expressible in Countable Monadic Second Order Logic (CMSO) admits a linear kernel for classes of graphs that exclude a fixed graph (resp., an apex graph) H as a minor. Our results imply that a multitude of bidimensional problems admit linear kernels on the corresponding graph classes. For most of these problems no polynomial kernels on H-minor-free graphs were known prior to our work.

A graph $G$ is contractible to a graph $H$ if there is a set $X \subseteq E(G)$, such that $G/X$ is isomorphic to $H$. Here, $G/X$ is the graph obtained from $G$ by contracting all the edges in $X$. For a family of graphs $\cal F$, the $\mathcal{F}$-Contraction problem takes as input a graph $G$ on $n$ vertices, and the objective is to output the largest integer $t$, such that $G$ is contractible to a graph $H \in {\cal F}$, where $|V(H)|=t$. When $\cal F$ is the family of paths, then the corresponding $\mathcal{F}$-Contraction problem is called Path Contraction. The problem Path Contraction admits a simple algorithm running in time $2^{n}\cdot n^{{\mathcal{O}}(1)}$. In spite of the deceptive simplicity of the problem, beating the $2^{n}\cdot n^{{\mathcal{O}}(1)}$ bound for Path Contraction seems quite challenging. In this paper, we design an exact exponential time algorithm for Path Contraction that runs in time ${{1.99987}^n}\cdot n^{{\mathcal O}(1)}$. We also define a problem called 3-Disjoint Connected Subgraphs and design an algorithm for it that runs in time $1.88^n\cdot n^{{\mathcal O}(1)}$. The above algorithm is used as a subroutine in our algorithm for Path Contraction.

An undirected graph $G$ is $d$-degenerate if every subgraph of $G$ has a vertex of degree at most $d$. By the classical theorem of Erdös and Gallai from 1959, every graph of degeneracy $d>1$ contains a cycle of length at least $d+1$. The proof of Erdös and Gallai is constructive and can be turned into a polynomial time algorithm constructing a cycle of length at least $d+1$. But can we decide in polynomial time whether a graph contains a cycle of length at least $d+2$? An easy reduction from Hamiltonian Cycle provides a negative answer to this question: Deciding whether a graph has a cycle of length at least $d+2$ is NP-complete. Surprisingly, the complexity of the problem changes drastically when the input graph is 2-connected. In this case we prove that deciding whether $G$ contains a cycle of length at least $d+k$ can be done in time $2^{\mathcal{O}(k)}\cdot|V(G)|^{\mathcal{O}(1)}$. In other words, deciding whether a 2-connected $n$-vertex $G$ contains a cycle of length at least $d+\log{n}$ can be done in polynomial time. Similar algorithmic results hold for long paths in graphs. We observe that deciding whether a graph has a path of length at least $d+1$ is NP-complete. However, we prove that if graph $G$ is connected, then deciding whether $G$ contains a path of length at least $d+k$ can be done in time $2^{\mathcal{O}(k)}\cdot n^{\mathcal{O}(1)}$. We complement these results by showing that the choice of degeneracy as the “above guarantee parameterization” is optimal in the following sense: For any $\varepsilon>0$ it is NP-complete to decide whether a connected (2-connected) graph of degeneracy $d$ has a path (cycle) of length at least $(1+\varepsilon)d$.

For a family of graphs ℱ, the W eighted ℱ V ertex D eletion problem, is defined as follows: given an n -vertex undirected graph G and a weight function w : V ( G )࢐ ℝ, find a minimum weight subset S ⊆ V ( G ) such that G - S belongs to ℱ. We devise a recursive scheme to obtain O(log O(1) n )-approximation algorithms for such problems, building upon the classical technique of finding balanced separators . We obtain the first O(log O(1) n )-approximation algorithms for the following problems. • Let F be a finite set of graphs containing a planar graph, and ℱ= G ( F ) be the maximal family of graphs such that every graph H ∈ G ( F ) excludes all graphs in F as minors. The vertex deletion problem corresponding to ℱ= G ( F ) is the W eighted P lanar F -M inor -F ree D eletion (WP F -MFD) problem. We give a randomized and a deterministic approximation algorithms for WP F -MFD with ratios O(log 1.5 n ) and O(log 2 n ), respectively. Prior to our work, a randomized constant factor approximation algorithm for the unweighted version was known [FOCS 2012]. After our work, a deterministic constant factor approximation algorithm for the unweighted version was also obtained [SODA 2019]. • We give an O(log 2 n )-factor approximation algorithm for W eighted C hordal V ertex D eletion , the vertex deletion problem to the family of chordal graphs. On the way to this algorithm, we also obtain a constant factor approximation algorithm for M ulticut on chordal graphs. • We give an O(log 3 n )-factor approximation algorithm for W eighted D istance H ereditary V ertex D eletion . We believe that our recursive scheme can be applied to obtain O(log O(1) n )-approximation algorithms for many other problems as well.

We provide a randomized linear time approximation scheme for a generic problem about clustering of binary vectors subject to additional constraints. The new constrained clustering problem generalizes a number of problems and by solving it, we obtain the first linear time-approximation schemes for a number of well-studied fundamental problems concerning clustering of binary vectors and low-rank approximation of binary matrices. Among the problems solvable by our approach are L ow GF(2)-R ank A pproximation , L ow B oolean -R ank A pproximation , and various versions of B inary C lustering . For example, for L ow GF(2)-R ank A pproximation problem, where for an m × n binary matrix A and integer r > 0, we seek for a binary matrix B of GF(2) rank at most r such that the ℓ 0 -norm of matrix A−B is minimum, our algorithm, for any ϵ > 0 in time f ( r ,ϵ)⋅ n ⋅ m , where f is some computable function, outputs a (1+ϵ)-approximate solution with probability at least (1−1\ e ). This is the first linear time approximation scheme for these problems. We also give (deterministic) PTASes for these problems running in time n f ( r )1\ϵ 2 log 1\ϵ , where f is some function depending on the problem. Our algorithm for the constrained clustering problem is based on a novel sampling lemma, which is interesting on its own.

A rectilinear Steiner tree for a set K of points in the plane is a tree that connects k using horizontal and vertical lines. In the R ectilinear S teiner T ree problem, the input is a set K ={ z 1 , z 2 ,…, z n } of n points in the Euclidean plane (R 2 ), and the goal is to find a rectilinear Steiner tree for k of smallest possible total length. A rectilinear Steiner arborescence for a set k of points and a root r ∈ K is a rectilinear Steiner tree T for K such that the path in T from r to any point z ∈ K is a shortest path. In the R ectilinear S teiner A rborescence problem, the input is a set K of n points in R 2 , and a root r ∈ K , and the task is to find a rectilinear Steiner arborescence for K , rooted at r of smallest possible total length. In this article, we design deterministic algorithms for these problems that run in 2 O (√ n log n ) time.

We study the first-order (FO) model checking problem of dense graph classes, namely, those that have FO interpretations in (or are FO transductions of) some sparse graph classes. We give a structural characterization of the graph classes that are FO interpretable in graphs of bounded degree. This characterization allows us to efficiently compute such an FO interpretation for an input graph. As a consequence, we obtain an FPT algorithm for successor-invariant FO model checking on any graph class that is FO interpretable in (or an FO transduction of) a graph class of bounded degree. The approach we use to obtain these results may also be of independent interest.

In the Bisection problem, we are given as input an edge-weighted graph G. The task is to find a partition of V(G) into two parts A and B such that ||A| - |B|| <= 1 and the sum of the weights of the edges with one endpoint in A and the other in B is minimized. We show that the complexity of the Bisection problem on trees, and more generally on graphs of bounded treewidth, is intimately linked to the (min, +)-Convolution problem. Here the input consists of two sequences (a[i])^{n-1}_{i = 0} and (b[i])^{n-1}_{i = 0}, the task is to compute the sequence (c[i])^{n-1}_{i = 0}, where c[k] = min_{i=0,...,k}(a[i] + b[k - i]). In particular, we prove that if (min, +)-Convolution can be solved in O(tau(n)) time, then Bisection of graphs of treewidth t can be solved in time O(8^t t^{O(1)} log n * tau(n)), assuming a tree decomposition of width t is provided as input. Plugging in the naive O(n^2) time algorithm for (min, +)-Convolution yields a O(8^t t^{O(1)} n^2 log n) time algorithm for Bisection. This improves over the (dependence on n of the) O(2^t n^3) time algorithm of Jansen et al. [SICOMP 2005] at the cost of a worse dependence on t. "Conversely", we show that if Bisection can be solved in time O(beta(n)) on edge weighted trees, then (min, +)-Convolution can be solved in O(beta(n)) time as well. Thus, obtaining a sub-quadratic algorithm for Bisection on trees is extremely challenging, and could even be impossible. On the other hand, for unweighted graphs of treewidth t, by making use of a recent algorithm for Bounded Difference (min, +)-Convolution of Chan and Lewenstein [STOC 2015], we obtain a sub-quadratic algorithm for Bisection with running time O(8^t t^{O(1)} n^{1.864} log n).

A graph G is contractible to a graph H if there is a set X subseteq E(G), such that G/X is isomorphic to H. Here, G/X is the graph obtained from G by contracting all the edges in X. For a family of graphs F, the F-Contraction problem takes as input a graph G on n vertices, and the objective is to output the largest integer t, such that G is contractible to a graph H in F, where |V(H)|=t. When F is the family of paths, then the corresponding F-Contraction problem is called Path Contraction. The problem Path Contraction admits a simple algorithm running in time 2^n * n^{O(1)}. In spite of the deceptive simplicity of the problem, beating the 2^n * n^{O(1)} bound for Path Contraction seems quite challenging. In this paper, we design an exact exponential time algorithm for Path Contraction that runs in time 1.99987^n * n^{O(1)}. We also define a problem called 3-Disjoint Connected Subgraphs, and design an algorithm for it that runs in time 1.88^n * n^{O(1)}. The above algorithm is used as a sub-routine in our algorithm for Path Contraction.

A few years ago, Alon et al. [ISMB 2008] gave a simple randomized O((2e)^km epsilon^{-2})-time exponential-space algorithm to approximately compute the number of paths on k vertices in a graph G up to a multiplicative error of 1 +/- epsilon. Shortly afterwards, Alon and Gutner [IWPEC 2009, TALG 2010] gave a deterministic exponential-space algorithm with running time (2e)^{k+O(log^3k)}m log n whenever epsilon^{-1}=k^{O(1)}. Recently, Brand et al. [STOC 2018] provided a speed-up at the cost of reintroducing randomization. Specifically, they gave a randomized O(4^km epsilon^{-2})-time exponential-space algorithm. In this article, we revisit the algorithm by Alon and Gutner. We modify the foundation of their work, and with a novel twist, obtain the following results. - We present a deterministic 4^{k+O(sqrt{k}(log^2k+log^2 epsilon^{-1}))}m log n-time polynomial-space algorithm. This matches the running time of the best known deterministic polynomial-space algorithm for deciding whether a given graph G has a path on k vertices. - Additionally, we present a randomized 4^{k+O(log k(log k + log epsilon^{-1}))}m log n-time polynomial-space algorithm. While Brand et al. make non-trivial use of exterior algebra, our algorithm is very simple; we only make elementary use of the probabilistic method. Thus, the algorithm by Brand et al. runs in time 4^{k+o(k)}m whenever epsilon^{-1}=2^{o(k)}, while our deterministic and randomized algorithms run in time 4^{k+o(k)}m log n whenever epsilon^{-1}=2^{o(k^{1/4})} and epsilon^{-1}=2^{o(k/(log k))}, respectively. Prior to our work, no 2^{O(k)}n^{O(1)}-time polynomial-space algorithm was known. Additionally, our approach is embeddable in the classic framework of divide-and-color, hence it immediately extends to approximate counting of graphs of bounded treewidth; in comparison, Brand et al. note that their approach is limited to graphs of bounded pathwidth.

Bidimensionality is the most common technique to design subexponential-time parameterized algorithms on special classes of graphs, particularly planar graphs. The core engine behind it is a combinatorial lemma of Robertson, Seymour and Thomas that states that every planar graph either has a $\sqrt{k}\times \sqrt{k}$-grid as a minor, or its treewidth is $O(\sqrt{k})$. However, bidimensionality theory cannot be extended directly to several well-known classes of geometric graphs. Nevertheless, a relaxation of this lemma has been proven useful for unit disk graphs. Inspired by this, we prove a new decomposition lemma for map graphs. Informally, our lemma states the following. For any map graph $G$, there exists a collection $(U_1,\ldots,U_t)$ of cliques of $G$ with the following property: $G$ either contains a $\sqrt{k}\times \sqrt{k}$-grid as a minor, or it admits a tree decomposition where every bag is the union of $O(\sqrt{k})$ of the cliques in the above collection. The new lemma appears to be a handy tool in the design of subexponential parameterized algorithms on map graphs. We demonstrate its usability by designing algorithms on map graphs with running time $2^{O({\sqrt{k}\log{k}})} \cdot n^{O(1)}$ for the Connected Planar $\cal F$-Deletion problem (that encompasses problems such as Feedback Vertex Set and Vertex Cover). Obtaining subexponential algorithms for Longest Cycle/Path and Cycle Packing is more challenging. We have to construct tree decompositions with more powerful properties and to prove sublinear bounds on the number of ways an optimum solution could "cross" bags in these decompositions. For Longest Cycle/Path, these are the first subexponential-time parameterized algorithms on map graphs. For Feedback Vertex Set and Cycle Packing, we improve upon known $2^{O({k^{0.75}\log{k}})} \cdot n^{O(1)}$-time algorithms on map graphs.

A bond of a graph G is an inclusion-wise minimal disconnecting set of G, i.e., bonds are cut-sets that determine cuts [S,V\S] of G such that G[S] and G[V\S] are both connected. Given s,t in V(G), an st-bond of G is a bond whose removal disconnects s and t. Contrasting with the large number of studies related to maximum cuts, there are very few results regarding the largest bond of general graphs. In this paper, we aim to reduce this gap on the complexity of computing the largest bond and the largest st-bond of a graph. Although cuts and bonds are similar, we remark that computing the largest bond of a graph tends to be harder than computing its maximum cut. We show that Largest Bond remains NP-hard even for planar bipartite graphs, and it does not admit a constant-factor approximation algorithm, unless P = NP. We also show that Largest Bond and Largest st-Bond on graphs of clique-width w cannot be solved in time f(w) x n^{o(w)} unless the Exponential Time Hypothesis fails, but they can be solved in time f(w) x n^{O(w)}. In addition, we show that both problems are fixed-parameter tractable when parameterized by the size of the solution, but they do not admit polynomial kernels unless NP subseteq coNP/poly.

We survey some recent graph algorithms that are based on picking a vertex at random and declaring it to be a part of the solution. This simple idea has been deployed to obtain state-of-the-art parameterized, exact exponential time, and approximation algorithms for a number of problems, such as Feedback Vertex Set and 3-Hitting Set. We will also discuss a recent 2-approximation algorithm for Feedback Vertex Set in Tournaments that is based on picking a vertex at random and declaring it to not be part of the solution.

In algorithmic graph theory, a classic open question is to determine the complexity of the Maximum Independent Set problem on $$P_t$$ -free graphs, that is, on graphs not containing any induced path on t vertices. So far, polynomial-time algorithms are known only for $$t\le 5$$ (Lokshtanov et al., in: Proceedings of the twenty-fifth annual ACM-SIAM symposium on discrete algorithms, SODA 2014, Portland, OR, USA, January 5–7, 2014, pp 570–581, 2014), and an algorithm for $$t=6$$ announced recently (Grzesik et al. in Polynomial-time algorithm for maximum weight independent set on $${P}_6$$ -free graphs. CoRR, arXiv:1707.05491 , 2017). Here we study the existence of subexponential-time algorithms for the problem: we show that for any $$t\ge 1$$ , there is an algorithm for Maximum Independent Set on $$P_t$$ -free graphs whose running time is subexponential in the number of vertices. Even for the weighted version MWIS, the problem is solvable in $$2^{\mathcal {O}(\sqrt{tn \log n})}$$ time on $$P_t$$ -free graphs. For approximation of MIS in broom-free graphs, a similar time bound is proved. Scattered Set is the generalization of Maximum Independent Set where the vertices of the solution are required to be at distance at least d from each other. We give a complete characterization of those graphs H for which d-Scattered Set on H-free graphs can be solved in time subexponential in the size of the input (that is, in the number of vertices plus the number of edges):

We give algorithms with running time $$2^{\mathcal {O}({\sqrt{k}\log {k}})} \cdot n^{\mathcal {O}(1)}$$ for the following problems. Given an n-vertex unit disk graph G and an integer k, decide whether G contains For the first three problems, no subexponential time parameterized algorithms were previously known. For the remaining two problems, our algorithms significantly outperform the previously best known parameterized algorithms that run in time $$2^{\mathcal {O}(k^{0.75}\log {k})} \cdot n^{\mathcal {O}(1)}$$ . Our algorithms are based on a new kind of tree decompositions of unit disk graphs where the separators can have size up to $$k^{\mathcal {O}(1)}$$ and there exists a solution that crosses every separator at most $$\mathcal {O}(\sqrt{k})$$ times. The running times of our algorithms are optimal up to the $$\log {k}$$ factor in the exponent, assuming the exponential time hypothesis.

In the classic Minimum Bisection problem we are given as input an undirected graph $G$ and an integer $k$. The task is to determine whether there is a partition of $V(G)$ into two parts $A$ and $B$ such that $||A|-|B|| \leq 1$ and there are at most $k$ edges with one endpoint in $A$ and the other in $B$. In this paper we give an algorithm for Minimum Bisection with running time $2^{\mathcal{O}(k^3)}n^3 \log^3 n$. This is the first fixed parameter tractable algorithm for Minimum Bisection parameterized by $k$. At the core of our algorithm lies a new decomposition theorem that states that every graph $G$ can be decomposed by small separators into parts where each part is “highly connected” in the following sense: any separator of bounded size can separate only a limited number of vertices from each part of the decomposition. Our techniques generalize to the weighted setting, where we seek a bisection of minimum weight among solutions that contain at most $k$ edges.

In the Steiner Tree problem, we are given as input a connected $n$-vertex graph with edge weights in $\{1,2,\ldots,W\}$, and a set of $k$ terminal vertices. Our task is to compute a minimum-weight tree that contains all of the terminals. The main result of the paper is an algorithm solving Steiner Tree in time $\mathcal{O}(7.97^k\cdot n^4\cdot \log{W})$ and using $\mathcal{O}(n^3\cdot \log{nW} \cdot \log k)$ space. This is the first single-exponential time, polynomial space FPT algorithm for the weighted Steiner Tree problem. Whereas our main result seeks to optimize the polynomial dependency in $n$ for both the running time and space usage, it is possible to trade between polynomial dependence in $n$ and the single-exponential dependence in $k$ to obtain faster running time as a function of $k$, but at the cost of increased running time and space usage as a function of $n$. In particular, we show that there exists a polynomial space algorithm for Steiner Tree running in $\mathcal{O}(6.751^kn^{O(1)}\log W)$ time. Finally, by pushing such a trade-off between a polynomial in $n$ and an exponential in $k$ dependencies, we show that for any $\epsilon>0$ there is an $n^{\mathcal{O}(f(\epsilon))}\log W$ space $4^{(1+\epsilon)k}n^{\mathcal{O}(f(\epsilon))}\log W$ time algorithm for Steiner Tree, where $f$ is a computable function depending only on $\epsilon$.

The Cycle Packing problem asks whether a given undirected graph $G=(V,E)$ contains $k$ vertex-disjoint cycles. Since the publication of the classic Erdös--Pósa theorem in 1965, this problem received significant attention in the fields of graph theory and algorithm design. In particular, this problem is one of the first problems studied in the framework of parameterized complexity. The nonuniform fixed-parameter tractability of Cycle Packing follows from the Robertson--Seymour theorem, a fact already observed by Fellows and Langston in the 1980s. In 1994, Bodlaender showed that Cycle Packing can be solved in time $2^{\mathcal{O}(k^2)}\cdot |V|$ using exponential space. In the case a solution exists, Bodlaender's algorithm also outputs a solution (in the same time). It has later become common knowledge that Cycle Packing admits a $2^{\mathcal{O}(k\log^2k)}\cdot |V|$-time (deterministic) algorithm using exponential space, which is a consequence of the Erdös--Pósa theorem. Nowadays, the design of this algorithm is given as an exercise in textbooks on parameterized complexity. Yet, no algorithm that runs in time $2^{o(k\log^2k)}\cdot |V|^{\mathcal{O}(1)}$, beating the bound $2^{\mathcal{O}(k\log^2k)}\cdot |V|^{\mathcal{O}(1)}$, has been found. In light of this, it seems natural to ask whetherthe $2^{\mathcal{O}(k\log^2k)}\cdot |V|^{\mathcal{O}(1)}$ bound is essentially optimal. In this paper, we answer this question negatively by developing a $2^{\mathcal{O}(\frac{k\log^2k}{\log\log k})}\cdot |V|$-time (deterministic) algorithm for Cycle Packing. In the case a solution exists, our algorithm also outputs a solution (in the same time). Moreover, apart from beating the bound $2^{\mathcal{O}(k\log^2k)}\cdot |V|^{\mathcal{O}(1)}$, our algorithm runs in time linear in $|V|$, and its space complexity is polynomial in the input size.

The family of judicious partitioning problems, introduced by Bollobás and Scott to the field of extremal combinatorics, has been extensively studied from a structural point of view for over two decades. This rich realm of problems aims to counterbalance the objectives of classical partitioning problems such as Min Cut, Min Bisection, and Max Cut. While these classical problems focus solely on the minimization/maximization of the number of edges crossing the cut, judicious (bi)partitioning problems ask the natural question of the minimization/maximization of the number of edges lying in the (two) sides of the cut. In particular, Judicious Bipartition (JB) seeks a bipartition that is “judicious” in the sense that neither side is burdened by too many edges, and Balanced JB (BJB) also requires that the sizes of the sides themselves are “balanced” in the sense that neither of them is too large. Both of these problems were defined in the work by Bollobás and Scott and have received notable scientific attention since then. In this paper, we shed light on the study of judicious partitioning problems from the viewpoint of algorithm design. Specifically, we prove that BJB is fixed parameter tractable (FPT) (which also proves that JB is FPT).

Given a graph G and a parameter k , the C hordal V ertex D eletion (CVD) problem asks whether there exists a subset U ⊆ V ( G ) of size at most k that hits all induced cycles of size at least 4. The existence of a polynomial kernel for CVD was a well-known open problem in the field of Parameterized Complexity. Recently, Jansen and Pilipczuk resolved this question affirmatively by designing a polynomial kernel for CVD of size O ( k 161 log 58 k ) and asked whether one can design a kernel of size O ( k 10 ) [Jansen an Pilipczuk, SODA 2017]. While we do not completely resolve this question, we design a significantly smaller kernel of size O ( k 12 log 10 k ), inspired by the O ( k 2 ) -size kernel for F eedback V ertex S et [Thomassé, TALG 2010]. Furthermore, we introduce the notion of the independence degree of a vertex, which is our main conceptual contribution.

We consider the fundamental Matroid Theory problem of finding a circuit in a matroid containing a set T of given terminal elements. For graphic matroids, this corresponds to the problem of finding a simple cycle passing through a set of given terminal edges in a graph. The algorithmic study of the problem on regular matroids, a superclass of graphic matroids, was initiated by Gavenčiak, Král’, and Oum [ICALP’12], who proved that the case of the problem with ∣T∣ = 2 is fixed-parameter tractable (FPT) when parameterized by the length of the circuit. We extend the result of Gavenčiak, Král’, and Oum by showing that for regular matroids • the M inimum S panning C ircuit problem, deciding whether there is a circuit with at most ℓ elements containing T , is FPT parameterized by k = ℓ − ∣T∣ • the S panning C ircuit problem, deciding whether there is a circuit containing ∣T∣, is FPT parameterized by ∣T∣. We note that extending our algorithmic findings to binary matroids, a superclass of regular matroids, is highly unlikely: M inimum S panning C ircuit parameterized by ℓ is W[1]-hard on binary matroids even when ∣T∣ = 1. We also show a limit to how far our results can be strengthened by considering a smaller parameter. More precisely, we prove that M inimum S panning C ircuit parameterized by ∣T∣ is W[1]-hard even on cographic matroids, a proper subclass of regular matroids.

M AX -C UT , E DGE D OMINATING S ET , G RAPH C OLORING , and H AMILTONIAN C YCLE on graphs of bounded clique-width have received significant attention as they can be formulated in MSO 2 (and, therefore, have linear-time algorithms on bounded treewidth graphs by the celebrated Courcelle’s theorem), but cannot be formulated in MSO 1 (which would have yielded linear-time algorithms on bounded clique-width graphs by a well-known theorem of Courcelle, Makowsky, and Rotics). Each of these problems can be solved in time g ( k ) n f ( k ) on graphs of clique-width k . Fomin et al. (2010) showed that the running times cannot be improved to g ( k ) n O (1) assuming W[1]≠FPT. However, this does not rule out non-trivial improvements to the exponent f ( k ) in the running times. In a follow-up paper, Fomin et al. (2014) improved the running times for E DGE D OMINATING S ET and M AX -C UT to n O ( k ) , and proved that these problems cannot be solved in time g ( k ) n o ( k ) unless ETH fails. Thus, prior to this work, E DGE D OMINATING S ET and M AX -C UT were known to have tight n Θ ( k ) algorithmic upper and lower bounds. In this article, we provide lower bounds for H AMILTONIAN C YCLE and G RAPH C OLORING . For H AMILTONIAN C YCLE , our lower bound g ( k ) n o ( k ) matches asymptotically the recent upper bound n O ( k ) due to Bergougnoux, Kanté, and Kwon (2017). As opposed to the asymptotically tight n Θ( k ) bounds for E DGE D OMINATING S ET , M AX -C UT , and H AMILTONIAN C YCLE , the G RAPH C OLORING problem has an upper bound of n O (2 k ) and a lower bound of merely n o (√ [4] k ) (implicit from the W[1]-hardness proof). In this article, we close the gap for G RAPH C OLORING by proving a lower bound of n 2 o ( k ) . This shows that G RAPH C OLORING behaves qualitatively different from the other three problems. To the best of our knowledge, G RAPH C OLORING is the first natural problem known to require exponential dependence on the parameter in the exponent of n .

We consider four well-studied NP-complete packing/covering problems on graphs: F eedback V ertex S et in T ournaments (FVST), C luster V ertex D eletion (CVD), T riangle P acking in T ournaments (TPT) and I nduced P 3 -P acking . For these four problems, kernels with O ( k 2 ) vertices have been known for a long time. In fact, such kernels can be obtained by interpreting these problems as finding either a packing of k pairwise disjoint sets of size 3 (3-S et P acking ) or a hitting set of size at most k for a family of sets of size at most 3 (3-H itting S et ). In this article, we give the first kernels for FVST, CVD, TPT, and I nduced P 3 -P acking with a subquadratic number of vertices. Specifically, we obtain the following results. • FVST admits a kernel with O ( k 3/2 ) vertices. • CVD admits a kernel with O ( k 5/3 ) vertices. • TPT admits a kernel with O ( k 3/2 ) vertices. • I nduced P 3 -P acking admits a kernel with O ( k 5/3 ) vertices. Our results resolve an open problem from WorKer 2010 on the existence of kernels with O( k 2−ϵ ) vertices for FVST and CVD. All of our results are based on novel uses of old and new “expansion lemmas” and a weak form of crown decomposition where (i) almost all of the head is used by the solution (as opposed to all ), (ii) almost none of the crown is used by the solution (as opposed to none ), and (iii) if H is removed from G , then there is almost no interaction between the head and the rest (as opposed to no interaction at all).

For a family of graphs $\cal F$, the canonical Weighted $\cal F$ Vertex Deletion problem is defined as follows: given an $n$-vertex undirected graph $G$ and a weight function $w: V(G)\rightarrow\mathbb{R}$, find a minimum weight subset $S\subseteq V(G)$ such that $G-S$ belongs to $\cal F$. We devise a recursive scheme to obtain $O(\log^{O(1)}n)$-approximation algorithms for such problems, building upon the classic technique of finding balanced separators in a graph. Roughly speaking, our scheme applies to problems where an optimum solution $S$, together with a well-structured set $X$, form a balanced separator of $G$. We obtain the first $O(\log^{O(1)}n)$-approximation algorithms for the following problems. * We give an $O(\log^2n)$-factor approximation algorithm for Weighted Chordal Vertex Deletion (WCVD), the vertex deletion problem to the family of chordal graphs. On the way, we also obtain a constant factor approximation algorithm for Multicut on chordal graphs. * We give an $O(\log^3n)$-factor approximation algorithm for Weighted Distance Hereditary Vertex Deletion (WDHVD). This is the vertex deletion problem to the family of distance hereditary graphs, or equivalently, the family of graphs of rankwidth 1. Our methods also allow us to obtain in a clean fashion a $O(\log^{1.5}n)$-approximation algorithm for the Weighted $\cal F$ Vertex Deletion problem when $\cal F$ is a minor closed family excluding at least one planar graph. For the unweighted version of the problem constant factor approximation algorithms are were known~[Fomin et al., FOCS~2012], while for the weighted version considered here an $O(\log n \log\log n)$-approximation algorithm follows from~[Bansal et al., SODA~2017]. We believe that our recursive scheme can be applied to obtain $O(\log^{O(1)}n)$-approximation algorithms for many other problems as well.

We study the problem of finding a minimum-distortion embedding of the shortest path metric of an unweighted graph into a "simpler" metric X. Computing such an embedding (exactly or approximately) is a non-trivial task even when X is the metric induced by a path, or, equivalently, the real line. In this paper we give approximation and fixed-parameter tractable (FPT) algorithms for minimum-distortion embeddings into the metric of a subdivision of some fixed graph H, or, equivalently, into any fixed 1-dimensional simplicial complex. More precisely, we study the following problem: For given graphs G, H and integer c, is it possible to embed G with distortion c into a graph homeomorphic to H? Then embedding into the line is the special case H=K_2, and embedding into the cycle is the case H=K_3, where K_k denotes the complete graph on k vertices. For this problem we give - an approximation algorithm, which in time f(H)* poly (n), for some function f, either correctly decides that there is no embedding of G with distortion c into any graph homeomorphic to H, or finds an embedding with distortion poly(c); - an exact algorithm, which in time f'(H, c)* poly (n), for some function f', either correctly decides that there is no embedding of G with distortion c into any graph homeomorphic to H, or finds an embedding with distortion c. Prior to our work, poly(OPT)-approximation or FPT algorithms were known only for embedding into paths and trees of bounded degrees.

In the Directed Feedback Vertex Set (DFVS) problem, we are given as input a directed graph D and an integer k, and the objective is to check whether there exists a set S of at most k vertices such that F=D-S is a directed acyclic graph (DAG). Determining whether DFVS admits a polynomial kernel (parameterized by the solution size) is one of the most important open problems in parameterized complexity. In this article, we give a polynomial kernel for DFVS parameterized by the solution size plus the size of any treewidth-eta modulator, for any positive integer eta. We also give a polynomial kernel for the problem, which we call Vertex Deletion to treewidth-eta DAG, where given as input a directed graph D and a positive integer k, the objective is to decide whether there exists a set of at most k vertices, say S, such that D-S is a DAG and the treewidth of D-S is at most eta.

Given a Counting Monadic Second Order (CMSO) sentence $ψ$, the CMSO$[ψ]$ problem is defined as follows. The input to CMSO$[ψ]$ is a graph $G$, and the objective is to determine whether $G\models ψ$. Our main theorem states that for every CMSO sentence $ψ$, if CMSO$[ψ]$ is solvable in polynomial time on "globally highly connected graphs", then CMSO$[ψ]$ is solvable in polynomial time (on general graphs). We demonstrate the utility of our theorem in the design of parameterized algorithms. Specifically we show that technical problem-specific ingredients of a powerful method for designing parameterized algorithms, recursive understanding, can be replaced by a black-box invocation of our main theorem. We also show that our theorem can be easily deployed to show fixed parameterized tractability of a wide range of problems, where the input is a graph $G$ and the task is to find a connected induced subgraph of $G$ such that "few" vertices in this subgraph have neighbors outside the subgraph, and additionally the subgraph has a CMSO-definable property.

Deterministic polynomial-time computation of a representation of a transversal matroid is a longstanding open problem. We present a deterministic computation of a so-called union representation of a transversal matroid in time quasipolynomial in the rank of the matroid. More precisely, we output a collection of linear matroids such that a set is independent in the transversal matroid if and only if it is independent in at least one of them. Our proof directly implies that if one is interested in preserving independent sets of size at most r, for a given r\in\mathbb{N}, but does not care whether larger independent sets are preserved, then a union representation can be computed deterministically in time quasipolynomial in r. This consequence is of independent interest, and sheds light on the power of union~representation. Our main result also has applications in Parameterized Complexity. First, it yields a fast computation of representative sets, and due to our relaxation in the context of r, this computation also extends to (standard) truncations. In turn, this computation enables to efficiently solve various problems, such as subcases of subgraph isomorphism, motif search and packing problems, in the presence of color lists. Such problems have been studied to model scenarios where pairs of elements to be matched may not be identical but only similar, and color lists aim to describe the set of compatible elements associated with each element.

We consider problems where the input is a set of points in the plane and an integer $k$, and the task is to find a subset $S$ of the input points of size $k$ such that $S$ satisfies some property. We focus on properties that depend only on the order type of the points and are monotone under point removals. We show that not all such problems are fixed-parameter tractable parameterized by $k$, by exhibiting a property defined by three forbidden patterns for which finding a $k$-point subset with the property is $\mathrm{W}[1]$-complete and (assuming the exponential time hypothesis) cannot be solved in time $n^{o(k/\log k)}$. However, we show that problems of this type are fixed-parameter tractable for all properties that include all collinear point sets, properties that exclude at least one convex polygon, and properties defined by a single forbidden pattern.

In the embedded planar diameter improvement problem (EPDI) we are given a graph G embedded in the plane and a positive integer d. The goal is to determine whether one can add edges to the planar embedding of G in such a way that planarity is preserved and in such a way that the resulting graph has diameter at most d. Using non-constructive techniques derived from Robertson and Seymour's graph minor theory, together with the effectivization by self-reduction technique introduced by Fellows and Langston, one can show that EPDI can be solved in time f(d)* |V(G)|^{O(1)} for some function f(d). The caveat is that this algorithm is not strongly uniform in the sense that the function f(d) is not known to be computable. On the other hand, even the problem of determining whether EPDI can be solved in time f_1(d)* |V(G)|^{f_2(d)} for computable functions f_1 and f_2 has been open for more than two decades [Cohen at. al. Journal of Computer and System Sciences, 2017]. In this work we settle this later problem by showing that EPDI can be solved in time f(d)* |V(G)|^{O(d)} for some computable function f. Our techniques can also be used to show that the embedded k-outerplanar diameter improvement problem (k-EOPDI), a variant of EPDI where the resulting graph is required to be k-outerplanar instead of planar, can be solved in time f(d)* |V(G)|^{O(k)} for some computable function f. This shows that for each fixed k, the problem k-EOPDI is strongly uniformly fixed parameter tractable with respect to the diameter parameter d.

In this paper we study recently introduced conflict version of the classical Feedback Vertex Set (FVS) problem. For a family of graphs F, we consider the problem F-CF-Feedback Vertex Set (F-CF-FVS, for short). The F-CF-FVS problem takes as an input a graph G, a graph H in F (where V(G)=V(H)), and an integer k, and the objective is to decide if there is a set S subseteq V(G) of size at most k such that G-S is a forest and S is an independent set in H. Observe that if we instantiate F to be the family of edgeless graphs then we get the classical FVS problem. Jain, Kanesh, and Misra [CSR 2018] showed that in contrast to FVS, F-CF-FVS is W[1]-hard on general graphs and admits an FPT algorithm if F is the family of d-degenerate graphs. In this paper, we relate F-CF-FVS to the Independent Set problem on special classes of graphs, and obtain a complete dichotomy result on the Parameterized Complexity of the problem F-CF-FVS, when F is a hereditary graph family. In particular, we show that F-CF-FVS is FPT parameterized by the solution size if and only if F+Cluster IS is FPT parameterized by the solution size. Here, F+Cluster IS is the Independent Set problem in the (edge) union of a graph G in F and a cluster graph H (G and H are explicitly given). Next, we exploit this characterization to obtain new FPT results as well as intractability results for F-CF-FVS. In particular, we give an FPT algorithm for F+Cluster IS when F is the family of K_{i,j}-free graphs. We show that for the family of bipartite graph B, B-CF-FVS is W[1]-hard, when parameterized by the solution size. Finally, we consider, for each 0< epsilon<1, the family of graphs F_epsilon, which comprise of graphs G such that |E(G)| <= |V(G)|^(2-epsilon), and show that F_epsilon-CF-FVS is W[1]-hard, when parameterized by the solution size, for every 0<epsilon<1.

Max-Cut (MC), Edge Dominating Set (EDS), Graph Coloring (GC) and Hamiltonian Path (HP) on graphs of bounded cliquewidth have received significant attention as they can be formulated in MSO2 (and therefore have linear-time algorithms on bounded treewidth graphs by the celebrated Courcelle's theorem), but cannot be formulated in MSO1 (which would have yielded linear-time algorithms on bounded cliquewidth graphs by a well-known theorem of Courcelle, Makowsky, and Rotics). Each of these problems can be solved in time g(k)nf(k) on graphs of cliquewidth k. Fomin et al. [Intractability of Clique-Width Parameterizations. SIAM J. Comput. 39(5): 1941–1956 (2010)] showed that the running times cannot be improved to g(k)nO(1) assuming W[1]≠FPT. However, this does not rule out nontrivial improvements to the exponent f(k) in the running times. In a follow-up paper, Fomin et al. [Almost Optimal Lower Bounds for Problems Parameterized by Clique-Width. SIAM J. Comput. 43(5): 1541–1563 (2014)] improved the running times for EDS and MC to nO(k), and proved g(k)no(k) lower bounds for EDS, MC and HP assuming the ETH. Recently, Bergougnoux, Kante and Kwon [WADS 2017] gave an nO(k)-time algorithm for HP. Thus, prior to this work, EDS, MC and HP were known to have tight nΘ(k) algorithmic upper and lower bounds. In contrast, GC has an upper bound of nO(2k) and a lower bound of merely (implicit from the W[1]-hardness proof). In this paper, we close the gap for GC by proving a lower bound of n2o(k) This shows that GC behaves qualitatively different from the other three problems. To the best of our knowledge, GC is the first natural problem known to require exponential dependence on the parameter in the exponent of n.

We consider four well-studied NP-complete packing/covering problems on graphs: Feedback Vertex Set in Tournaments (FVST), Cluster Vertex Deletion (CVD), Triangle Packing in Tournaments (TPT) and Induced P3-Packing. For these four problems kernels with O(k2) vertices have been known for a long time. In fact, such kernels can be obtained by interpreting these problems as finding either a packing of k pairwise disjoint sets of size 3 (3-Set Packing) or a hitting set of size at most k for a family of sets of size at most 3 (3-Hitting Set). In this paper, we give the first kernels for FVST, CVD, TPT and Induced P3-Packing with a subquadratic number of vertices. Specifically, we obtain the following results. • FVST admits a kernel with vertices. • CVD admits a kernel with vertices. • TPT admits a kernel with vertices. • Induced P3-Packing admits a kernel with vertices. Our results resolve an open problem from WorKer 2010 on the existence of kernels with

We settle the complexity of the Independent Set Reconfiguration problem on bipartite graphs under all three commonly studied reconfiguration models. We show that under the token jumping or token addition/removal model the problem is NP-complete. For the token sliding model, we show that the problem remains PSPACE-complete.

We investigate the algorithmic properties of circuits of bounded treewidth. Here the treewidth of a circuit C is defined as the treewidth of the underlying undirected graph of C, after the vertices corresponding to input gates have been removed. Thus, boolean formulae correspond to circuits of treewidth 1. Our first main result is an algorithm for counting the number of satisfying assignments of circuits with n input gates, treewidth ω, and at most s · n gates. The running time of our algorithm is , which for formulae instantiates to 2n(1–1/O(s)). This is the first algorithm to achieve exponential speedup over brute force for the satisfiability of linear size circuits with treewidth bounded by a constant greater than 1. For treewidth 1, i.e., boolean formulae, our algorithm significantly outperforms the previously fastest 2n(1-1/O(s2)) time satisfiability algorithm by Santhanam [32]. Our second main result is an algorithm for True Quantified Boolean Circuit Satisfiability for circuits of treewidth ω, in which every input gate has fanout at most s. The running time of our algorithm is . Our algorithm is the first to achieve exponential speed-up over brute force for such circuits. Indeed, even for quantified boolean formulae where every variable appears at most s times, the previously best known algorithm by Santhanam [32] has running time 2n(1–1/O(f(s)log n)). Utilizing the structural properties of low treewidth circuits which helped us obtain improved exponential-time algorithms for satisfiability, we also show that the number of wires of any constant treewidth circuit that computes the majority function must be super-linear.

We present two new combinatorial tools for the design of parameterized algorithms. The first is a simple linear time randomized algorithm that given as input a d-degenerate graph G and an integer k, outputs an independent set Y, such that for every independent set X in G of size at most k, the probability that X is a subset of Y is at least . The second is a new (deterministic) polynomial time graph sparsification procedure that given a graph G, a set T = {{s1, t1}, {s2, t2}, …, {sℓ, tℓ}} of terminal pairs and an integer k, returns an induced subgraph G* of G that maintains all the inclusion minimal multicuts of G of size at most k, and does not contain any (k + 2)-vertex connected set of size 2O(k). In particular, G* excludes a clique of size 2O(k) as a topological minor. Put together, our new tools yield new randomized fixed parameter tractable (FPT) algorithms for Stable s-t Separator, Stable Odd Cycle Transversal and Stable Multicut on general graphs, and for Stable Directed Feedback Vertex Set on d-degenerate graphs, resolving two problems left open by Marx et al. [ACM Transactions on Algorithms, 2013]. All of our algorithms can be derandomized at the cost of a small overhead in the running time.

Planarity, bipartiteness and (directed) acyclicity are basic graph properties with classic linear time recognition algorithms. However, the problems of testing whether a given (di)graph has k vertices whose deletion makes it planar, bipartite or a directed acyclic graph (DAG) are all fundamental NP-complete problems when k is part of the input. As a result, a significant amount of research has been devoted to understanding whether, for every fixed k, these problems admit a polynomial time algorithm (where the exponent in the polynomial is independent of k) and in particular, whether they admit linear time algorithms. While we now know that for any fixed k, we can test in linear time whether a graph is k vertices away from being planar [FOCS 2009, SODA 2014] or bipartite [SODA 2014, SICOMP 2016], the best known algorithms in the case of directed acyclicity are the algorithm of Garey and Tarjan [IPL 78] which runs in time O(nk–1m) and the algorithm of Chen, Liu, Lu, O'Sullivan and Razgon [JACM 2008] which runs in time O(k!4kk4nm). In other words, it has remained open whether it is possible to recognize in linear time, a graph which is 2 vertices away from being acyclic! In this paper, we settle this question by giving an algorithm that decides whether a given graph is k vertices away from being acyclic, in time O(k!4kk5(n + m)). That is, for every fixed k, our algorithm runs in time O(m + n), thus mirroring the case for planarity and bipartiteness. Our algorithm is designed via a general methodology that shaves off a factor of n from some algorithms that use the powerful technique of iterative compression. The two main features of our methodology are: (i) This is the first generic technique for designing linear time algorithms for directed cut-problems and (ii) it can be used in combination with future improvements in algorithms for the compression version of other well-studied cut-problems such as Multicut and Directed Subset Feedback Vertex Set.

A classical result by Erdos and Posa states that there is a function $f: {\mathbb N} \rightarrow {\mathbb N}$ such that for every $k$, every graph $G$ contains $k$ pairwise vertex disjoint cycles or a set $T$ of at most $f(k)$ vertices such that $G-T$ is acyclic. The generalisation of this result to directed graphs is known as Younger's conjecture and was proved by Reed, Robertson, Seymour and Thomas in 1996. This so-called Erdos-Posa-property can naturally be generalised to arbitrary graphs and digraphs. Robertson and Seymour proved that a graph $H$ has the Erdos-Posa-property if, and only if, $H$ is planar. In this paper we study the corresponding problem for digraphs. We obtain a complete characterisation of the class of strongly connected digraphs which have the Erdos-Posa-property (both for topological and butterfly minors). We also generalise this result to classes of digraphs which are not strongly connected. In particular, we study the class of vertex-cyclic digraphs (digraphs without trivial strong components). For this natural class of digraphs we obtain a nearly complete characterisation of the digraphs within this class with the Erdos-Posa-property. In particular we give positive and algorithmic examples of digraphs with the Erdos-Posa-property by using directed tree decompositions in a novel way.

Two of the most widely used approaches to obtain polynomial-time approximation schemes (PTASs) on planar graphs are the Lipton-Tarjan separator-based approach and Baker’s approach. In 2005, Demaine and Hajiaghayi strengthened both approaches using bidimensionality and obtained efficient polynomial-time approximation schemes (EPTASs) for several problems, including C onnected D ominating S et and F eedback V ertex S et . In this work, we unify the two strengthened approaches to combine the best of both worlds. We develop a framework allowing the design of EPTAS on classes of graphs with the subquadratic grid minor (SQGM) property. Roughly speaking, a class of graphs has the SQGM property if, for every graph G from the class, the fact that G contains no t × t grid as a minor guarantees that the treewidth of G is subquadratic in t . For example, the class of planar graphs and, more generally, classes of graphs excluding some fixed graph as a minor, have the SQGM property. At the heart of our framework is a decomposition lemma stating that for “most” bidimensional problems on a graph class G with the SQGM property, there is a polynomial-time algorithm that, given a graph G ϵ G as input and an ϵ > 0, outputs a vertex set X of size ϵ ċ OPT such that the treewidth of G - X is f (ϵ). Here, OPT is the objective function value of the problem in question and f is a function depending only on ϵ. This allows us to obtain EPTASs on (apex)-minor-free graphs for all problems covered by the previous framework as well as for a wide range of packing problems, partial covering problems and problems that are neither closed under taking minors nor contractions. To the best of our knowledge, for many of these problems—including C ycle P acking , F -P acking , F -D eletion , M ax L eaf S panning T ree , or P artial r -D ominating S et —no EPTASs, even on planar graphs, were previously known. We also prove novel excluded grid theorems in unit disk and map graphs without large cliques. Using these theorems, we show that these classes of graphs have the SQGM property. Based on the developed framework, we design EPTASs and subexponential time parameterized algorithms for various classes of problems on unit disk and map graphs.

A central problem in parameterized algorithms is to obtain algorithms with running time $f(k)\cdot n^{O(1)}$ such that $f$ is as slow growing a function of the parameter $k$ as possible. In particular, a large number of basic parameterized problems admit parameterized algorithms where $f(k)$ is single-exponential, that is, $c^k$ for some constant $c$, which makes aiming for such a running time a natural goal for other problems as well. However, there are still plenty of problems where the $f(k)$ appearing in the best-known running time is worse than single-exponential and it remained “slightly superexponential” even after serious attempts to bring it down. A natural question to ask is whether the $f(k)$ appearing in the running time of the best-known algorithms is optimal for any of these problems. In this paper, we examine parameterized problems where $f(k)$ is $k^{O(k)}=2^{O(k\log k)}$ in the best-known running time, and for a number of such problems we show that the dependence on $k$ in the running time cannot be improved to single-exponential. More precisely we prove the following tight lower bounds, for four natural problems, arising from three different domains: (1) In the Closest String problem, given strings $s_1$, $\dots$, $s_t$ over an alphabet $\Sigma$ of length $L$ each, and an integer $d$, the question is whether there exists a string $s$ over $\Sigma$ of length $L$, such that its hamming distance from each of the strings $s_i$, $1\leq i \leq t$, is at most $d$. The pattern matching problem Closest String is known to be solvable in times $2^{O(d\log d)}\cdot n^{O(1)}$ and $2^{O(d\log |\Sigma|)}\cdot n^{O(1)}$. We show that there are no $2^{o(d\log d)}\cdot n^{O(1)}$ or $2^{o(d\log |\Sigma|)}\cdot n^{O(1)}$ time algorithms, unless the Exponential Time Hypothesis (ETH) fails. (2) The graph embedding problem Distortion, that is, deciding whether a graph $G$ has a metric embedding into the integers with distortion at most $d$ can be solved in time $2^{O(d\log d)}\cdot n^{O(1)}$. We show that there is no $2^{o(d\log d)}\cdot n^{O(1)}$ time algorithm, unless the ETH fails. (3) The Disjoint Paths problem can be solved in time $2^{O(w\log w)}\cdot n^{O(1)}$ on graphs of treewidth at most $w$. We show that there is no $2^{o(w\log w)}\cdot n^{O(1)}$ time algorithm, unless the ETH fails. (4) The Chromatic Number problem can be solved in time $2^{O(w\log w)}\cdot n^{O(1)}$ on graphs of treewidth at most $w$. We show that there is no $2^{o(w\log w)}\cdot n^{O(1)}$ time algorithm, unless the ETH fails. To obtain our results, we first prove the lower bound for variants of basic problems: finding cliques, independent sets, and hitting sets. These artificially constrained variants form a good starting point for proving lower bounds on natural problems without any technical restrictions and could be of independent interest. Several follow-up works have already obtained tight lower bounds by using our framework, and we believe it will prove useful in obtaining even more lower bounds in the future.

A key result in the field of kernelization, a subfield of parameterized complexity, states that the classic Disjoint Cycle Packing problem, i.e., finding $k$ vertex disjoint cycles in a given graph $G$, admits no polynomial kernel unless ${\sf NP} \subseteq {\sf coNP} / {\sf poly}$. However, very little is known about this problem beyond the aforementioned kernelization lower bound (within the parameterized complexity framework). In the hope of clarifying the picture and better understanding the types of constraints that separate kernelizable from nonkernelizable variants of Disjoint Cycle Packing, we investigate two relaxations of the problem. The first variant, which we call Almost Disjoint Cycle Packing, introduces a global relaxation parameter $t$. That is, given a graph $G$ and integers $k$ and $t$, the goal is to find at least $k$ distinct cycles such that every vertex of $G$ appears in at most $t$ of the cycles. The second variant, Pairwise Disjoint Cycle Packing, introduces a local relaxation parameter, and we seek at least $k$ distinct cycles such that every two cycles intersect in at most $t$ vertices. While the Pairwise Disjoint Cycle Packing problem admits a polynomial kernel for all $t \geq 1$, the kernelization complexity of Almost Disjoint Cycle Packing reveals an interesting spectrum of upper and lower bounds. In particular, for $t = \frac{k}{c}$, where $c$ could be a function of $k$, we obtain a kernel of size $\mathcal{O}(2^{c^2}k^{7 + c}\log^3 k)$ whenever $c\in o(\sqrt k)$. Thus the kernel size varies from being subexponential when $c\in o(\sqrt k)$, to quasi-polynomial when $c\in o(\log^{\ell} k)$, $\ell \in \mathbb{R}_+$, and polynomial when $c\in \mathcal{O}(1)$. We complement these results for Almost Disjoint Cycle Packing by showing that the problem does not admit a polynomial kernel whenever $t \in \mathcal{O}(k^{\epsilon})$ for any $0 \leq \epsilon < 1$, unless ${\sf NP} \subseteq {\sf coNP} / {\sf poly}$.

Seymour's decomposition theorem for regular matroids is a fundamental result with a number of combinatorial and algorithmic applications. In this work we demonstrate how this theorem can be used in the design of parameterized algorithms on regular matroids. We consider the problem of covering a set of vectors of a given finite dimensional linear space (vector space) by a subspace generated by a set of vectors of minimum size. Specifically, in the Space Cover problem, we are given a matrix $M$ and a subset of its columns $T$; the task is to find a minimum set $F$ of columns of $M$ disjoint with $T$ such that the linear span of $F$ contains all vectors of $T$. For graphic matroids this problem is essentially Steiner Forest and for cographic matroids this is a generalization of Multiway Cut. Our main result is the algorithm with running time $2^{\mathcal{O}h(k)}\cdot ||M|| ^{\mathcal{O}h(1)}$ solving Space Cover in the case when $M$ is a totally unimodular matrix over rationals, where $k$ is the size of $F$. In other words, we show that on regular matroids the problem is fixed-parameter tractable parameterized by the rank of the covering subspace.

For a target rank $r$, the rigidity of a matrix $A$ over a field $\mathbb{F}$ is the minimum Hamming distance between $A$ and a matrix of rank at most $r$. Rigidity is a classical concept in computational complexity theory: constructions of rigid matrices are known to imply lower bounds of significant importance relating to arithmetic circuits. Yet, from the viewpoint of parameterized complexity, the study of central properties of matrices in general, and of the rigidity of a matrix in particular, has been neglected. In this paper, we conduct a comprehensive study of different aspects of the computation of the rigidity of general matrices in the framework of parameterized complexity. Naturally, given parameters $r$ and $k$, the Matrix Rigidity problem asks whether the rigidity of $A$ for the target rank $r$ is at most $k$. We show that in the case $\mathbb{F}=\mathbb{R}$ or $\mathbb{F}$ is any finite field, this problem is fixed-parameter tractable with respect to $k+r$. To this end, we present a dimension reduction procedure, which may be a valuable primitive in future studies of problems of this nature. We also employ central tools in real algebraic geometry, which are not well known in parameterized complexity, as a black box. In particular, we view the output of our dimension reduction procedure as an algebraic variety. Our main results are complemented by a \sf W[1]-hardness result and a subexponential-time parameterized algorithm for a special case of Matrix Rigidity, highlighting the different flavors of this problem.

A vertex subset $S$ in a graph $G$ is a dominating set if every vertex not contained in $S$ has a neighbor in $S$. A dominating set $S$ is a connected dominating set if the subgraph $G[S]$ induced by $S$ is connected. A connected dominating set $S$ is a minimal connected dominating set if no proper subset of $S$ is also a connected dominating set. We prove that there exists a constant $\epsilon > 10^{-50}$ such that every graph $G$ on $n$ vertices has at most $\mathcal{O}(2^{(1-\epsilon)n})$ minimal connected dominating sets. For the same $\epsilon$ we also give an algorithm with running time $2^{(1-\epsilon)n}\cdot n^{\mathcal{O}(1)}$ to enumerate all minimal connected dominating sets in an input graph $G$.

We investigate the complexity of several fundamental polynomial-time solvable problems on graphs and on matrices, when the given instance has low treewidth; in the case of matrices, we consider the treewidth of the graph formed by non-zero entries. In each of the considered cases, the best known algorithms working on general graphs run in polynomial time; however, the exponent of the polynomial is large. Therefore, our main goal is to construct algorithms with running time of the form poly( k )⋅ n or poly( k )⋅ n log n , where k is the width of the tree decomposition given on the input. Such procedures would outperform the best known algorithms for the considered problems already for moderate values of the treewidth, like O ( n 1/ c ) for a constant c . Our results include the following: — an algorithm for computing the determinant and the rank of an n × n matrix using O ( k 3 ⋅ n ) time and arithmetic operations; —an algorithm for solving a system of linear equations using O ( k 3 ⋅ n ) time and arithmetic operations; —an O ( k 3 ⋅ n log n )-time randomized algorithm for finding the cardinality of a maximum matching in a graph; —an O ( k 4 ⋅ n log 2 n )-time randomized algorithm for constructing a maximum matching in a graph; —an O ( k 2 ⋅ n log n )-time algorithm for finding a maximum vertex flow in a directed graph. Moreover, we give an approximation algorithm for treewidth with time complexity suited to the running times as above. Namely, the algorithm, when given a graph G and integer k , runs in time O ( k 7 ⋅ n log n ) and either correctly reports that the treewidth of G is larger than k , or constructs a tree decomposition of G of width O ( k 2 ). The above results stand in contrast with the recent work of Abboud et al. (SODA 2016), which shows that the existence of algorithms with similar running times is unlikely for the problems of finding the diameter and the radius of a graph of low treewidth.

We give the first linear kernels for the D ominating S et and C onnected D ominating S et problems on graphs excluding a fixed graph H as a topological minor. In other words, we prove the existence of polynomial time algorithms that, for a given H -topological-minor-free graph G and a positive integer k , output an H -topological-minor-free graph G ′ on O ( k ) vertices such that G has a (connected) dominating set of size k if and only if G ′ has one. Our results extend the known classes of graphs on which the D ominating S et and C onnected D ominating S et problems admit linear kernels. Prior to our work, it was known that these problems admit linear kernels on graphs excluding a fixed apex graph H as a minor. Moreover, for D ominating S et , a kernel of size k c ( H ) , where c ( H ) is a constant depending on the size of H , follows from a more general result on the kernelization of D ominating S et on graphs of bounded degeneracy. Alon and Gutner explicitly asked whether one can obtain a linear kernel for D ominating S et on H -minor-free graphs. We answer this question in the affirmative and in fact prove a more general result. For C onnected D ominating S et no polynomial kernel even on H -minor-free graphs was known prior to our work. On the negative side, it is known that C onnected D ominating S et on 2-degenerated graphs does not admit a polynomial kernel unless coNP ⊆ NP/poly. Our kernelization algorithm is based on a non-trivial combination of the following ingredients • The structural theorem of Grohe and Marx [STOC 2012] for graphs excluding a fixed graph H as a topological minor; • A novel notion of protrusions, different than the one defined in [FOCS 2009]; • Our results are based on a generic reduction rule that produces an equivalent instance (in case the input graph is H -minor-free) of the problem, with treewidth O (√ k ). The application of this rule in a divide-and-conquer fashion, together with the new notion of protrusions, gives us the linear kernels. A protrusion in a graph [FOCS 2009] is a subgraph of constant treewidth which is separated from the rest of the graph by at most a constant number of vertices. In our variant of protrusions, instead of stipulating that the subgraph be of constant treewidth , we ask that it contains a constant number of vertices from a solution . We believe that this new take on protrusions would be useful for other graph problems and in different algorithmic settings.

We obtain a number of lower bounds on the running time of algorithms solving problems on graphs of bounded treewidth. We prove the results under the Strong Exponential Time Hypothesis of Impagliazzo and Paturi. In particular, assuming that n -variable m -clause SAT cannot be solved in time (2-ϵ) n m O (1) , we show that for any ϵ > 0: • I ndependent S et cannot be solved in time (2-ϵ) tw( G ) | V ( G )| O (1) , • D ominating S et cannot be solved in time (3-ϵ) tw( G ) | V ( G )| O (1) , • M ax C ut cannot be solved in time (2-ϵ) tw( G ) | V ( G )| O (1) , • O dd C ycle T ransversal cannot be solved in time (3-ϵ) tw( G ) | V ( G )| O (1) , • For any fixed q ≥ 3, q -C oloring cannot be solved in time ( q -ϵ) tw ( G ) | V ( G )| O (1) , • P artition I nto T riangles cannot be solved in time (2-ϵ) tw ( G ) | V ( G )| O (1) . Our lower bounds match the running times for the best known algorithms for the problems, up to the ϵ in the base.

In the M aximum W eight I ndependent S et problem, the input is a graph G , every vertex has a non-negative integer weight, and the task is to find a set S of pairwise nonadjacent vertices, maximizing the total weight of the vertices in S . We give an n O (log 2 n ) time algorithm for this problem on graphs excluding the path P 6 on 6 vertices as an induced subgraph. Currently, there is no constant k known for which M aximum W eight I ndependent S et on P k -free graphs becomes NP-hard, and our result implies that if such a k exists, then k > 6 unless all problems in NP can be decided in quasi-polynomial time. Using the combinatorial tools that we develop for this algorithm, we also give a polynomial-time algorithm for M aximum W eight E fficient D ominating S et on P 6 -free graphs. In this problem, the input is a graph G , every vertex has an integer weight, and the objective is to find a set S of maximum weight such that every vertex in G has exactly one vertex in S in its closed neighborhood or to determine that no such set exists. Prior to our work, the class of P 6 -free graphs was the only class of graphs defined by a single forbidden induced subgraph on which the computational complexity of M aximum W eight E fficient D ominating S et was unknown.

In the S ubset F eedback V ertex S et (S ubset FVS) problem, the input is a graph G on n vertices and m edges, a subset of vertices T , referred to as terminals, and an integer k . The objective is to determine whether there exists a set of at most k vertices intersecting every cycle that contains a terminal. The study of parameterized algorithms for this generalization of the F eedback V ertex S et problem has received significant attention over the past few years. In fact, the parameterized complexity of this problem was open until 2011, when two groups independently showed that the problem is fixed parameter tractable. Using tools from graph minors,, Kawarabayashi and Kobayashi obtained an algorithm for S ubset FVS running in time O ( f ( k )ċ n 2 m ) [SODA 2012, JCTB 2012]. Independently, Cygan et al. [ICALP 2011, SIDMA 2013] designed an algorithm for S ubset FVS running in time 2 O ( k log k ) ċ n O (1) . More recently, Wahlström obtained the first single exponential time algorithm for S ubset FVS, running in time 4 k ċ n O (1) [SODA 2014]. While the 2 O ( k ) dependence on the parameter k is optimal under the Exponential Time Hypothesis, the dependence of this algorithm as well as those preceding it, on the input size is at least quadratic. In this article, we design the first linear time parameterized algorithms for S ubset FVS. More precisely, we obtain the following new algorithms for S ubset FVS. — A randomized algorithm for S ubset FVS running in time O (25.6 k ċ ( n + m )). — A deterministic algorithm for S ubset FVS running in time 2 O ( k log k ) ċ ( n + m ). Since it is known that assuming the Exponential Time Hypothesis, S ubset FVS cannot have an algorithm running in time 2 o ( k ) n O (1) , our first algorithm obtains the best possible asymptotic dependence on both the parameter as well as the input size. Both of our algorithms are based on “cut centrality,” in the sense that solution vertices are likely to show up in minimum size cuts between vertices sampled from carefully chosen distributions.

The optimization version of the Unique Label Cover problem is at the heart of the Unique Games Conjecture which has played an important role in the proof of several tight inapproximability results. In recent years, this problem has been also studied extensively from the point of view of parameterized complexity. Cygan et al. [FOCS 2012] proved that this problem is fixed-parameter tractable (FPT) and Wahlström [SODA 2014] gave an FPT algorithm with an improved parameter dependence. Subsequently, Iwata, Wahlström and Yoshida [2014] proved that the edge version of Unique Label Cover can be solved in linear FPT-time. That is, there is an FPT algorithm whose dependence on the input-size is linear. However, such an algorithm for the node version of the problem was left as an open problem. In this paper, we resolve this question by presenting the first linear-time FPT algorithm for Node Unique Label Cover.

For a family of graphs F, an n-vertex graph G, and a positive integer k, the F-Deletion problem asks whether we can delete at most k vertices from G to obtain a graph in F. F-Deletion generalizes many classical graph problems such as Vertex Cover, Feedback Vertex Set, and Odd Cycle Transversal. A (multi) graph G = (V, \cup_{i=1}^{\alpha} E_{i}), where the edge set of G is partitioned into \alpha color classes, is called an \alpha-edge-colored graph. A natural extension of the F-Deletion problem to edge-colored graphs is the Simultaneous (F_1, \ldots, F_\alpha)-Deletion problem. In the latter problem, we are given an \alpha-edge-colored graph G and the goal is to find a set S of at most k vertices such that each graph G_i - S, where G_i = (V, E_i) and 1 \leq i \leq \alpha, is in F_i. Recently, a subset of the authors considered the aforementioned problem with F_1 = \ldots = F_\alpha being the family of all forests. They showed that the problem is fixed-parameter tractable when parameterized by k and \alpha, and can be solved in O(2^{O(\alpha k)}n^{O(1)}) time. In this work, we initiate the investigation of the complexity of Simultaneous (F_1, \ldots, F_\alpha)-Deletion with different families of graphs. In the process, we obtain a complete characterization of the parameterized complexity of this problem when one or more of the F_i's is the class of bipartite graphs and the rest (if any) are forests. We show that if F_1 is the family of all bipartite graphs and each of F_2 = F_3 = \ldots = F_\alpha is the family of all forests then the problem is fixed-parameter tractable parameterized by k and \alpha. However, even when F_1 and F_2 are both the family of all bipartite graphs, then the Simultaneous (F_1, F_2)-Deletion} problem itself is already W[1]-hard.

The family of judicious partitioning problems, introduced by Bollob\'as and Scott to the field of extremal combinatorics, has been extensively studied from a structural point of view for over two decades. This rich realm of problems aims to counterbalance the objectives of classical partitioning problems such as Min Cut, Min Bisection and Max Cut. While these classical problems focus solely on the minimization/maximization of the number of edges crossing the cut, judicious (bi)partitioning problems ask the natural question of the minimization/maximization of the number of edges lying in the (two) sides of the cut. In particular, Judicious Bipartition (JB) seeks a bipartition that is "judicious" in the sense that neither side is burdened by too many edges, and Balanced JB also requires that the sizes of the sides themselves are "balanced" in the sense that neither of them is too large. Both of these problems were defined in the work by Bollob\'as and Scott, and have received notable scientific attention since then. In this paper, we shed light on the study of judicious partitioning problems from the viewpoint of algorithm design. Specifically, we prove that BJB is FPT (which also proves that JB is FPT).

The Cycle Packing problem asks whether a given undirected graph G=(V,E) contains k vertex-disjoint cycles. Since the publication of the classic Erdos-Posa theorem in 1965, this problem received significant scientific attention in the fields of Graph Theory and Algorithm Design. In particular, this problem is one of the first problems studied in the framework of Parameterized Complexity. The non-uniform fixed-parameter tractability of Cycle Packing follows from the Robertson–Seymour theorem, a fact already observed by Fellows and Langston in the 1980s. In 1994, Bodlaender showed that Cycle Packing can be solved in time 2^{O(k^2)}|V| using exponential space. In case a solution exists, Bodlaender's algorithm also outputs a solution (in the same time). It has later become common knowledge that Cycle Packing admits a 2^{O(k\log^2 k)}|V|-time (deterministic) algorithm using exponential space, which is a consequence of the Erdos-Posa theorem. Nowadays, the design of this algorithm is given as an exercise in textbooks on Parameterized Complexity. Yet, no algorithm that runs in time 2^{o(k\log^2k)}|V|^{O(1)}, beating the bound 2^{O(k\log^2k)}\cdot |V|^{O(1)}, has been found. In light of this, it seems natural to ask whether the 2^{O(k\log^2k)}|V|^{O(1)}$ bound is essentially optimal. In this paper, we answer this question negatively by developing a 2^{O(k\log^2k/log log k})} |V|-time (deterministic) algorithm for Cycle Packing. In case a solution exists, our algorithm also outputs a solution (in the same time). Moreover, apart from beating the known bound, our algorithm runs in time linear in |V|, and its space complexity is polynomial in the input size.

Given a graph G and a parameter k, the Chordal Vertex Deletion (CVD) problem asks whether there exists a subset U ⊆ V (G) of size at most k that hits all induced cycles of size at least 4. The existence of a polynomial kernel for CVD was a well-known open problem in the field of Parameterized Complexity. Recently, Jansen and Pilipczuk resolved this question affirmatively by designing a polynomial kernel for CVD of size O(k161 log58 k), and asked whether one can design a kernel of size O(k10). While we do not completely resolve this question, we design a significantly smaller kernel of size O(k25 log14 k), inspired by the O(k2)-size kernel for Feedback Vertex Set. To obtain this result, we first design an O(opt-log2 n)-factor approximation algorithm for CVD, which is central to our kernelization procedure. Thus, we improve upon both the kernelization algorithm and the approximation algorithm of Jansen and Pilipczuk. Next, we introduce the notion of the independence degree of a vertex, which is our main conceptual contribution. We believe that this notion could be useful in designing kernels for other problems.

We consider the fundamental Matroid Theory problem of finding a circuit in a matroid spanning a set t of given terminal elements. For graphic matroids this corresponds to the problem of finding a simple cycle passing through a set of given terminal edges in a graph. The algorithmic study of the problem on regular matroids, a superclass of graphic matroids, was initiated by Gavenčiak, Král', and Oum [ICALP'12], who proved that the case of the problem with |T| = 2 is fixed-parameter tractable (FPT) when parameterized by the length of the circuit. We extend the result of Gavenciak, Král', and Oum by showing that for regular matroids the Minimum Spanning Circuit problem, deciding whether there is a circuit with at most ℓ elements containing T, is FPT parameterized by k = ℓ — |T|; the Spanning Circuit problem, deciding whether there is a circuit containing T, is FPT parameterized by |T|. We note that extending our algorithmic findings to binary matroids, a superclass of regular matroids, is highly unlikely: Minimum Spanning Circuit parameterized by ℓ is W[1]- hard on binary matroids even when |T| = 1. We also show a limit to how far our results can be strengthened by considering a smaller parameter. More precisely, we prove that Minimum Spanning Circuit parameterized by |T| is W[1]-hard even on cographic matroids, a proper subclass of regular matroids.

We investigate the complexity of several fundamental polynomial-time solvable problems on graphs and on matrices, when the given instance has low treewidth; in the case of matrices, we consider the treewidth of the graph formed by non-zero entries. In each of the considered cases, the best known algorithms working on general graphs run in polynomial, but far from linear, time. Thus, our goal is to construct algorithms with running time of the form poly(k) · n or poly(k) · n log n, where k is the width of the tree decomposition given on the input. Such procedures would outperform the best known algorithms for the considered problems already for moderate values of the treewidth, like O(n1/c) for some small constant c. Our results include: an algorithm for computing the determinant and the rank of an n × n matrix using O(k3 · n) time and arithmetic operations;an algorithm for solving a system of linear equations using O(k3 · n) time and arithmetic operations;an O(k3 · n log n)-time randomized algorithm for finding the cardinality of a maximum matching in a graph;an O(k4 · nlog2 n)-time randomized algorithm for constructing a maximum matching in a graph;an O(k2 · n log n)-time algorithm for finding a maximum vertex flow in a directed graph. Moreover, we provide an approximation algorithm for treewidth with time complexity suited to the running times as above. Namely, the algorithm, when given a graph G and integer k, runs in time O(k2 · n log n) and either correctly reports that the treewidth of G is larger than k, or constructs a tree decomposition of G of width O(k2). The above results stand in contrast with the recent work of Abboud et al. [SODA 2016], which shows that the existence of algorithms with similar running times is unlikely for the problems of finding the diameter and the radius of a graph of low treewidth.

We consider the problem of solving systems of multivariate polynomial equations of degree k over a finite field. For every integer k ≤ 2 and finite field q where q = pd for a prime p, we give, to the best of our knowledge, the first algorithms that achieve an exponential speedup over the brute force O(qn) time algorithm in the worst case. We present two algorithms, a randomized algorithm with running time qn+o(n) · q−n/O(k) time if q < 24ekd, and otherwise, where e = 2.718… is Napier's constant, and a deterministic algorithm for counting solutions with running time qn+o(n) · q−n/O(kq6/7d). For the important special case of quadratic equations in F2, our randomized algorithm has running time O(20.8765n). For systems over 2 we also consider the case where the input polynomials do not have bounded degree, but instead can be efficiently represented as a ΣΠΣ circuit, i.e., a sum of products of sums of variables. For this case we present a deterministic algorithm running in time 2n-dn for δ = 1/O(log(s/n)) for instances with s product gates in total and n variables. Our algorithms adapt several techniques recently developed via the polynomial method from circuit complexity. The algorithm for systems of ΣΠΣ polynomials also introduces a new degree reduction method that takes an instance of the problem and outputs a subexponential-sized set of instances, in such a way that feasibility is preserved and every polynomial among the output instances has degree O(log(s/n)).

The edit operation that contracts edges, which is a fundamental operation in the theory of graph minors, has recently gained substantial scientific attention from the viewpoint of Parameterized Complexity. In this paper, we examine an important family of graphs, namely the family of split graphs, which in the context of edge contractions, is proven to be significantly less obedient than one might expect. Formally, given a graph G and an integer k, the Split Contraction problem asks whether there exists a subset X of edges of G such that G/X is a split graph and X has at most k elements. Here, G/X is the graph obtained from G by contracting edges in X. It was previously claimed that the Split Contraction problem is fixed-parameter tractable. However, we show that, despite its deceptive simplicity, it is W[1]-hard. Our main result establishes the following conditional lower bound: under the Exponential Time Hypothesis, the Split Contraction problem cannot be solved in time 2^(o(l^2)) * poly(n) where l is the vertex cover number of the input graph. We also verify that this lower bound is essentially tight. To the best of our knowledge, this is the first tight lower bound of the form 2^(o(l^2)) * poly(n) for problems parameterized by the vertex cover number of the input graph. In particular, our approach to obtain this lower bound borrows the notion of harmonious coloring from Graph Theory, and might be of independent interest.

The rigidity of a matrix A for a target rank r over a field F is the minimum Hamming distance between A and a matrix of rank at most r. Rigidity is a classical concept in Computational Complexity Theory: constructions of rigid matrices are known to imply lower bounds of significant importance relating to arithmetic circuits. Yet, from the viewpoint of Parameterized Complexity, the study of central properties of matrices in general, and of the rigidity of a matrix in particular, has been neglected. In this paper, we conduct a comprehensive study of different aspects of the computation of the rigidity of general matrices in the framework of Parameterized Complexity. Naturally, given parameters r and k, the Matrix Rigidity problem asks whether the rigidity of A for the target rank r is at most k. We show that in case F equals the reals or F is any finite field, this problem is fixed-parameter tractable with respect to k+r. To this end, we present a dimension reduction procedure, which may be a valuable primitive in future studies of problems of this nature. We also employ central tools in Real Algebraic Geometry, which are not well known in Parameterized Complexity, as a black box. In particular, we view the output of our dimension reduction procedure as an algebraic variety. Our main results are complemented by a W[1]-hardness result and a subexponential-time parameterized algorithm for a special case of Matrix Rigidity, highlighting the different flavors of this problem.

In this paper we propose a new framework for analyzing the performance of preprocessing algorithms. Our framework builds on the notion of kernelization from parameterized complexity. However, as opposed to the original notion of kernelization, our definitions com- bine well with approximation algorithms and heuristics. The key new definition is that of a polynomial size α-approximate kernel. Loosely speaking, a polynomial size α-approximate kernel is a polynomial time pre-processing algorithm that takes as input an instance (I, k) to a parameterized problem, and outputs another instance (I′,k′) to the same problem, such that |I′| + k′ ≤ kO(1). Additionally, for every c ≥ 1, a c-approximate solution s′ to the pre-processed instance (I′, k′) can be turned in polynomial time into a (c · α)-approximate solution s to the original instance (I,k).

We give a fixed-parameter tractable algorithm that, given a parameter $k$ and two graphs $G_1,G_2$, either concludes that one of these graphs has treewidth at least $k$ or determines whether $G_1$ and $G_2$ are isomorphic. The running time of the algorithm on an $n$-vertex graph is $2^{\mathcal{O}(k^5\log k)}\cdot n^5$, and this is the first fixed-parameter algorithm for Graph Isomorphism parameterized by treewidth. Our algorithm in fact solves the more general canonization problem. We namely design a procedure working in $2^{\mathcal{O}(k^5\log k)}\cdot n^5$ time that, for a given graph $G$ on $n$ vertices, either concludes that the treewidth of $G$ is at least $k$ or (i) finds in an isomorphic-invariant way a graph $\mathfrak{c}(G)$ that is isomorphic to $G$; (ii) finds an isomorphism-invariant construction term---an algebraic expression that encodes $G$ together with a tree decomposition of $G$ of width less than $k$. Hence, the isomorphism test reduces to verifying whether the computed isomorphic copies or the construction terms for $G_1$ and $G_2$ are equal.

We study the parameterized complexity of the directed variant of the classical Steiner Tree problem on various classes of directed sparse graphs. While the parameterized complexity of Steiner Tree parameterized by the number of terminals is well understood, not much is known about the parameterization by the number of nonterminals in the solution tree. All that is known for this parameterization is that both the directed and the undirected versions are W[2]-hard on general graphs and hence unlikely to be fixed parameter tractable (FPT). The undirected Steiner Tree problem becomes FPT when restricted to sparse classes of graphs such as planar graphs, but the techniques used to show this result break down on directed planar graphs. In this article we precisely chart the tractability border for Directed Steiner Tree (DST) on sparse graphs parameterized by the number of nonterminals in the solution tree. Specifically, we show that the problem is FPT on graphs excluding a topological minor but becomes W[2]-hard on graphs of degeneracy 2. On the other hand we show that if the subgraph induced by the terminals is acyclic, then the problem becomes FPT on graphs of bounded degeneracy. We further show that our algorithm achieves the best possible asymptotic running time dependence on the solution size and degeneracy of the input graph, under standard complexity theoretic assumptions. Using the ideas developed for DST, we also obtain improved algorithms for Dominating Set on sparse undirected graphs. These algorithms are asymptotically optimal. (An erratum is attached.)

In the Subset Odd Cycle Transversal (Subset OCT) problem, the input is a graph $G$, a subset of vertices $T$ and a positive integer $k$ and the objective is to determine whether there exists a $k$-sized vertex subset that intersects every odd cycle containing a vertex from $T$. Clearly, Subset OCT is a generalization of the classic Odd Cycle Transversal problem where the objective is to determine whether there exists a $k$-sized vertex subset that intersects every odd cycle in the given graph. We remark that Subset OCT also generalizes the well known Multiway Cut problem, as well as a parity constrained variant, the Odd Multiway Cut problem. Recently, Kakimura, Kawarabayashi, and Kobayashi [ Proceedings of SODA, 2012, pp. 1726--1736] proposed a fixed parameter tractable (FPT) algorithm for this problem that runs in time $f(k)mn^3$ using the theory of graph minors, where $f$ is some function, and $n$ and $m$ denote the number of vertices and edges in the graph. However, the dependence of this function on $k$ is at least triple exponential. In this paper, we give the first FPT algorithm for this problem where the exponential dependence of the running time of the algorithm on $k$ is polynomial. Our algorithm avoids the use of the theory of graph minors, is self contained, and runs in time $2^{\mathcal{O}(k^3\log k)}mn^2\log^2 n$, thus improving upon the algorithm of Kakimura and co-authors with respect to both the parameter as well as the input size. Our algorithm utilizes a recursive application of “generalized” important separators to reduce the subset version of this problem to the standard version of the problem.

A subfamily F′ of a set family F is said to q - represent F if for every A ∈ F and B of size q such that A ∩ B = ∅ there exists a set A′ ∈ F′ such that A′ ∩ B = ∅. Recently, we provided an algorithm that, for a given family F of sets of size p together with an integer q , efficiently computes a q -representative family F′ of F of size approximately (p+q p). In this article, we consider the efficient computation of q -representative families for product families F . A family F is a product family if there exist families A and B such that F = { A , ∪, B : A ∈ A , B ∈ B , A , ∩, B = ∅}. Our main technical contribution is an algorithm that, given A , B and q , computes a q -representative family F′ of F . The running time of our algorithm is sublinear in | F | for many choices of A , B , and q that occur naturally in several dynamic programming algorithms. We also give an algorithm for the computation of q -representative families for product families F in the more general setting where q -representation also involves independence in a matroid in addition to disjointness. This algorithm considerably outperforms the naive approach where one first computes F from A and B and then computes the q -representative family F′ from F . We give two applications of our new algorithms for computing q -representative families for product families. The first is a 3.8408 k n O (1) deterministic algorithm for the M ultilinear M onomial D etection ( k -M l D) problem. The second is a significant improvement of deterministic dynamic programming algorithms for “connectivity problems” on graphs of bounded treewidth.

The F -M inor -F ree D eletion problem asks, for a fixed set F and an input consisting of a graph G and integer k , whether k vertices can be removed from G such that the resulting graph does not contain any member of F as a minor. At FOCS 2012, Fomin et al. showed that the special case when F contains at least one planar graph has a kernel of size f ( F ) ċ k g ( F ) for some functions f and g . They left open whether this P lanar F -M inor -F ree D eletion problem has kernels whose size is uniformly polynomial, of the form f ( F ) ċ k c for some universal constant c . We prove that some P lanar F -M inor -F ree D eletion problems do not have uniformly polynomial kernels (unless NP ⊆ coNP/poly), not even when parameterized by the vertex cover number. On the positive side, we consider the problem of determining whether k vertices can be removed to obtain a graph of treedepth at most η. We prove that this problem admits uniformly polynomial kernels with O ( k 6 ) vertices for every fixed η.

A rectilinear Steiner tree for a set T of points in the plane is a tree which connects T using horizontal and vertical lines. In the Rectilinear Steiner Tree problem, input is a set T of n points in the Euclidean plane (R^2) and the goal is to find an rectilinear Steiner tree for T of smallest possible total length. A rectilinear Steiner arborecence for a set T of points and root r in T is a rectilinear Steiner tree S for T such that the path in S from r to any point t in T is a shortest path. In the Rectilinear Steiner Arborescense problem the input is a set T of n points in R^2, and a root r in T, the task is to find an rectilinear Steiner arborescence for T, rooted at r of smallest possible total length. In this paper, we give the first subexponential time algorithms for both problems. Our algorithms are deterministic and run in 2^{O(sqrt{n}log n)} time.

We prove the following theorem. Given a planar graph G and an integer k, it is possible in polynomial time to randomly sample a subset A of vertices of G with the following properties: 1) A induces a subgraph of G of treewidth O(√(k log k)), and 2) for every connected subgraph H of G on at most k vertices, the probability that A covers the whole vertex set of H is at least (2O(√k log2 k)· nO(1))-1, where n is the number of vertices of G. Together with standard dynamic programming techniques for graphs of bounded treewidth, this result gives a versatile technique for obtaining (randomized) subexponential parameterized algorithms for problems on planar graphs, usually with running time bound 2O(√(k log2k))nO(1). The technique can be applied to problems expressible as searching for a small, connected pattern with a prescribed property in a large host graph, examples of such problems include DIRECTED k-Path, WEIGHTED k-Path, VERTEX COVER LOCAL SEARCH, and SUBGRAPH ISOMORPHISM, among others. Up to this point, it was open whether these problems can be solved in subexponential parameterized time on planar graphs, because they are not amenable to the classic technique of bidimensionality. Furthermore, all our results hold in fact on any class of graphs that exclude a fixed apex graph as a minor, in particular on graphs embeddable in any fixed surface.

A bipartite tournament is a directed graph T:=(A cup B, E) such that every pair of vertices (a,b), a in A, b in B are connected by an arc, and no arc connects two vertices of A or two vertices of B. A feedback vertex set is a set S of vertices in T such that T - S is acyclic. In this article we consider the Feedback Vertex Set problem in bipartite tournaments. Here the input is a bipartite tournament T on n vertices together with an integer k, and the task is to determine whether T has a feedback vertex set of size at most k. We give a new algorithm for Feedback Vertex Set in Bipartite Tournaments. The running time of our algorithm is upper-bounded by O(1.6181^k + n^{O(1)}), improving over the previously best known algorithm with running time (2^k)k^{O(1)} + n^{O(1)} [Hsiao, ISAAC 2011]. As a by-product, we also obtain the fastest currently known exact exponential-time algorithm for the problem, with running time O(1.3820^n).

A key result in the field of kernelization, a subfield of parameterized complexity, states that the classic Disjoint Cycle Packing problem, i.e. finding k vertex disjoint cycles in a given graph G, admits no polynomial kernel unless NP subseteq coNP/poly. However, very little is known about this problem beyond the aforementioned kernelization lower bound (within the parameterized complexity framework). In the hope of clarifying the picture and better understanding the types of "constraints" that separate "kernelizable" from "non-kernelizable" variants of Disjoint Cycle Packing, we investigate two relaxations of the problem. The first variant, which we call Almost Disjoint Cycle Packing, introduces a "global" relaxation parameter t. That is, given a graph G and integers k and t, the goal is to find at least k distinct cycles such that every vertex of G appears in at most t of the cycles. The second variant, Pairwise Disjoint Cycle Packing, introduces a "local" relaxation parameter and we seek at least k distinct cycles such that every two cycles intersect in at most t vertices. While the Pairwise Disjoint Cycle Packing problem admits a polynomial kernel for all t >= 1, the kernelization complexity of Almost Disjoint Cycle Packing reveals an interesting spectrum of upper and lower bounds. In particular, for t = k/c, where c could be a function of k, we obtain a kernel of size O(2^{c^{2}}*k^{7+c}*log^3(k)) whenever c in o(sqrt(k))). Thus the kernel size varies from being sub-exponential when c in o(sqrt(k)), to quasipolynomial when c in o(log^l(k)), l in R_+, and polynomial when c in O(1). We complement these results for Almost Disjoint Cycle Packing by showing that the problem does not admit a polynomial kernel whenever t in O(k^{epsilon}), for any 0 <= epsilon < 1.

In the l-Component Order Connectivity problem (l in N), we are given a graph G on n vertices, m edges and a non-negative integer k and asks whether there exists a set of vertices S subseteq V(G) such that |S| <= k and the size of the largest connected component in G-S is at most l. In this paper, we give a kernel for l-Component Order Connectivity with at most 2*l*k vertices that takes n^{O(l)} time for every constant l. On the way to obtaining our kernel, we prove a generalization of the q-Expansion Lemma to weighted graphs. This generalization may be of independent interest.

We study the FO model checking problem of dense graph classes, namely those which are FO-interpretable in some sparse graph classes. Note that if an input dense graph is given together with the corresponding FO interpretation in a sparse graph, one can easily solve the model checking problem using the existing algorithms for sparse graph classes. However, if the assumed interpretation is not given, then the situation is markedly harder.

In the Maximum Weight Independent Set problem, the input is a graph G, every vertex has a nonnegative integer weight, and the task is to find a set S of pairwise non-adjacent vertices, maximizing the total weight of the vertices in S. We give an time algorithm for this problem on graphs excluding the path P6 on 6 vertices as an induced subgraph. Currently, there is no constant k known for which Maximum Weight Independent Set on Pk-free graphs becomes NP-complete, and our result implies that if such a k exists, then k > 6 unless all problems in NP can be decided in (quasi)polynomial time. Using the combinatorial tools that we develop for the above algorithm, we also give a polynomial-time algorithm for Maximum Weight Efficient Dominating Set on P6-free graphs. In this problem, the input is a graph G, every vertex has an integer weight, and the objective is to find a set S of maximum weight such that every vertex in G has exactly one vertex in S in its closed neighborhood, or to determine that no such set exists. Prior to our work, the class of P6-free graphs was the only class of graphs defined by a single forbidden induced subgraph on which the computational complexity of Maximum Weight Efficient Dominating Set was unknown.

For a family of graphs F, a graph G, and a positive integer k, the F-DELETION problem asks whether we can delete at most k vertices from G to obtain a graph in F. F-DELETION generalizes many classical graph problems such as Vertex Cover, Feedback Vertex Set, and Odd Cycle Transversal. A graph G = (V, cup_{i=1}^{alpha} E_{i}), where the edge set of G is partitioned into alpha color classes, is called an alpha-edge-colored graph. A natural extension of the F-DELETION problem to edge-colored graphs is the alpha-SIMULTANEOUS F-DELETION problem. In the latter problem, we are given an alpha-edge-colored graph G and the goal is to find a set S of at most k vertices such that each graph G_i\S, where G_i = (V, E_i) and 1 <= i <= alpha, is in F. In this work, we study alpha-SIMULTANEOUS F-DELETION for F being the family of forests. In other words, we focus on the alpha-SIMULTANEOUS FEEDBACK VERTEX SET (alpha-SIMFVS) problem. Algorithmically, we show that, like its classical counterpart, alpha-SIMFVS parameterized by k is fixed-parameter tractable (FPT) and admits a polynomial kernel, for any fixed constant alpha. In particular, we give an algorithm running in 2^{O(alpha * k)} * n^{O(1)} time and a kernel with O(alpha * k^{3(alpha + 1)}) vertices. The running time of our algorithm implies that alpha-SIMFVS is FPT even when alpha in o(log(n)). We complement this positive result by showing that for alpha in O(log(n)), where n is the number of vertices in the input graph, alpha-SIMFVS becomes W[1]-hard. Our positive results answer one of the open problems posed by Cai and Ye (MFCS 2014).

A tournament is a directed graph T such that every pair of vertices is connected by an arc. A feedback vertex set is a set S of vertices in T such that T\S is acyclic. In this article we consider the FEEDBACK VERTEX SET problem in tournaments. Here the input is a tournament T and an integer k, and the task is to determine whether T has a feedback vertex set of size at most k. We give a new algorithm for FEEDBACK VERTEX SET IN TOURNAMENTS. The running time of our algorithm is upper-bounded by O(1.6181^k + n^{O(1)}) and by O(1.466^n). Thus our algorithm simultaneously improves over the fastest known parameterized algorithm for the problem by Dom et al. running in time O(2^kk^{O(1)} + n^{O(1)}), and the fastest known exact exponential-time algorithm by Gaspers and Mnich with running time O(1.674^n). On the way to proving our main result we prove a strengthening of a special case of a graph partitioning theorem due to Bollobas and Scott. In particular we show that the vertices of any undirected m-edge graph of maximum degree d can be colored white or black in such a way that for each of the two colors, the number of edges with both endpoints of that color is between m/4-d/2 and m/4+d/2.

We give a new general approach for designing exact exponential-time algorithms for subset problems . In a subset problem the input implicitly describes a family of sets over a universe of size n and the task is to determine whether the family contains at least one set. A typical example of a subset problem is W EIGHTED d -SAT. Here, the input is a CNF-formula with clauses of size at most d , and an integer W . The universe is the set of variables and the variables have integer weights. The family contains all the subsets S of variables such that the total weight of the variables in S does not exceed W and setting the variables in S to 1 and the remaining variables to 0 satisfies the formula. Our approach is based on “monotone local search,” where the goal is to extend a partial solution to a solution by adding as few elements as possible. More formally, in the extension problem, we are also given as input a subset X of the universe and an integer k . The task is to determine whether one can add at most k elements to X to obtain a set in the (implicitly defined) family. Our main result is that a c k n O(1) time algorithm for the extension problem immediately yields a randomized algorithm for finding a solution of any size with running time O ((2−1/ c ) n ). In many cases, the extension problem can be reduced to simply finding a solution of size at most k . Furthermore, efficient algorithms for finding small solutions have been extensively studied in the field of parameterized algorithms. Directly applying these algorithms, our theorem yields in one stroke significant improvements over the best known exponential-time algorithms for several well-studied problems, including d -H ITTING S ET , F EEDBACK V ERTEX S ET , N ODE U NIQUE L ABEL C OVER , and W EIGHTED d -SAT. Our results demonstrate an interesting and very concrete connection between parameterized algorithms and exact exponential-time algorithms. We also show how to derandomize our algorithms at the cost of a subexponential multiplicative factor in the running time. Our derandomization is based on an efficient construction of a new pseudo-random object that might be of independent interest. Finally, we extend our methods to establish new combinatorial upper bounds and develop enumeration algorithms.

In a parameterized problem, every instance I comes with a positive integer k . The problem is said to admit a polynomial kernel if, in polynomial time, one can reduce the size of the instance I to a polynomial in k while preserving the answer. In this work, we give two meta-theorems on kernelization. The first theorem says that all problems expressible in counting monadic second-order logic and satisfying a coverability property admit a polynomial kernel on graphs of bounded genus. Our second result is that all problems that have finite integer index and satisfy a weaker coverability property admit a linear kernel on graphs of bounded genus. These theorems unify and extend all previously known kernelization results for planar graph problems.

Let M =( E , I ) be a matroid and let S ={ S 1 , ċ , S t } be a family of subsets of E of size p . A subfamily Ŝ ⊆ S is q - representative for S if for every set Y ⊆ E of size at most q , if there is a set X ∈ S disjoint from Y with X ∪ Y ∈ I , then there is a set Xˆ ∈ Ŝ disjoint from Y with Xˆ ∪ Y ∈ I . By the classic result of Bollobás, in a uniform matroid, every family of sets of size p has a q -representative family with at most ( p + q p ) sets. In his famous “two families theorem” from 1977, Lovász proved that the same bound also holds for any matroid representable over a field F. We give an efficient construction of a q -representative family of size at most ( p + q p ) in time bounded by a polynomial in ( p + q p ), t , and the time required for field operations. We demonstrate how the efficient construction of representative families can be a powerful tool for designing single-exponential parameterized and exact exponential time algorithms. The applications of our approach include the following: —In the L ong D irected C ycle problem, the input is a directed n -vertex graph G and the positive integer k . The task is to find a directed cycle of length at least k in G , if such a cycle exists. As a consequence of our 6.75 k + o ( k ) n O (1) time algorithm, we have that a directed cycle of length at least log n , if such a cycle exists, can be found in polynomial time. —In the M inimum E quivalent G raph (MEG) problem, we are seeking a spanning subdigraph D ′ of a given n -vertex digraph D with as few arcs as possible in which the reachability relation is the same as in the original digraph D . —We provide an alternative proof of the recent results for algorithms on graphs of bounded treewidth showing that many “connectivity” problems such as H amiltonian C ycle or S teiner T ree can be solved in time 2 O ( t ) n on n -vertex graphs of treewidth at most t . For the special case of uniform matroids on n elements, we give a faster algorithm to compute a representative family. We use this algorithm to provide the fastest known deterministic parameterized algorithms for k -P ath , k -T ree , and, more generally, k -S ubgraph I somorphism , where the k -vertex pattern graph is of constant treewidth.

We give an algorithm that for an input $n$-vertex graph $G$ and integer $k>0$, in time $2^{O(k)} n$, either outputs that the treewidth of $G$ is larger than $k$, or gives a tree decomposition of $G$ of width at most $5k+4$. This is the first algorithm providing a constant factor approximation for treewidth which runs in time single exponential in $k$ and linear in $n$. Treewidth-based computations are subroutines of numerous algorithms. Our algorithm can be used to speed up many such algorithms to work in time which is single exponential in the treewidth and linear in the input size.

We study a general class of problems called $\mathcal{F}$-Deletion problems. In an $\mathcal{F}$-Deletion problem, we are asked whether a subset of at most $k$ vertices can be deleted from a graph $G$ such that the resulting graph does not contain as a minor any graph from the family ${\cal F}$ of forbidden minors. We study the problem parameterized by $k$, using $p$-$\mathcal{F}$-Deletion to refer to the parameterized version of the problem. We obtain a number of algorithmic results on the $p$-$\mathcal{F}$-Deletion problem when $\mathcal{F}$ contains a planar graph. We give a linear vertex kernel on graphs excluding $t$-claw $K_{1,t}$, the star with $t$ leaves, as an induced subgraph, where $t$ is a fixed integer and an approximation algorithm achieving an approximation ratio of $O(\log^{3/2} OPT)$, where $OPT$ is the size of an optimal solution on general undirected graphs. Finally, we obtain polynomial kernels for the case when $\cal F$ only contains graph $\theta_c$ as a minor for a fixed integer $c$. The graph $\theta_c$ consists of two vertices connected by $c$ parallel edges. Even though this may appear to be a very restricted class of problems it already encompasses well-studied problems such as Vertex Cover, Feedback Vertex Set, and Diamond Hitting Set. The generic kernelization algorithm is based on a nontrivial application of protrusion techniques, previously used only for problems on topological graph classes.

In the Tree Deletion Set problem the input is a graph $G$ together with an integer $k$. The objective is to determine whether there exists a set $S$ of at most $k$ vertices such that $G\setminus S$ is a tree. The problem is \tt NP-complete and even \tt NP-hard to approximate within any factor of $\text{OPT}^c$ for any constant $c$. In this paper we give an $\mathcal{O}(k^5)$ size kernel for the Tree Deletion Set problem. An appealing feature of our kernelization algorithm is a new reduction rule, based on systems of linear equations, that we use to handle the instances on which Tree Deletion Set is hard to approximate.

The field of exact exponential time algorithms for non-deterministic polynomial-time hard problems has thrived since the mid-2000s. While exhaustive search remains asymptotically the fastest known algorithm for some basic problems, non-trivial exponential time algorithms have been found for a myriad of problems, including G raph C oloring , H amiltonian P ath , D ominating S et , and 3-CNF-S at . In some instances, improving these algorithms further seems to be out of reach. The CNF-S at problem is the canonical example of a problem for which the trivial exhaustive search algorithm runs in time O (2 n ), where n is the number of variables in the input formula. While there exist non-trivial algorithms for CNF-S at that run in time o (2 n ), no algorithm was able to improve the growth rate 2 to a smaller constant, and hence it is natural to conjecture that 2 is the optimal growth rate. The strong exponential time hypothesis (SETH) by Impagliazzo and Paturi [JCSS 2001] goes a little bit further and asserts that, for every ϵ < 1, there is a (large) integer k such that k -CNF-S at cannot be computed in time 2 ϵ n . In this article, we show that, for every ϵ < 1, the problems H itting S et , S et S plitting , and NAE-S at cannot be computed in time O (2 ϵ n ) unless SETH fails. Here n is the number of elements or variables in the input. For these problems, we actually get an equivalence to SETH in a certain sense. We conjecture that SETH implies a similar statement for S et C over and prove that, under this assumption, the fastest known algorithms for S teiner T ree , C onnected V ertex C over , S et P artitioning , and the pseudo-polynomial time algorithm for S ubset S um cannot be significantly improved. Finally, we justify our assumption about the hardness of S et C over by showing that the parity of the number of solutions to S et C over cannot be computed in time O (2 ϵ n ) for any ϵ < 1 unless SETH fails.

Over the past two decades the main focus of research into first-order (FO) model checking algorithms have been sparse relational structures-culminating in the FPT-algorithm by Grohe, Kreutzer and Siebertz for FO model checking of nowhere dense classes of graphs [STOC'14], with dense structures starting to attract attention only recently. Bova, Ganian and Szeider [CSL-LICS'14] initiated the study of the complexity of FO model checking on partially ordered sets (posets). Bova, Ganian and Szeider showed that model checking existential FO logic is fixed-parameter tractable (FPT) on posets of bounded width, where the width of a poset is the size of the largest antichain in the poset. The existence of an FPT algorithm for general FO model checking on posets of bounded width, however, remained open. We resolve this question in the positive by giving an algorithm that takes as its input an n-element poset P of width w and an FO logic formula φ, and determines whether φ holds on P in time f(φ, w) · n2.

Let $$M=(E,\mathcal{I})$$ be a matroid. A $$k$$ -truncation of $$M$$ is a matroid $$M'=(E,\mathcal{I}')$$ such that for any $$A\subseteq E$$ , $$A\in \mathcal{I}'$$ if and only if $$|A|\le k$$ and $$A\in \mathcal {I}$$ . Given a linear representation of $$M$$ we consider the problem of finding a linear representation of the $$k$$ -truncation of this matroid. This problem can be expressed as the following problem on matrices. Let $$M$$ be a $$n\times m$$ matrix over a field $$\mathbb {F}$$ . A rank $$k$$ -truncation of the matrix $$M$$ is a $$k\times m$$ matrix $$M_k$$ (over $${\mathbb F}$$ or a related field) such that for every subset $$I\subseteq \{1,\ldots ,m\}$$ of size at most $$k$$ , the set of columns corresponding to $$I$$ in $$M$$ has rank $$|I|$$ if and only if the corresponding set of columns in $$M_k$$ has rank $$|I|$$ . A common way to compute a rank $$k$$ -truncation of a $$n \times m$$ matrix is to multiply the matrix with a random $$k\times n$$ matrix (with the entries from a field of an exponential size), yielding a simple randomized algorithm. So a natural question is whether it possible to obtain a rank $$k$$ -truncation of a matrix, deterministically. In this paper we settle this question for matrices over any field in which the field operations can be done efficiently. This includes any finite field and the field of rationals ( $$\mathbb Q$$ ). Our algorithms are based on the properties of the classical Wronskian determinant, and the folded Wronskian determinant, which was recently introduced by Guruswami and Kopparty [ FOCS, 2013 ], and was implicitly present in the work of Forbes and Shpilka [ STOC, 2012 ]. These were used in the context of subspace designs, and reducing randomness for polynomial identity testing and other related problems. Our main conceptual contribution in this paper is to show that the Wronskian determinant can also be used to obtain a representation of the truncation of a linear matroid in deterministic polynomial time. Finally, we use our results to derandomize several parameterized algorithms, including an algorithm for computing $$\ell $$ -Matroid Parity, to which several problems like $$\ell $$ -Matroid Intersection can be reduced.

Let F be a family of graphs. Given an input graph G and a positive integer k, testing whether G has a k-sized subset of vertices S, such that G\S belongs to F, is a prototype vertex deletion problem. These type of problems have attracted a lot of attention in recent times in the domain of parameterized complexity. In this paper, we study two such problems; when F is either a family of cactus graphs or a family of odd-cactus graphs. A graph H is called a cactus graph if every pair of cycles in H intersect on at most one vertex. Furthermore, a cactus graph H is called an odd cactus, if every cycle of H is of odd length. Let us denote by C and C_{odd}, families of cactus and odd cactus, respectively. The vertex deletion problems corresponding to C and C_{odd} are called Diamond Hitting Set and Even Cycle Transversal, respectively. In this paper we design randomized algorithms with running time 12^{k}*n^{O(1)} for both these problems. Our algorithms considerably improve the running time for Diamond Hitting Set and Even Cycle Transversal, compared to what is known about them.

A backdoor set of a CNF formula is a set of variables such that fixing the truth values of the variables from this set moves the formula into a polynomial-time de-cidable class. In this work we obtain several algorithmic results for solving d-SAT, by exploiting backdoors to d-CNF formulas whose incidence graphs have small treewidth. For a CNF formula ϕ and integer t, a strong backdoor set to treewidth t is a set of variables such that each possible partial assignment τ to this set reduces ϕ to a formula whose incidence graph is of treewidth at most t. A weak backdoor set to treewidth t is a set of variables such that there is a partial assignment to this set that reduces ϕ to a satisfiable formula of treewidth at most t. Our main contribution is an algorithm that, given a d-CNF formula ϕ and an integer k, in time , either finds a satisfying assignment of ϕ, or reports correctly that ϕ is not satisfiable, or concludes correctly that ϕ has no weak or strong backdoor set to treewidth t of size at most k. As a consequence of the above, we show that d-SAT parameterized by the size of a smallest weak/strong backdoor set to formulas of treewidth t, is fixed-parameter tractable. Prior to our work, such results were know only for the very special case of t = 1 (Gaspers and Szeider, ICALP 2012). Our result not only extends the previous work, it also improves the running time substantially. The running time of our algorithm is linear in the input size for every fixed k. Moreover, the exponential dependence on the parameter k is asymptotically optimal under Exponential Time Hypothesis (ETH). One of our main technical contributions is a linear time “protrusion replacer” improving over a (n log2 n)-time procedure of Fomin et al. (FOCS 2012). The new deterministic linear time protrusion replacer has several applications in kernelization and parameterized algorithms.

We give a fixed-parameter tractable algorithm that, given a parameter k and two graphs G1, G2, either concludes that one of these graphs has treewidth at least k, or determines whether G1and G2are isomorphic. The running time of the algorithm on an n-vertex graph is 2O(k5 log k)· n5, and this is the first fixed-parameter algorithm for Graph Isomorphism parameterized by treewidth. Our algorithm in fact solves the more general canonization problem. We namely design a procedure working in 2OO(k5 log k)· n5time that, for a given graph G on n vertices, either concludes that the treewidth of G is at least k, or finds an isomorphism-invariant construction term - an algebraic expression that encodes G together with a tree decomposition of G of width O(k4). Hence, a canonical graph isomorphic to G can be constructed by simply evaluating the obtained construction term, while the isomorphism test reduces to verifying whether the computed construction terms for G1and G2are equal.

In the Tree Deletion Set problem the input is a graph G together with an integer k. The objective is to determine whether there exists a set S of at most k vertices such that G \ S is a tree. The problem is NP-complete and even NP-hard to approximate within any factor of OPT^c for any constant c. In this paper we give an O(k^5) size kernel for the Tree Deletion Set problem. An appealing feature of our kernelization algorithm is a new reduction rule, based on system of linear equations, that we use to handle the instances on which Tree Deletion Set is hard to approximate.

Let M = (E,I) be a matroid and let S = {S1, …, St} be a family of subsets of E of size p. A subfamily Ŝ ⊆ S is q-representative for S if for every set Y ⊆ E of size at most q, if there is a set X ∊ S disjoint from Y with X ∪ Y ∊ I, then there is a set ∊ Ŝ disjoint from Y with ∪ Y ∊ I. By the classical result of Bollobás, in a uniform matroid, every family of sets of size p has a q-representative family with at most sets. In his famous “two families theorem” from 1977, Lovász proved that the same bound also holds for any matroid representable over a field . As observed by Marx, Lovász's proof is constructive. In this paper we show how Lovász's proof can be turned into an algorithm constructing a q-representative family of size at most in time bounded by a polynomial in , t, and the time required for field operations. We demonstrate how the efficient construction of representative families can be a powerful tool for designing single-exponential parameterized and exact exponential time algorithms. The applications of our approach include the following. In the Long Directed Cycle problem the input is a directed n-vertex graph G and the positive integer k. The task is to find a directed cycle of length at least k in G, if such a cycle exists. As a consequence of our time algorithm, we have that a directed cycle of length at least logn, if such cycle exists, can be found in polynomial time. As it was shown by Björklund, Husfeldt, and Khanna [ICALP 2004], under an appropriate complexity assumption, it is impossible to improve this guarantee by more than a constant factor. Thus our algorithm not only improves over the best previous logn/log logn bound of Gabow and Nie SODA 2004] but also closes the gap between known lower and upper bounds for this problem. In the Minimum Equivalent Graph (MEG) problem we are seeking a spanning subdigraph D′ of a given n-vertex digraph D with as few arcs as possible in which the reachability relation is the same as in the original digraph D. The existence of a single-exponential cn-time algorithm for some constant c > 1 for MEG was open since the work of Moyles and Thompson [JACM 1969]. To demonstrate the diversity of applications of the approach, we provide an alternative proof of the results recently obtained by Bodlaender, Cygan, Kratsch and Nederlof for algorithms on graphs of bounded treewidth, who showed that many “connectivity” problems such as Hamiltonian Cycle or Steiner Tree can be solved in time 2 (t)n on n-vertex graphs of treewidth at most t. We believe that expressing graph problems in “matroid language” shed light on what makes it possible to solve connectivity problems single-exponential time parameterized by treewidth. For the special case of uniform matroids on n elements, we give a faster algorithm computing a representative family in time . We use this algorithm to provide the fastest known deterministic parameterized algorithms for k-Path, k-Tree, and more generally, for k-Subgraph Isomorphism, where the k-vertex pattern graph is of constant treewidth. For example, our k-Path algorithm runs in time on weighted graphs with maximum edge weight W.

The problem of testing whether a graph is planar has been studied for over half a century, and is known to be solvable in (n) time using a myriad of different approaches and techniques. Robertson and Seymour established the existence of a cubic algorithm for the more general problem of deciding whether an n-vertex graph can be made planar by at most k vertex deletions, for every fixed k. Of the known algorithms for k-Vertex Planarization, the algorithm of Marx and Schlotter (WG 2007, Algorithmica 2012) running in time achieves the best running time dependence on k. The algorithm of Kawarabayashi (FOCS 2009), running in time f(k)n for some f(k) ∊ that is not stated explicitly, achieves the best dependence on n. In this paper we present an algorithm for k-Vertex Planarization with running time 2 (k log k)·n, significantly improving the running time dependence on k without compromising the linear dependence on n. Our main technical contribution is a novel scheme to reduce the treewidth of the input graph to (k) in time 2O(klogk) · n. It combines new insights into the structure of graphs that become planar after contracting a matching, with a Baker-type subroutine that reduces the number of disjoint paths through planar parts of the graph that are not affected by the sought solution. To solve the reduced instances we formulate a dynamic programming algorithm for Weighted Vertex Planarization on graphs of treewidth w with running time 2 (w log w) · n, thereby improving over previous double-exponential algorithms. While Kawarabayashi's planarization algorithm relies heavily on deep results from the graph minors project, our techniques are elementary and practically self-contained. We expect them to be applicable to related edge-deletion and contraction variants of planarization problems.

The Independent Set problem is NP-hard in general, however polynomial time algorithms exist for the problem on various specific graph classes. Over the last couple of decades there has been a long sequence of papers exploring the boundary between the NP-hard and polynomial time solvable cases. In particular the complexity of Independent Set on P5-free graphs has received significant attention, and there has been a long list of results showing that the problem becomes polynomial time solvable on sub-classes of P5-free graphs. In this paper we give the first polynomial time algorithm for Independent Set on P5-free graphs. Our algorithm also works for the Weighted Independent Set problem.

In the classic Minimum Bisection problem we are given as input a graph G and an integer k. The task is to determine whether there is a partition of V (G) into two parts A and B such that ||A| -- |B|| ≤ 1 and there are at most k edges with one endpoint in A and the other in B. In this paper we give an algorithm for Minimum Bisection with running time O(2O(k3) n3 log3 n). This is the first fixed parameter tractable algorithm for Minimum Bisection. At the core of our algorithm lies a new decomposition theorem that states that every graph G can be decomposed by small separators into parts where each part is "highly connected" in the following sense: any cut of bounded size can separate only a limited number of vertices from each part of the decomposition.

We study the Cutwidth problem, where the input is a graph G, and the objective is find a linear layout of the vertices that minimizes the maximum number of edges intersected by any vertical line inserted between two consecutive vertices. We give an algorithm for Cutwidth with running time O(2 k n O(1)). Here k is the size of a minimum vertex cover of the input graph G, and n is the number of vertices in G. Our algorithm gives an O(2 n/2 n O(1)) time algorithm for Cutwidth on bipartite graphs as a corollary. This is the first non-trivial exact exponential time algorithm for Cutwidth on a graph class where the problem remains NP-complete. Additionally, we show that Cutwidth parameterized by the size of the minimum vertex cover of the input graph does not admit a polynomial kernel unless NP⊆coNP/poly. Our kernelization lower bound contrasts with the recent results of Bodlaender et al. (ICALP, Springer, Berlin, 2011; SWAT, Springer, Berlin, 2012) that both Treewidth and Pathwidth parameterized by vertex cover do admit polynomial kernels.

We obtain asymptotically tight algorithmic bounds for Max-Cut and Edge Dominating Set problems on graphs of bounded clique-width. We show that on an $n$-vertex graph of clique-width $t$ both problems (1) cannot be solved in time $f(t)n^{o(t)}$ for any function $f$ of $t$ unless exponential time hypothesis fails, and (2) can be solved in time $n^{O(t)}$.

In parameterized complexity, each problem instance comes with a parameter k , and a parameterized problem is said to admit a polynomial kernel if there are polynomial time preprocessing rules that reduce the input instance to an instance with size polynomial in k . Many problems have been shown to admit polynomial kernels, but it is only recently that a framework for showing the nonexistence of polynomial kernels for specific problems has been developed by Bodlaender et al. [2009] and Fortnow and Santhanam [2008]. With few exceptions, all known kernelization lower bounds results have been obtained by directly applying this framework. In this article, we show how to combine these results with combinatorial reductions that use colors and IDs in order to prove kernelization lower bounds for a variety of basic problems. To follow we give a summary of our main results. All results are under the assumption that the polynomial hierarchy does not collapse to the third level. —We show that the Steiner Tree problem parameterized by the number of terminals and solution size k , and the Connected Vertex Cover and Capacitated Vertex Cover problems do not admit a polynomial kernel. The two latter results are surprising because the closely related Vertex Cover problem admits a kernel with at most 2 k vertices. —Alon and Gutner [2008] obtain a k poly ( h ) kernel for Dominating Set in H -Minor Free Graphs parameterized by h = | H | and solution size k , and ask whether kernels of smaller size exist. We partially resolve this question by showing that Dominating Set in H -Minor Free Graphs does not admit a kernel with size polynomial in k + h . —Harnik and Naor [2007] obtain a “compression algorithm” for the Sparse Subset Sum problem. We show that their algorithm is essentially optimal by showing that the instances cannot be compressed further. —The Hitting Set and Set Cover problems are among the most-studied problems in algorithmics. Both problems admit a kernel of size k O ( d ) when parameterized by solution size k and maximum set size d . We show that neither of them, along with the Unique Coverage and Bounded Rank Disjoint Sets problems, admits a polynomial kernel. The existence of polynomial kernels for several of the problems mentioned previously was an open problem explicitly stated in the literature [Alon and Gutner 2008; Betzler 2006; Guo and Niedermeier 2007; Guo et al. 2007; Moser et al. 2007]. Many of our results also rule out the existence of compression algorithms, a notion similar to kernelization defined by Harnik and Naor [2007], for the problems in question.

We investigate the parameterized complexity of Vertex Cover parameterized by the difference between the size of the optimal solution and the value of the linear programming (LP) relaxation of the problem. By carefully analyzing the change in the LP value in the branching steps, we argue that combining previously known preprocessing rules with the most straightforward branching algorithm yields an O *(2.618 k ) algorithm for the problem. Here, k is the excess of the vertex cover size over the LP optimum, and we write O *( f ( k )) for a time complexity of the form O ( f ( k ) n O (1) ). We proceed to show that a more sophisticated branching algorithm achieves a running time of O *(2.3146 k ). Following this, using previously known as well as new reductions, we give O *(2.3146 k ) algorithms for the parameterized versions of Above Guarantee Vertex Cover , Odd Cycle Transversal , Split Vertex Deletion, and Almost 2-SAT , and O *(1.5214 k ) algorithms for König Vertex Deletion and Vertex Cover parameterized by the size of the smallest odd cycle transversal and König vertex deletion set. These algorithms significantly improve the best known bounds for these problems. The most notable improvement among these is the new bound for Odd Cycle Transversal —this is the first algorithm that improves on the dependence on k of the seminal O *(3 k ) algorithm of Reed, Smith, and Vetta. Finally, using our algorithm, we obtain a kernel for the standard parameterization of Vertex Cover with at most 2 k − c log k vertices. Our kernel is simpler than previously known kernels achieving the same size bound.

RNA splicing is a cellular process driven by the interaction between numerous regulatory sequences and binding sites, however, such interactions have been primarily explored by laboratory methods since computational tools largely ignore the relationship between different splicing elements. Current computational methods identify either splice sites or other regulatory sequences, such as enhancers and silencers. We present a novel approach for characterizing co-occurring relationships between splice site motifs and splicing enhancers. Our approach relies on an efficient algorithm for approximately solving Consensus Sequence with Outliers , an NP-complete string clustering problem. In particular, we give an algorithm for this problem that outputs near-optimal solutions in polynomial time. To our knowledge, this is the first formulation and computational attempt for detecting co-occurring sequence elements in RNA sequence data. Further, we demonstrate that SeeSite is capable of showing that certain ESEs are preferentially associated with weaker splice sites, and that there exists a co-occurrence relationship with splice site motifs.

We give an algorithm that for an input n-vertex graph G and integer k > 0, in time O(ckn) either outputs that the tree width of G is larger than k, or gives a tree decomposition of G of width at most 5k + 4. This is the first algorithm providing a constant factor approximation for tree width which runs in time single-exponential in k and linear in n. Tree width based computations are subroutines of numerous algorithms. Our algorithm can be used to speed up many such algorithms to work in time which is single-exponential in the tree width and linear in the input size.

Multi-fold cross-validation is an established practice to estimate the error rate of a learning algorithm. Quantifying the variance reduction gains due to cross-validation has been challenging due to the inherent correlations introduced by the folds. In this work we introduce a new and weak measure of stability called \emphloss stability and relate the cross-validation performance to loss stability; we also establish that this relationship is near-optimal. Our work thus quantitatively improves the current best bounds on cross-validation.

We give the first linear kernels for Dominating Set and Connected Dominating Set problems on graphs excluding a fixed graph H as a topological minor.

The Bipartite Contraction problem takes as input an $n$-vertex graph $G$ and an integer $k$, and the task is to determine whether we can obtain a bipartite graph from $G$ by a sequence of at most $k$ edge contractions. We show that Bipartite Contraction is fixed-parameter tractable when parameterized by $k$. Despite a strong resemblance between Bipartite Contraction and the classical Odd Cycle Transversal (OCT) problem, the methods developed to tackle OCT do not seem to be directly applicable to Bipartite Contraction. To obtain our result, we combine several techniques and concepts that are central in parameterized complexity: iterative compression, irrelevant vertices, and important separators. To the best of our knowledge, this is the first time the irrelevant vertex technique and the concept of important separators are applied in unison. Furthermore, our algorithm may serve as a comprehensible example of the usage of the irrelevant vertex technique.

The field of exact exponential time algorithms for NP-hard problems has thrived over the last decade. While exhaustive search remains asymptotically the fastest known algorithm for some basic problems, difficult and non-trivial exponential time algorithms have been found for a myriad of problems, including GRAPH COLORING, HAMILTONIAN PATH, DOMINATING SET and 3-CNF-SAT. In some instances, improving these algorithms further seems to be out of reach. The CNF-SAT problem is the canonical example of a problem for which the trivial exhaustive search algorithm runs in time O(2n), where n is the number of variables in the input formula. While there exist non-trivial algorithms for CNF-SAT that run in time o(2n), no algorithm was able to improve the growth rate 2 to a smaller constant, and hence it is natural to conjecture that 2 is the optimal growth rate. The strong exponential time hypothesis (SETH) by Impagliazzo and Paturi [JCSS 2001] goes a little bit further and asserts that, for every ϵϵn. In this paper, we show that, for every ϵϵn) unless SETH fails. Here n is the number of elements or variables in the input. For these problems, we actually get an equivalence to SETH in a certain sense. We conjecture that SETH implies a similar statement for SET COVER, and prove that, under this assumption, the fastest known algorithms for STEINTER TREE, CONNECTED VERTEX COVER, SET PARTITIONING, and the pseudo-polynomial time algorithm for SUBSET SUM cannot be significantly improved. Finally, we justify our assumption about the hardness of SET COVER by showing that the parity of the number of set covers.

Let F be a finite set of graphs. In the F-DELETION problem, we are given an n-vertex graph G and an integer k as input, and asked whether at most k vertices can be deleted from G such that the resulting graph does not contain a graph from F as a minor. F-DELETION is a generic problem and by selecting different sets of forbidden minors F, one can obtain various fundamental problems such as VERTEX COVER, FEEDBACK VERTEX SET or TREEWIDTH η-DELETION. In this paper we obtain a number of generic algorithmic results about F-DELETION, when F contains at least one planar graph. The highlights of our work are · A constant factor approximation algorithm for the optimization version of F-DELETION; · A linear time and single exponential parameterized algorithm, that is, an algorithm running in time O(2O(k)n), for the parameterized version of F-DELETION where all graphs in F are connected; · A polynomial kernel for parameterized F-DELETION. These algorithms unify, generalize, and improve a multitude of results in the literature. Our main results have several direct applications, but also the methods we develop on the way have applicability beyond the scope of this paper. Our results - constant factor approximation, polynomial kernelization and FPT algorithms - are stringed together by a common theme of polynomial time preprocessing.

In the Odd Cycle Transversal (OCT) problem we are given a graph G on n vertices and an integer k, the objective is to determine whether there exists a vertex set O in G of size at most k such that G - O is bipartite. Reed, Smith and Vetta [Oper. Res. Lett., 2004] gave an algorithm for OCT with running time 3^kn^{O(1)}. Assuming the exponential time hypothesis of Impagliazzo, Paturi and Zane, the running time can not be improved to 2^{o(k)}n^{O(1)}. We show that OCT admits a randomized algorithm running in O(n^{O(1)} + 2^{O(sqrt{k} log k)}n) time when the input graph is planar. As a byproduct we also obtain a linear time algorithm for OCT on planar graphs with running time O(n^O(1) + 2O( sqrt(k) log k) n) time. This improves over an algorithm of Fiorini et al. [Disc. Appl. Math., 2008].

Bidimensionality theory was introduced by Demaine et al. [JACM 2005] as a framework to obtain algorithmic results for hard problems on minor closed graph classes. The theory has been successfully applied to yield subexponential time parameterized algorithms, EPTASs and linear kernels for many problems on families of graphs excluding a fixed graph H as a minor. In this paper we use several of the key ideas from Bidimensionality to give a new generic approach to design EPTASs and subexponential time parameterized algorithms for problems on classes of graphs which are not minor closed, but instead exhibit a geometric structure. In particular we present EPTASs and subexponential time parameterized algorithms for Feedback Vertex Set, Vertex Cover, Connected Vertex Cover, on map graphs and unit disk graphs, PTASs for Diamond Hitting Set on map graphs and unit disk graphs, and a PTAS and a subexponential time algorithm for Cycle Packing on unit disk graphs. To the best of our knowledge, these results were previously unknown, with the exception of the EPTAS and a subexponential time parameterized algorithm on unit disk graphs for Vertex Cover, which were obtained by Marx [ESA 2005] and Alber and Fiala [J. Algorithms 2004], respectively. Our results are based on the recent decomposition theorems proved by Fomin et al. in [SODA 2011] and novel grid-excluding theorems in unit disk and map graphs without large cliques. Our algorithms work directly on the input graph and do not require the geometric representations of the input graph. We also show that our approach can not be extended in its full generality to more general classes of geometric graphs, such as intersection graphs of unit balls in ℝd, d ≥ 3. Specifically, we prove that Feedback Vertex Set on unit-ball graphs in ℝ3 neither admits PTASs unless P=NP, nor subexponential time algorithms unless the Exponential Time Hypothesis fails. Additionally, we show that the decomposition theorems which our approach is based on, fail for disk graphs and that therefore any extension of our results to disk graphs would require new algorithmic ideas. On the other hand, we prove that our EPTASs and subexponential time algorithms for Vertex Cover and Connected Vertex Cover carry over both to disk graphs and to unit-ball graphs in ℝd for every fixed d.

We give the first linear kernels for Dominating Set and Connected Dominating Set problems on graphs excluding a fixed graph H as a minor. In other words, we give polynomial time algorithms that, for a given H-minor free graph G and positive integer k, output an H-minor free graph G′ on O(k) vertices such that G has a (connected) dominating set of size k if and only if G′ has. Prior to our work, the only polynomial kernel for Dominating Set on graphs excluding a fixed graph H as a minor was due to Alon and Gutner [ECCC 2008, IWPEC 2009] and to Philip, Raman, and Sikdar [ESA 2009] but the size of their kernel is kc(H), where c(H) is a constant depending on the size of H. Alon and Gutner asked explicitly, whether one can obtain a linear kernel for Dominating Set on H-minor free graphs. We answer this question in affirmative. For Connected Dominating Set no polynomial kernel on H-minor free graphs was known prior to our work. Our results are based on a novel generic reduction rule producing an equivalent instance of the problem with treewidth O(√k). The application of this rule in a divide-and-conquer fashion together with protrusion techniques brings us to linear kernels. As a byproduct of our results we obtain the first subexponential time algorithms for Connected Dominating Set, a deterministic algorithm solving the problem on an n-vertex H-minor free graph in time 2O(√k log k) + nO(1) and a Monte Carlo algorithm of running time 2O(√k) + nO(1). For Dominating Set our results implies a significant simplification and refinement of a 2O(√k) nO(1) algorithm on H minor free graphs due to Demaine et al. [SODA 2003, J. ACM 2005].

The problem of determining the cutwidth of a graph is a notoriously hard problem which remains NP-complete under severe restrictions on input graphs. Until recently, nontrivial polynomial-time cutwidth algorithms were known only for subclasses of graphs of bounded treewidth. Very recently, Heggernes et al. (SIAM J. Discrete Math., 25 (2011), pp. 1418--1437) initiated the study of cutwidth on graph classes containing graphs of unbounded treewidth and showed that a greedy algorithm computes the cutwidth of threshold graphs. We continue this line of research and present the first polynomial-time algorithm for computing the cutwidth of bipartite permutation graphs. Our algorithm runs in linear time. We stress that the cutwidth problem is NP-complete on bipartite graphs and its computational complexity is open even on small subclasses of permutation graphs, such as trivially perfect graphs.

The k -Leaf Out-Branching problem is to find an out-branching, that is a rooted oriented spanning tree, with at least k leaves in a given digraph. The problem has recently received much attention from the viewpoint of parameterized algorithms. Here, we take a kernelization based approach to the k -Leaf-Out-Branching problem. We give the first polynomial kernel for Rooted k -Leaf-Out-Branching, a variant of k -Leaf-Out-Branching where the root of the tree searched for is also a part of the input. Our kernel with O ( k 3 ) vertices is obtained using extremal combinatorics. For the k -Leaf-Out-Branching problem, we show that no polynomial-sized kernel is possible unless coNP is in NP/poly . However, our positive results for Rooted k -Leaf-Out-Branching immediately imply that the seemingly intractable k -Leaf-Out-Branching problem admits a data reduction to n independent polynomial-sized kernels. These two results, tractability and intractability side by side, are the first ones separating Karp kernelization from Turing kernelization . This answers affirmatively an open problem regarding “cheat kernelization” raised by Mike Fellows and Jiong Guo independently.

We initiate the study of the Bipartite Contraction problem from the perspective of parameterized complexity. In this problem we are given a graph $G$ and an integer $k$, and the task is to determine whether we can obtain a bipartite graph from $G$ by a sequence of at most $k$ edge contractions. Our main result is an $f(k) n^{O(1)}$ time algorithm for Bipartite Contraction. Despite a strong resemblance between Bipartite Contraction and the classical Odd Cycle Transversal (OCT) problem, the methods developed to tackle OCT do not seem to be directly applicable to Bipartite Contraction. Our algorithm is based on a novel combination of the irrelevant vertex technique, introduced by Robertson and Seymour, and the concept of important separators. Both techniques have previously been used as key components of algorithms for fundamental problems in parameterized complexity. However, to the best of our knowledge, this is the first time the two techniques are applied in unison.

Bidimensionality theory appears to be a powerful framework for the development of meta-algorithmic techniques. It was introduced by Demaine et al. [J. ACM 2005] as a tool to obtain sub-exponential time parameterized algorithms for problems on H-minor free graphs. Demaine and Hajiaghayi [SODA 2005] extended the theory to obtain polynomial time approximation schemes (PTASs) for bidimensional problems, and subsequently improved these results to EPTASs. Fomin et. al [SODA 2010] established a third meta-algorithmic direction for bidimensionality theory by relating it to the existence of linear kernels for parameterized problems. In this paper we revisit bidimensionality theory from the perspective of approximation algorithms and redesign the framework for obtaining EPTASs to be more powerful, easier to apply and easier to understand. One of the important conditions required in the framework developed by Demaine and Hajiaghayi [SODA 2005] is that to obtain an EPTAS for a graph optimization problem П, we have to know a constant-factor approximation algorithm for П. Our approach eliminates this strong requirement, which makes it amenable to more problems. At the heart of our framework is a decomposition lemma which states that for “most” bidimensional problems, there is a polynomial time algorithm which given an H-minor-free graph G as input and an ε > 0 outputs a vertex set X of size ε · OPT such that the treewidth of G\X is O(1/ε). Here, OPT is the objective function value of the problem in question This allows us to obtain EPTASs on (apex)-minor-free graphs for all problems covered by the previous framework, as well as for a wide range of packing problems, partial covering problems and problems that are neither closed under taking minors, nor contractions. To the best of our knowledge for many of these problems including Cycle Packing, Vertex-H-Packing, Maximum Leaf Spanning Tree, and Partial r-Dominating Set no EPTASs on planar graphs were previously known.

A central problem in parameterized algorithms is to obtain algorithms with running time f(k) · nO(1) such that f is as slow growing function of the parameter k as possible. In particular, the first natural goal is to make f(k) single-exponential, that is, ck for some constant c. This has led to the development of parameterized algorithms for various problems where f(k) appearing in their running time is of form 2O(k). However there are still plenty of problems where the “slightly superexponential” f(k) appearing in the best known running time has remained non single-exponential even after a lot of attempts to bring it down. A natural question to ask is whether the f(k) appearing in the running time of the best-known algorithms is optimal for any of these problems. In this paper, we examine parameterized problems where f(k) is kO(k) = 2O(k log k) in the best known running time and for a number of such problems, we show that the dependence on k in the running time cannot be improved to single exponential. More precisely we prove following tight lower bounds, for three natural problems, arising from three different domains: The pattern matching problem Closest String is known to be solvable in time 2O(d log d) · nO(1) and 2O(d log |σ|) · nO(1). We show that there is no 2o(d log d).· nO(1) and 2o(d log |σ|) · nO(1) time algorithm, unless Exponential Time Hypothesis (ETH) fails. The graph embedding problem Distortion, that is, deciding whether a graph G has a metric embedding into the integers with distortion at most d can be done in time 2O(d log d) · nO(1). We show that there is no 2o(d log d) · nO(1) time algorithm, unless ETH fails. The Disjoint Paths problem can be solved in time in time 2O(w log w) · nO(1) on graphs of treewidth at most w. We show that there is no 2o(w log w) · nO(1) time algorithm, unless ETH fails. To obtain our result we first prove the lower bound for variants of basic problems: finding cliques, independent sets, and hitting sets. These artificially constrained variants form a good starting point for proving lower bounds on natural problems without any technical restrictions and could be of independent interest. We believe that many further results of this form can be obtained by using the framework of the current paper.

We obtain a number of lower bounds on the running time of algorithms solving problems on graphs of bounded treewidth. We prove the results under the Strong Exponential Time Hypothesis of Impagliazzo and Paturi. In particular, assuming that SAT cannot be solved in (2 − ε) nmO(1) time, we show that for any ε > 0; Independent Set cannot be solved in time (2 − ε)tw(G)|V(G)|O(1), Dominating Set cannot be solved in time (3 − ε)tw(G)|V(G)|O(1), Max Cut cannot be solved in time (2 − ε)tw(G)|V(G)|O(1), Odd Cycle Tranversal cannot be solved in time (3 − ε)tw(G) |V(G)|O(1), For any q ≥ 3, q-Coloring cannot be solved in time (q − ε)tw(G)|V(G)|O(1), Partition Into Triangles cannot be solved in time (2 − ε)tw(G)|V(G)|O(1). Our lower bounds match the running times for the best known algorithms for the problems, up to the ε in the base.

We study a general class of problems called F-deletion problems. In an F-deletion problem, we are asked whether a subset of at most $k$ vertices can be deleted from a graph $G$ such that the resulting graph does not contain as a minor any graph from the family F of forbidden minors. We obtain a number of algorithmic results on the F-deletion problem when F contains a planar graph. We give (1) a linear vertex kernel on graphs excluding $t$-claw $K_{1,t}$, the star with $t$ leves, as an induced subgraph, where $t$ is a fixed integer. (2) an approximation algorithm achieving an approximation ratio of $O(\log^{3/2} OPT)$, where $OPT$ is the size of an optimal solution on general undirected graphs. Finally, we obtain polynomial kernels for the case when F contains graph $θ_c$ as a minor for a fixed integer $c$. The graph $θ_c$ consists of two vertices connected by $c$ parallel edges. Even though this may appear to be a very restricted class of problems it already encompasses well-studied problems such as {\sc Vertex Cover}, {\sc Feedback Vertex Set} and Diamond Hitting Set. The generic kernelization algorithm is based on a non-trivial application of protrusion techniques, previously used only for problems on topological graph classes.

We give a linear-time algorithm to compute the cutwidth of threshold graphs, thereby resolving the computational complexity of cutwidth on this graph class. Threshold graphs are a well-studied subclass of interval graphs and of split graphs, both of which are unrelated subclasses of chordal graphs. To complement our result, we show that cutwidth is NP-complete on split graphs, and consequently also on chordal graphs. The cutwidth of interval graphs is still open, and only very few graph classes are known so far on which polynomial-time cutwidth algorithms exist. Thus we contribute to define the border between graph classes on which cutwidth is polynomially solvable and on which it remains NP-complete.

We present a fast local search algorithm that finds an improved solution (if there is any) in the k-exchange neighborhood of the given solutionto an instance of Weighted Feedback Arc Set in Tournaments. More precisely,given an arc weighted tournament T on n vertices and a feedback arc set F (a set of arcs whose deletion from T turns it into a directed acyclic graph), our algorithm decides in time O(2o(k) n log n) if there is a feedback arc set of smaller weight and that differs from F in at most k arcs. To our knowledge this is the first algorithm searching the k-exchange neighborhood of an NP-complete problem that runs in (parameterized) subexponential time. Using this local search algorithm for Weighted Feedback Arc Set in Tournaments, we obtain subexponential time algorithms for a local search variant of Kemeny Ranking — a problem in social choice theory and of One-Sided Cross Minimization — a problem in graph drawing.

Computing the Dodgson Score of a candidate in an election is a hard computational problem, which has been analyzed using classical and parameterized analysis. In this paper we resolve two open problems regarding the parameterized complexity of DODGSON SCORE. We show that DODGSON SCORE parameterized by the target score value $k$ does not have a polynomial kernel unless the polynomial hierarchy collapses to the third level; this complements a result of Fellows, Rosamond and Slinko who obtain a non-trivial kernel of exponential size for a generalization of this problem. We also prove that DODGSON SCORE parameterized by the number $n$ of votes is hard for $W[1]$.

Many NP-hard problems can be solved efficiently when the input is restricted to graphs of bounded tree-width or clique-width. In particular, by the celebrated result of Courcelle, every decision problem expressible in monadic second order logic is fixed parameter tractable when parameterized by the tree-width of the input graph. On the other hand if we restrict ourselves to graphs of clique-width at most t, then there are many natural problems for which the running time of the best known algorithms is of the form nf(t), where n is the input length and f is some function. It was an open question whether natural problems like Graph Coloring, Max-Cut, Edge Dominating Set, and Hamiltonian Path are fixed parameter tractable when parameterized by the clique-width of the input graph. As a first step toward obtaining lower bounds for clique-width parameterizations, in [SODA 2009], we showed that unless FPT≠W[1], there is no algorithm with run time O(g(t) · nc), for some function g and a constant c not depending on t, for Graph Coloring, Edge Dominating Set and Hamiltonian Path. But the lower bounds obtained in [SODA 2009] are weak when compared to the upper bounds on the time complexity of the known algorithms for these problems when parameterized by the clique-width. In this paper, we obtain the asymptotically tight bounds for Max-Cut and Edge Dominating Set by showing that both problems cannot be solved in time f(t)no(t), unless Exponential Time Hypothesis (ETH) collapses; and can be solved in time nO(t), where f is an arbitrary function of t, on input of size n and clique-width at most t. We obtain our lower bounds by giving non-trivial structure-preserving “linear FPT reductions”.

Bidimensionality theory appears to be a powerful framework in the development of meta-algorithmic techniques. It was introduced by Demaine et al. [J. ACM 2005] as a tool to obtain sub-exponential time parameterized algorithms for bidimensional problems on H-minor free graphs. Demaine and Hajiaghayi [SODA 2005] extended the theory to obtain polynomial time approximation schemes (PTASs) for bidimensional problems. In this paper, we establish a third meta-algorithmic direction for bidimensionality theory by relating it to the existence of linear kernels for parameterized problems. In parameterized complexity, each problem instance comes with a parameter k and the parameterized problem is said to admit a linear kernel if there is a polynomial time algorithm, called a kernelization algorithm, that reduces the input instance to an equivalent instance (called kernel) with size linearly bounded by k. We show that “essentially” all bidimensional problems not only have sub-exponential time algorithms and PTASs but they also have linear kernels, affirmatively answering an open question from [J. ACM 2005] where the existence of linear kernels was conjectured for the first time. In particular, we prove that every minor (respectively contraction) bidimensional problem that satisfies the separation property and is of finite integer index, admits a linear kernel for classes of graphs that exclude a fixed graph (respectively an apex graph H) H as a minor. Recently, Bodlaender et al. [FOCS 2009] laid the foundation for obtaining meta-algorithmic results for kernelization and showed that various problems satisfying some logical and compactness properties have polynomial, even linear kernels on graphs of bounded genus. With the use of bidimensionality we are able to extend these results to minor-free and apex-minor-free graphs. Our results imply that a multitude of bidimensional problems, which include Dominating Set, Feedback Vertex Set, Edge Dominating Set, Vertex Cover, r-Dominating Set, Connected Dominating Set, Cycle Packing, Connected Vertex Cover, Almost Constant Treewidth, and various other vertex covering and packing problems, admit linear kernels on the corresponding graph classes. For most of these problems no polynomial kernels on H-minor-free graphs were known prior to our work.

In this paper we make the first step beyond bidimensionality by obtaining subexponential time algorithms for problems on directed graphs. We develop two different methods to achieve subexponential time parameterized algorithms for problems on sparse directed graphs. We exemplify our approaches with two well studied problems. For the first problem, $k$-Leaf Out-Branching, which is to find an oriented spanning tree with at least $k$ leaves, we obtain an algorithm solving the problem in time $2^{\cO(\sqrt{k} \log k)} n+ n^{\cO(1)}$ on directed graphs whose underlying undirected graph excludes some fixed graph $H$ as a minor. For the special case when the input directed graph is planar, the running time can be improved to $2^{\cO(\sqrt{k} )}n + n^{\cO(1)}$. The second example is a generalization of the {\sc Directed Hamiltonian Path} problem, namely $k$-Internal Out-Branching, which is to find an oriented spanning tree with at least $k$ internal vertices. We obtain an algorithm solving the problem in time $2^{\cO(\sqrt{k} \log k)} + n^{\cO(1)}$ on directed graphs whose underlying undirected graph excludes some fixed apex graph $H$ as a minor. Finally, we observe that for any $\ve>0$, the $k$-Directed Path problem is solvable in time $\cO((1+\ve)^k n^{f(\ve)})$, where $f$ is some function of $\ve$. Our methods are based on non-trivial combinations of obstruction theorems for undirected graphs, kernelization, problem specific combinatorial structures and a layering technique similar to the one employed by Baker to obtain PTAS for planar graphs.

The Subset Sum and Knapsack problems are fundamental NP-complete problems and the pseudo-polynomial time dynamic programming algorithms for them appear in every algorithms textbook. The algorithms require pseudo-polynomial time and space. Since we do not expect polynomial time algorithms for Subset Sum and Knapsack to exist, a very natural question is whether they can be solved in pseudo-polynomial time and polynomial space. In this paper we answer this question affirmatively, and give the first pseudo-polynomial time, polynomial space algorithms for these problems.

We show that Edge Dominating Set, Hamiltonian Cycle, and Graph Coloring are $W[1]$-hard parameterized by clique-width. It was an open problem, explicitly mentioned in several papers, whether any of these problems is fixed parameter tractable when parameterized by the clique-width, that is, solvable in time $g(k)\cdot n^{O(1)}$ on n-vertex graphs of clique-width k, where g is some function of k only. Our results imply that the running time $O(n^{f(k)})$ of many clique-width-based algorithms is essentially the best we can hope for (up to a widely believed assumption from parameterized complexity, namely $FPT\neq W[1]$).

Polynomial time preprocessing to reduce instance size is one of the most commonly deployed heuristics to tackle computationally hard problems. In a parameterized problem, every instance I comes with a positive integer k. The problem is said to admit a polynomial kernel if, in polynomial time, we can reduce the size of the instance I to a polynomial in k, while preserving the answer. In this paper, we show that all problems expressible in Counting Monadic Second Order Logic and satisfying a compactness property admit a polynomial kernel on graphs of bounded genus. Our second result is that all problems that have finite integer index and satisfy a weaker compactness condition admit a linear kernel on graphs of bounded genus. The study of kernels on planar graphs was initiated by a seminal paper of Alber, Fellows, and Niedermeier [J. ACM, 2004 ] who showed that Planar Dominating Set admits a linear kernel. Following this result, a multitude of problems have been shown to admit linear kernels on planar graphs by combining the ideas of Alber et al. with problem specific reduction rules. Our theorems unify and extend all previously known kernelization results for planar graph problems. Combining our theorems with the Erdos-Posa property we obtain various new results on linear kernels for a number of packing and covering problems.

Partial Cover problems are optimization versions of fundamental and well studied problems like {\sc Vertex Cover} and {\sc Dominating Set}. Here one is interested in covering (or dominating) the maximum number of edges (or vertices) using a given number ($k$) of vertices, rather than covering all edges (or vertices). In general graphs, these problems are hard for parameterized complexity classes when parameterized by $k$. It was recently shown by Amini et. al. [{\em FSTTCS 08}\,] that {\sc Partial Vertex Cover} and {\sc Partial Dominating Set} are fixed parameter tractable on large classes of sparse graphs, namely $H$-minor free graphs, which include planar graphs and graphs of bounded genus. In particular, it was shown that on planar graphs both problems can be solved in time $2^{\cO(k)}n^{\cO(1)}$.

The Target Set Selection problem proposed by Kempe, Kleinberg, and Tardos, gives a nice clean combinatorial formulation for many problems arising in economy, sociology, and medicine. Its input is a graph with vertex thresholds, the social network, and the goal is to find a subset of vertices, the target set, that "activates" a prespecified number of vertices in the graph. Activation of a vertex is defined via a so-called activation process as follows: Initially, all vertices in the target set become active. Then at each step i of the process, each vertex gets activated if the number of its active neighbors at iteration i -- 1 exceeds its threshold. The activation process is "monotone" in the sense that once a vertex is activated, it remains active for the entire process.

Many hard problems can be solved efficiently when the input is restricted to graphs of bounded treewidth. By the celebrated result of Courcelle, every decision problem expressible in monadic second order logic is fixed parameter tractable when parameterized by the treewidth of the input graph. Moreover, for every fixed k ≥ 0, such problems can be solved in linear time on graphs of treewidth at most k. In particular, this implies that basic problems like Dominating Set, Graph Coloring, Clique, and Hamiltonian Cycle are solvable in linear time on graphs of bounded treewidth. A significant amount of research in graph algorithms has been devoted to extending this result to larger classes of graphs. It was shown that some of the algorithmic meta-theorems for treewidth can be carried over to graphs of bounded clique-width. Courcelle, Makowsky, and Rotics proved that the analogue of Courcelle's result holds for graphs of bounded clique-width when the logical formulas do not use edge set quantifications. Despite of its generality, this does not resolve the parameterized complexity of many basic problems concerning edge subsets (like Edge Dominating Set), vertex partitioning (like Graph Coloring), or global connectivity (like Hamiltonian Cycle). There are various algorithms solving some of these problems in polynomial time on graphs of clique-width at most k. However, these are not fixed parameter tractable algorithms and have typical running times O(nf(k)), where n is the input length and f is some function. It was an open problem, explicitly mentioned in several papers, whether any of these problems is fixed parameter tractable when parameterized by the clique-width, i.e. solvable in time O(g(k)·nc), for some function g and a constant c not depending on k. In this paper we resolve this problem by showing that Edge Dominating Set, Hamiltonian Cycle, and Graph Coloring are W[1]-hard parameterized by clique-width. This shows that the running time O(nf(k)) of many clique-width based algorithms is essentially the best we can hope for (up to a widely believed assumption from parameterized complexity, namely FPT ≠ W[1])—the price we pay for generality.

The {\sc $k$-Leaf Out-Branching} problem is to find an out-branching, that is a rooted oriented spanning tree, with at least $k$ leaves in a given digraph. The problem has recently received much attention from the viewpoint of parameterized algorithms. Here, we take a kernelization based approach to the {\sc $k$-Leaf-Out-Branching} problem. We give the first polynomial kernel for {\sc Rooted $k$-Leaf-Out-Branching}, a variant of {\sc $k$-Leaf-Out-Branching} where the root of the tree searched for is also a part of the input. Our kernel has cubic size and is obtained using extremal combinatorics. For the {\sc $k$-Leaf-Out-Branching} problem, we show that no polynomial kernel is possible unless the polynomial hierarchy collapses to third level by applying a recent breakthrough result by Bodlaender et al. (ICALP 2008) in a non-trivial fashion. However, our positive results for {\sc Rooted $k$-Leaf-Out-Branching} immediately imply that the seemingly intractable {\sc $k$-Leaf-Out-Branching} problem admits a data reduction to $n$ independent $O(k^3)$ kernels. These two results, tractability and intractability side by side, are the first ones separating {\it many-to-one kernelization} from {\it Turing kernelization}. This answers affirmatively an open problem regarding ``cheat kernelization'' raised by Mike Fellows and Jiong Guo independently.