For the vast majority of local problems on graphs of small treewidth (where, by local we mean that a solution can be verified by checking separately the neighbourhood of each vertex), standard dynamic programming techniques give c tw | V | O(1) time algorithms, where tw is the treewidth of the input graph G = ( V,E ) and c is a constant. On the other hand, for problems with a global requirement (usually connectivity) the best–known algorithms were naive dynamic programming schemes running in at least tw tw time. We bridge this gap by introducing a technique we named Cut&Count that allows to produce c tw | V | O(1) time Monte-Carlo algorithms for most connectivity-type problems, including Hamiltonian Path , Steiner Tree , Feedback Vertex Set and Connected Dominating Set . These results have numerous consequences in various fields, like parameterized complexity, exact and approximate algorithms on planar and H -minor-free graphs and exact algorithms on graphs of bounded degree. The constant c in our algorithms is in all cases small, and in several cases we are able to show that improving those constants would cause the Strong Exponential Time Hypothesis to fail. In all these fields we are able to improve the best-known results for some problems. Also, looking from a more theoretical perspective, our results are surprising since the equivalence relation that partitions all partial solutions with respect to extendability to global solutions seems to consist of at least tw tw equivalence classes for all these problems. Our results answer an open problem raised by Lokshtanov, Marx and Saurabh [SODA’11]. In contrast to the problems aimed at minimizing the number of connected components that we solve using Cut&Count as mentioned above, we show that, assuming the Exponential Time Hypothesis, the aforementioned gap cannot be bridged for some problems that aim to maximize the number of connected components like Cycle Packing .

We study a capacitated network design problem in a geometric setting. The input consists of an integral edge capacity $k$ and two sets of points on the Euclidean plane, sources, and sinks, with an integral demand for each point. The demand of each source specifies the amount of flow that has to be shipped from the source, and the demand of each sink specifies the amount of flow that has to be shipped to the sink. The goal is to construct a minimum-length network that allows one to route the requested flow from the sources to the sinks and where each edge in the network has capacity $k$. The vertices of the network are not constrained to the sets of sinks and sources---any point on the Euclidean plane can be used as a vertex. The flow is splittable and parallel edges are allowed. The capacitated geometric network design problem generalizes, among others, the geometric Steiner tree problem, and as such it is NP-hard. We show that if the demands are polynomially bounded and the edge capacity $k$ is not too large, the single-sink capacitated geometric network design problem admits a polynomial time approximation scheme. If the capacity is arbitrarily large, then we design a quasi-polynomial time approximation scheme for the capacitated geometric network design problem allowing for an arbitrary number of sinks. Our results rely on a derivation of an upper bound on the number of vertices different from sources and sinks (the so-called Steiner vertices) in an optimal network. The bound is polynomial in the total demand of the sources.

Signed graphs, i.e., undirected graphs with edges labelled with a plus or minus sign, are commonly used to model relationships in social networks. Recently, Kermarrec and Thraves (2011) initiated the study of the problem of appropriately visualising the network: They asked whether any signed graph can be embedded into the metric space ${{\mathbb {R}}}^{l}$ in such a manner that every vertex is closer to all its friends (neighbours via positive edges) than to all its enemies (neighbours via negative edges). Interestingly, embeddability into ${{\mathbb {R}}}^{1}$ can be expressed as a purely combinatorial problem. In this paper we pursue a deeper study of this case, answering several questions posed by Kermarrec and Thraves. First, we refine the approach of Kermarrec and Thraves for the case of complete signed graphs by showing that the problem is closely related to the recognition of proper interval graphs. Second, we prove that the general case, whose polynomial-time tractability remained open, is in fact NP-complete. Finally, we provide lower and upper bounds for the time complexity of the general case: we prove that the existence of a subexponential time (in the number of vertices and edges of the input signed graph) algorithm would violate the Exponential Time Hypothesis, whereas a simple dynamic programming approach gives a running time single-exponential in the number of vertices.

In a scheduling problem, denoted by 1|prec|∑C i in the Graham notation, we are given a set of n jobs, together with their processing times and precedence constraints. The task is to order the jobs so that their total completion time is minimized. 1|prec|∑C i is a special case of the Traveling Repairman Problem with precedences. A natural dynamic programming algorithm solves both these problems in 2 n n O(1) time, and whether there exists an algorithms solving 1|prec|∑C i in O(c n ) time for some constant c<2 was an open problem posted in 2004 by Woeginger. In this paper we answer this question positively.

The 2-Disjoint Connected Subgraphs problem, given a graph along with two disjoint sets of terminals Z 1,Z 2, asks whether it is possible to find disjoint sets A 1,A 2, such that Z 1⊆A 1, Z 2⊆A 2 and A 1,A 2 induce connected subgraphs. While the naive algorithm runs in O(2 n n O(1)) time, solutions with complexity of form O((2−ε) n ) have been found only for special graph classes (van ’t Hof et al. in Theor. Comput. Sci. 410(47–49):4834–4843, 2009; Paulusma and van Rooij in Theor. Comput. Sci. 412(48):6761–6769, 2011). In this paper we present an O(1.933 n ) algorithm for 2-Disjoint Connected Subgraphs in general case, thus breaking the 2 n barrier. As a counterpoise of this result we show that if we parameterize the problem by the number of non-terminal vertices, it is hard both to speed up the brute-force approach and to find a polynomial kernel.

The classical Feedback Vertex Set problem asks, for a given undirected graph $G$ and an integer $k$, to find a set of at most $k$ vertices that hits all the cycles in the graph $G$. Feedback Vertex Set has attracted a large amount of research in the parameterized setting, and subsequent fixed-parameter and kernelization algorithms have been a rich source of ideas in the field. In this paper we consider a more general and difficult version of the problem, named Subset Feedback Vertex Set (Subset-FVS), where an instance comes additionally with a set $S \subseteq V$ of vertices, and we ask for a set of at most $k$ vertices that hits all simple cycles passing through $S$. Because of its applications in circuit testing and genetic linkage analysis, Subset-FVS was studied from the approximation algorithm perspective by Even et al. [SIAM J. Discrete Math., 13 (2000), pp. 225--267; SIAM J. Comput., 30 (2000), pp. 1231--1252]. The question of whether the Subset-FVS problem is fixed-parameter tractable was posed independently by Kawarabayashi and Saurabh in 2009. We answer this question affirmatively. We begin by showing that this problem is fixed-parameter tractable when parameterized by $|S|$. Next we present an algorithm which reduces the given instance to $2^k n^{O(1)}$ instances with the size of $S$ bounded by $O(k^3)$, using kernelization techniques such as the $2$-expansion lemma, Menger's theorem, and Gallai's theorem. These two facts allow us to give a $2^{O(k\log k)} n^{O(1)}$ time algorithm solving the Subset-FVS problem, proving that it is indeed fixed-parameter tractable.

The Pathwidth One Vertex Deletion (POVD) problem asks whether, given an undirected graph G and an integer k, one can delete at most k vertices from G so that the remaining graph has pathwidth at most 1. The question can be considered as a natural variation of the extensively studied Feedback Vertex Set (FVS) problem, where the deletion of at most k vertices has to result in the remaining graph having treewidth at most 1 (i.e., being a forest). Recently Philip et al. (WG, Lecture Notes in Computer Science, vol. 6410, pp. 196–207, 2010) initiated the study of the parameterized complexity of POVD, showing a quartic kernel and an algorithm which runs in time 7 k n O(1). In this article we improve these results by showing a quadratic kernel and an algorithm with time complexity 4.65 k n O(1), thus obtaining almost tight kernelization bounds when compared to the general result of Dell and van Melkebeek (STOC, pp. 251–260, ACM, New York, 2010). Techniques used in the kernelization are based on the quadratic kernel for FVS, due to Thomassé (ACM Trans. Algorithms 6(2), 2010).

One of the driving problems in the CSP area is the dichotomy conjecture, formulated in 1993 by Feder and Vardi [Monotone monadic SNP and constraint satisfaction, in Proceedings of the 25th Annual Symposium on Theory of Computing, ACM, New York, 1993, pp. 612--622], stating that for any fixed relational structure $\Gamma$ the constraint satisfaction problem CSP($\Gamma$) is either NP-complete or polynomial time solvable. A large amount of research has gone into checking various specific cases of this conjecture. One such variant which attracted a lot of attention in recent years is the List Matrix Partition problem. In 2004 Cameron et al. [SIAM J. Discrete Math., 21 (2007), pp. 900--929] classified almost all List Matrix Partition variants for matrices of size at most four. The only case which resisted the classification became known as the Stubborn problem. In this paper we show a result which enables us to finish the classification---thus solving a problem which resisted attacks for a few years. Our approach is based on a combinatorial problem known to be at least as hard as the Stubborn problem---the 3-Compatible Coloring problem. In this problem we are given a complete graph with each edge assigned one of three possible colors and we want to assign one of those three colors to each vertex in such a way that no edge has the same color as both of its endpoints. The tractability of the 3-Compatible Coloring problem has been open for several years and the best known algorithm prior to this paper is due to Feder et al. [Two algorithms for general list matrix partitions, in Proceedings of the 16th Annual ACM-SIAM Symposium on Discrete Algorithms, ACM, New York, SIAM, Philadelphia, 2005, pp. 870--876]---a quasipolynomial algorithm with a $n^{O(\log n / \log \log n)}$ time complexity. In this paper we present a polynomial-time algorithm for the 3-Compatible Coloring problem and consequently we prove a dichotomy for the k-Compatible Coloring problem.

For the vast majority of local problems on graphs of small tree width (where by local we mean that a solution can be verified by checking separately the neighbourhood of each vertex), standard dynamic programming techniques give c^tw |V|^O(1) time algorithms, where tw is the tree width of the input graph G = (V, E) and c is a constant. On the other hand, for problems with a global requirement (usually connectivity) the best -- known algorithms were naive dynamic programming schemes running in at least tw^tw time. We breach this gap by introducing a technique we named Cut&Count that allows to produce c^tw |V|^O(1) time Monte Carlo algorithms for most connectivity-type problems, including Hamiltonian Path, Steiner Tree, Feedback Vertex Set and Connected Dominating Set. These results have numerous consequences in various fields, like parameterized complexity, exact and approximate algorithms on planar and H-minor-free graphs and exact algorithms on graphs of bounded degree. The constant c in our algorithms is in all cases small, and in several cases we are able to show that improving those constants would cause the Strong Exponential Time Hypothesis to fail. In contrast to the problems aiming to minimize the number of connected components that we solve using Cut&Count as mentioned above, we show that, assuming the Exponential Time Hypothesis, the aforementioned gap cannot be breached for some problems that aim to maximize the number of connected components like Cycle Packing.

We present a polynomial time algorithm for the 3-Compatible colouring problem, where we are given a complete graph with each edge assigned one of 3 possible colours and we want to assign one of those 3 colours to each vertex in such a way that no edge has the same colour as both of its endpoints. Consequently we complete the proof of a dichotomy for the k-Compatible Colouring problem. The tractability of the 3-Compatible colouring problem has been open for several years and the best known algorithm prior to this paper is due to Feder et al. [SODA'05] — a quasipolynomial algorithm with a nO(log n/log log n) time complexity. Furthermore our result implies a polynomial algorithm for the Stubborn problem which enables us to finish the classification of all List Matrix Partition variants for matrices of size at most four over subsets of {0, 1} started by Cameron et al. [SODA'04].