We consider the Steiner Orientation problem, where we are given as input a mixed graph G = (V,E,A) and a set of k demand pairs (s_i,t_i), i ∈ [k]. The goal is to orient the undirected edges of G in a way that the resulting directed graph has a directed path from s_i to t_i for all i ∈ [k]. We adopt the point of view of structural parameterized complexity and investigate the complexity of Steiner Orientation for standard measures, such as treewidth. Our results indicate that Steiner Orientation is a surprisingly hard problem from this point of view. In particular, our main contributions are the following: 1) We show that Steiner Orientation is NP-complete on instances where the underlying graph has feedback vertex number 2, treewidth 2, pathwidth 3, and vertex integrity 6. 2) We present an XP algorithm parameterized by vertex cover number vc of complexity n^O(vc²). Furthermore, we show that this running time is essentially optimal by proving that a running time of n^o(vc²) would refute the ETH. 3) We consider parameterizations by the number of undirected or directed edges (|E| or |A|) and we observe that the trivial 2^|E| n^O(1)-time algorithm for the former parameter is optimal under the SETH. Complementing this, we show that the problem admits a 2^O(|A|) n^O(1)-time algorithm. In addition to the above, we consider the complexity of Steiner Orientation parameterized by tw+k (FPT), distance to clique (FPT), and vc+k (FPT with a polynomial kernel).

The uniqueness of an optimal solution to a combinatorial optimization problem attracts many fields of researchers' attention because it has a wide range of applications, it is related to important classes in computational complexity, and the existence of only one solution is often critical for algorithm designs in theory. However, as the authors know, there is no major benchmark set consisting of only instances with unique solutions, and no algorithm generating instances with unique solutions is known; a systematic approach to getting a problem instance guaranteed having a unique solution would be helpful. A possible approach is as follows: Given a problem instance, we specify a small part of a solution in advance so that only one optimal solution meets the specification. This paper formulates such a ``pre-assignment'' approach for the vertex cover problem as a typical combinatorial optimization problem and discusses its computational complexity. First, we show that the problem is ΣP2-complete in general, while the problem becomes NP-complete when an input graph is bipartite. We then present an O(2.1996^n)-time algorithm for general graphs and an O(1.9181^n)-time algorithm for bipartite graphs, where n is the number of vertices. The latter is based on an FPT algorithm with O*(3.6791^τ) time for vertex cover number τ. Furthermore, we show that the problem for trees can be solved in O(1.4143^n) time.

The problem of computing the longest common subsequence of two sequences ( LCS for short) is a classical and fundamental problem in computer science. In this article, we study four variants of LCS : the Repetition-Bounded Longest Common Subsequence problem ( RBLCS ), the Multiset-Restricted Common Subsequence problem ( MRCS ), the Two-Side-Filled Longest Common Subsequence problem ( 2FLCS ), and the One-Side-Filled Longest Common Subsequence problem ( 1FLCS ). Although the original LCS can be solved in polynomial time, all these four variants are known to be NP-hard. Recently, an exact, O ( 1 . 4422 5 n ) -time, dynamic programming (DP) based algorithm for RBLCS was proposed, where the two input sequences have lengths n and p o l y ( n ) . Here, we first establish that each of MRCS , 1FLCS , and 2FLCS is polynomially equivalent to RBLCS . Then, we design a refined DP-based algorithm for RBLCS that runs in O ( 1 . 4142 2 n ) time, which implies that MRCS , 1FLCS , and 2FLCS can also be solved in O ( 1 . 4142 2 n ) time. Finally, we give a polynomial-time 2-approximation algorithm for 2FLCS .

Abstract We consider a graph orientation problem that can be viewed as a generalization of Minimum Graph Coloring. Our problem takes as input an undirected graph $$G = (V, E)$$ G = ( V , E ) in which every edge $$\{u, v\} \in E$$ { u , v } ∈ E has two (potentially different and not necessarily positive) weights representing the lengths of its two possible directions ( u , v ) and ( v , u ), and asks for an orientation, i.e., an assignment of a direction to each edge of G , such that the length of a longest simple directed path in the resulting directed graph is minimized. A longest path in a graph is not always a maximal path when some edges have negative lengths, so the problem has two variants depending on whether all simple directed paths or maximal simple directed paths only are taken into account in the definition. We prove that the problems are NP-hard to approximate even if restricted to subcubic planar graphs, and develop fast polynomial-time algorithms for both problem variants for three classes of graphs: path graphs, cycle graphs, and star graphs.

Abstract Given a graph $$G = (V,E)$$ G = ( V , E ) , $$A \subseteq V$$ A ⊆ V , and integers k and $$\ell $$ ℓ , the $$(A,\ell )$$ ( A , ℓ ) -Path Packing problem asks to find k vertex-disjoint paths of length exactly $$\ell $$ ℓ that have endpoints in A and internal points in $$V{\setminus }A$$ V \ A . We study the parameterized complexity of this problem with parameters |A|, $$\ell $$ ℓ , k, treewidth, pathwidth, and their combinations. We present sharp complexity contrasts with respect to these parameters. Among other results, we show that the problem is polynomial-time solvable when $$\ell \le 3$$ ℓ ≤ 3 , while it is NP-complete for constant $$\ell \ge 4$$ ℓ ≥ 4 . We also show that the problem is W[1]-hard parameterized by pathwidth $${}+|A|$$ + | A | , while it is fixed-parameter tractable parameterized by treewidth $${}+\ell $$ + ℓ . Additionally, we study a variant called Short A-Path Packing that asks to find k vertex-disjoint paths of length at most $$\ell $$ ℓ . We show that all our positive results on the exact-length version can be translated to this version and show the hardness of the cases where |A| or $$\ell $$ ℓ is a constant.

Given a sequence of integers, we want to find a longest increasing subsequence of the sequence. It is known that this problem can be solved in \(O\left (n \log n\right )\) time and space. Our goal in this paper is to reduce the space consumption while keeping the time complexity small. For \(\sqrt {n} \le s \le n\) , we present algorithms that use \(O\left (s \log n\right )\) bits and \(O\left (\frac {1}{s} \cdot n^{2} \cdot \log n\right )\) time for computing the length of a longest increasing subsequence, and \(O\left (\frac {1}{s} \cdot n^{2} \cdot \log ^{2} n\right )\) time for finding an actual subsequence. We also show that the time complexity of our algorithms is optimal up to polylogarithmic factors in the framework of sequential access algorithms with the prescribed amount of space.

In this paper, we study exact, exponential-time algorithms for a variant of the classic Longest Common Subsequence problem called the Repetition-Bounded Longest Common Subsequence problem (or RBLCS , for short): Let an alphabet S be a finite set of symbols and an occurrence constraint C o c c be a function C o c c : S → N , assigning an upper bound on the number of occurrences of each symbol in S . Given two sequences X and Y over the alphabet S and an occurrence constraint C o c c , the goal of RBLCS is to find a longest common subsequence of X and Y such that each symbol s ∈ S appears at most C o c c ( s ) times in the obtained subsequence. The special case where C o c c ( s ) = 1 for every symbol s ∈ S is known as the Repetition-Free Longest Common Subsequence problem ( RFLCS ) and has been studied previously; e.g., in [1] , Adi et al. presented a simple (exponential-time) exact algorithm for RFLCS . However, they did not analyze its time complexity in detail, and to the best of our knowledge, there are no previous results on the running times of any exact algorithms for this problem. Without loss of generality, we will assume that | X | ≤ | Y | and | X | = n . In this paper, we first propose a simpler algorithm for RFLCS based on the strategy used in [1] and show explicitly that its running time is O ( 1.44225 n ) . Next, we provide a dynamic programming (DP) based algorithm for RBLCS and prove that its running time is O ( 1.44225 n ) for any occurrence constraint C o c c , and even less in certain special cases. In particular, for RFLCS , our DP-based algorithm runs in O ( 1.41422 n ) time, which is faster than the previous one. Furthermore, we prove NP-hardness and APX-hardness results for RBLCS on restricted instances.

A pseudoforest is a graph where each connected component contains at most one cycle, or alternatively, a graph that can be turned into a forest by removing at most one edge from each connected component. In this paper, we show that the following problem can be solved in O(3^k n k^{O(1)}) time: given a graph G and an integer k, can we delete at most k vertices from G such that we obtain a pseudoforest? The result improves upon an earlier result by Philip et al. [MFCS 2015] who gave a (nonlinear) 7.56^k n^{O(1)}-time algorithm both in the exponential factor depending on k as well as in the polynomial factor depending on n.

In this paper, we study covering and domination problems on directed graphs. Although undirected Vertex Cover and Edge Dominating Set are well-studied classical graph problems, the directed versions have not been studied much due to the lack of clear definitions. We give natural definitions for Directed r-In (Out) Vertex Cover and Directed (p,q)-Edge Dominating Set as directed generations of Vertex Cover and Edge Dominating Set. For these problems, we show that (1) Directed r-In (Out) Vertex Cover and Directed (p,q)-Edge Dominating Set are NP-complete on planar directed acyclic graphs except when r=1 or (p,q)=(0,0), (2) if r>=2, Directed r-In (Out) Vertex Cover is W[2]-hard and (c*ln k)-inapproximable on directed acyclic graphs, (3) if either p or q is greater than 1, Directed (p,q)-Edge Dominating Set is W[2]-hard and (c*ln k)-inapproximable on directed acyclic graphs, (4) all problems can be solved in polynomial time on trees, and (5) Directed (0,1),(1,0),(1,1)-Edge Dominating Set are fixed-parameter tractable in general graphs. The first result implies that (directed) r-Dominating Set on directed line graphs is NP-complete even if r=1.

In the economic activities, the central bank has an important role to cover payments of banks, when they are short of funds to clear their debts. For this purpose, the central bank timely puts funds so that the economic activities go smooth. Since payments in this mechanism are processed sequentially, the total amount of funds put by the central bank critically depends on the order of the payments. Then an interest goes to the amount to prepare if the order of the payments can be controlled by the central bank, or if it is determined under the worst case scenario. This motivates us to introduce a brand-new problem, which we call the settlement fund circulation problem. The problems are formulated as follows: Let G=(V,A) be a directed multigraph with a vertex set V and an arc set A. Each arc a\in A is endowed debt d(a)\ge 0, and the debts are settled sequentially under a sequence \pi of arcs. Each vertex v\in V is put fund in the amount of p_{\pi}(v)\ge 0 under the sequence. The minimum/maximum settlement fund circulation problem (Min-SFC/Max-SFC) in a given graph G with debts d: A\rightarrow \mathbb{R}_{+}\cup \{0\} asks to find a bijection \pi:A\to \{1,2,\dots,|A|\} that minimizes/maximizes the total funds \sum _{v\in V}p_{\pi }(v). In this paper, we show that both Min-SFC and Max-SFC are NP-hard; in particular, Min-SFC is (I) strongly NP-hard even if G is (i) a multigraph with |V|=2 or (ii) a simple graph with treewidth at most two,and is (II) (not necessarily strongly) NP-hard for simple trees of diameter four, while it is solvable in polynomial time for stars. Also, we identify several polynomial time solvable cases for both problems.

The problem MaxW-Light (MaxW-Heavy) for an undirected graph is to assign a direction to each edge so that the number of vertices of outdegree at most W (resp. at least W) is maximized. It is known that these problems are NP-hard even for fixed W. For example, Max 0-Light is equivalent to the problem of finding a maximum independent set. In this paper, we show that for any fixed constant W, MaxW-Heavy can be solved in linear time for hereditary graph classes for which treewidth is bounded by a function of degeneracy. We show that such graph classes include chordal graphs, circular-arc graphs, d-trapezoid graphs, chordal bipartite graphs, and graphs of bounded clique-width. To have a polynomial-time algorithm for MaxW-Light, we need an additional condition of a polynomial upper bound on the number of potential maximal cliques to apply the metatheorem by Fomin et al. (SIAM J Comput 44:54–87, 2015). The aforementioned graph classes, except bounded clique-width graphs, satisfy such a condition. For graphs of bounded clique-width, we present a dynamic programming approach not using the metatheorem to show that it is actually polynomial-time solvable for this graph class too. We also study the parameterized complexity of the problems and show some tractability and intractability results.

A code design problem for memory devices with restricted state transitions is formulated as a combinatorial optimization problem that is called a subgraph domatic partition (subDP) problem. If any neighbor set of a given state transition graph contains all the colors, then the coloring is said to be valid. The goal of a subDP problem is to find the valid coloring that has the largest number of colors for a subgraph of a given directed graph. The number of colors in an optimal valid coloring indicates the writing capacity of that state transition graph. The subDP problems are computationally hard; it is proved to be NP-complete in this paper. One of our main contributions in this paper is to show the asymptotic behavior of the writing capacity C(G) for sequences of dense bidirectional graphs; this is given by C(G) = Ω(n/ ln n), where n is the number of nodes. A probabilistic method, Lovász local lemma (LLL), plays an essential role in deriving the asymptotic expression.

We investigate pattern formation, i.e., self-organization, by a swarm of mobile robots, which is closely related with the agreement problem in distributed computing. Consider a system of anonymous mobile robots in a 2-dimensional Euclidean space in which each robot repeatedly executes a “Look-Compute-Move” cycle, to observe the positions of all the robots, to compute a route to the next position using an algorithm, and then to trace the route, where the algorithm is common to all robots. The robots are said to be fully synchronous if their Look-Compute-Move cycles are completely synchronized, and the $i$th Look, Compute, and Move of all robots start and end simultaneously. They are said to be asynchronous if no assumptions are made on their synchrony. The robots are said to be oblivious if they have no memory to memorize the execution history and hence behave based only on the robots' positions observed during the immediately preceding Look. We show that the set of geometric patterns formable by oblivious asynchronous robots is exactly the set of those formable by nonoblivious fully synchronous robots, except for a point of multiplicity 2, i.e., gathering for two robots. In short, contrary to our intuition, synchrony and memory do not help in pattern formation, except for gathering. Specifically, we propose an algorithm for oblivious asynchronous robots that, given a geometric pattern as input, forms it, as long as it is formable by nonoblivious fully synchronous robots except for a point of multiplicity 2.

The Local/Global Alignment (Zemla, 2003), or LGA, is a popular method for the comparison of protein structures. One of the two components of LGA requires us to compute the longest common contiguous segments between two protein structures. That is, given two structures A = (a1, ... ,a(n)) and B = (b1, ... ,b(n)) where a(k), b(k) ∈ ℝ(3), we are to find, among all the segments f = (a(i), ... ,a(j)) and g = (b(i), ... ,b(j)) that fulfill a certain criterion regarding their similarity, those of the maximum length. We consider the following criteria: (1) the root mean squared deviation (RMSD) between f and g is to be within a given t ∈ ℝ; (2) f and g can be superposed such that for each k, i ≤ k ≤ j, ||a(k) - b(k)|| ≤ t for a given t ∈ ℝ. We give an algorithm of O(n log n + nl) time complexity when the first requirement applies, where l is the maximum length of the segments fulfilling the criterion. We show an FPTAS which, for any ϵ ∈ ℝ, finds a segment of length at least l, but of RMSD up to (1 + ϵ)t, in O(n log n + n/ϵ) time. We propose an FPTAS which for any given ϵ ∈ R, finds all the segments f and g of the maximum length which can be superposed such that for each k, i ≤ k ≤ j, ||a(k) - b(k)|| ≤ (1 + ϵ)t, thus fulfilling the second requirement approximately. The algorithm has a time complexity of O(n log(2) n/ϵ(5)) when consecutive points in A are separated by the same distance (which is the case with protein structures). These worst-case runtime complexities are verified using C++ implementations of the algorithms, which we have made available at http://alcs.sourceforge.net/.

In a coalescing random walk, a set of particles make independent discrete-time random walks on a graph. Whenever one or more particles meet at a vertex, they unite to form a single particle, which then continues a random walk through the graph. Let $G=(V,E)$ be an undirected and connected graph with $n$ vertices and $m$ edges. The coalescence time, $C(n)$, is the expected time for all particles to coalesce, when initially one particle is located at each vertex. We study the problem of bounding the coalescence time for general connected graphs and prove that $C(n) = O\big(\frac{1}{1-\lambda_2}\big(\log^{4} n + \frac{n}{\nu}\big)\big)$. Here $\lambda_2$ is the second eigenvalue of the transition matrix of the random walk. To avoid problems arising from, e.g., lack of coalescence on bipartite graphs, we assume the random walk can be made lazy if required. The value of $\nu$ is given by $\nu= \sum_{v\in V} d^2(v)/(d^2n)$, where $d(v)$ is the degree of vertex $v$, and $d=2m/n$ is the average degree. The parameter $\nu$ is an indicator of the variability of vertex degrees: $1 \le \nu = O(n)$, with $\nu=1$ for regular graphs. Our general bound on $C(n)$ holds for all connected graphs. This implies, for example, that $C(n)=O(n/(1-\lambda_2))$ for $d$-regular graphs with expansion parameterized by the eigenvalue gap $1-\lambda_2$. The bound on $C(n)$ given above is sublinear for some classes of graphs with skewed degree distributions. In the voter model, initially each vertex has a distinct opinion, and at each step each vertex changes its opinion to that of a random neighbor. Let ${\mathbf{E}} (C_{{\mbox{\boldmath$v$}}})$ be the expected time for voting to complete, that is, for a unique opinion to emerge. A system of coalescing particles, where initially one particle is located at each vertex, corresponds to the voter model in that $\mathbf{E}(C_{{\mbox{\boldmath$v$}}})=C(n)$. Thus our result stated above for $C(n)$ also gives general bounds for $\mathbf{E}(C_{{\mbox{\boldmath$v$}}})$.

In a coalescing random walk, a set of particles make independent discrete-time random walks on a graph. Whenever one or more particles meet at a vertex, they unite to form a single particle, which then continues the random walk through the graph. Coalescing random walks can be used to achieve consensus in distributed networks, and is the basis of the self-stabilizing mutual exclusion algorithm of Israeli and Jalfon [14].

In this article, we investigate deductive inference for interiors and exteriors of Horn knowledge bases, where interiors and exteriors were introduced by Makino and Ibaraki [1996] to study stability properties of knowledge bases. We present a linear time algorithm for deduction for interiors and show that deduction is coNP-complete for exteriors. Under model-based representation, we show that the deduction problem for interiors is NP-complete while the one for exteriors is coNP-complete. As for Horn envelopes of exteriors, we show that it is linearly solvable under model-based representation, while it is coNP-complete under formula-based representation. We also discuss polynomially solvable cases for all the intractable problems.

We study a new variant of the graph orientation problem called MAXMINO where the input is an undirected, edge-weighted graph and the objective is to assign a direction to each edge so that the minimum weighted outdegree (taken over all vertices in the resulting directed graph) is maximized. All edge weights are assumed to be positive integers. This problem is closely related to the job scheduling on parallel machines, called the machine covering problem, where its goal is to assign jobs to parallel machines such that each machine is covered as much as possible. First, we prove that MAXMINO is strongly NP-hard and cannot be approximated within a ratio of 2 = ¿ for constant ¿ > 0 in polynomial time unless P = NP, even if all edge weights belong to {2}, every vertex has degree at most three, and the input graph is bipartite or planar. Next, we show how to solve MAXMINO exactly in polynomial time for the special case in which all edge weights are equal to 1. This technique gives us a simple polynomial-time wmax/wmin- approximation algorithm for MAXMINO where wmaxand wmindenote the maximum and minimum weights among all the input edges. Furthermore we also observe that this approach yields an exact algorithm for the general case of MAXMINO whose running time is polynomial whenever the number of edges having weight larger than wminis at most logarithmic in the number of vertices. Finally, we, show that MAXMINO is solvable in polynomial time if the input is a cactus graph.

It is known that the leader election in anonymous networks is not always solvable, and solvable/unsolvable cases are characterized by the network topologies. Therefore a distributed leader election algorithm is required to elect a leader when it is possible, otherwise recognize the impossibility and stop. Although former studies proposed several leader election algorithms, the space complexity of the problem is not considered well. This paper focuses on the space complexity, that is, the necessary or sufficient number of bits on processors to execute a leader election algorithm. First we show that only one bit memory is sufficient for a leader election algorithm which is specific to a fixed n. We then show that a general algorithm can solve the leader election for arbitrary n if each processor has O(n log d) bits memory where d is the maximum degree of a processor. Finally, we give a lower bound Ω(log n) on the space complexity, that is, we show that it is impossible to construct a leader election algorithm if only log n bits are available for a processor.