NP-hard Graph Problems Research Articles

On algorithmic applications of sim-width and mim-width of (H1,H2)-free graphs

Mim-width and sim-width are among the most powerful graph width parameters, with sim-width more powerful than mim-width, which is in turn more powerful than clique-width. While several NP-hard graph problems become tractable for graph classes whose mim-width is bounded and quickly computable, no algorithmic applications of boundedness of sim-width are known. In Kang et al. (2017) [32], it is asked whether Independent Set and 3-Colouring are NP-complete on graphs of sim-width at most 1. We observe that, for each k∈N, Listk-Colouring is polynomial-time solvable for graph classes whose sim-width is bounded and quickly computable. Moreover, we show that if the same holds for Independent Set, then IndependentH-Packing is polynomial-time solvable for graph classes whose sim-width is bounded and quickly computable. This problem is a common generalisation of Independent Set, Induced Matching, Dissociation Set and k-Separator.We also make progress toward classifying the mim-width of (H1,H2)-free graphs in the case H1 is complete or edgeless. Our results solve some open problems in Brettell et al. (2022) [6].

Focused jump-and-repair constraint handling for fixed-parameter tractable graph problems closed under induced subgraphs

Repair operators are often used for constraint handling in constrained combinatorial optimization. We investigate the (1+1) EA equipped with a tailored jump-and-repair operation that can be used to probabilistically repair infeasible offspring in graph problems. Instead of evolving candidate solutions to the entire graph, we expand the genotype to allow the (1+1) EA to develop in parallel a feasible solution together with a growing subset of the instance (an induced subgraph). With this approach, we prove that the EA is able to probabilistically simulate an iterative compression process used in classical fixed-parameter algorithmics to obtain a randomized FPT performance guarantee on three NP-hard graph problems. For k-VertexCover, we prove that the (1+1) EA using focused jump-and-repair can find a k-vertex cover (if one exists) in O(2kn2log⁡n) iterations in expectation. This leads to an exponential (in k) improvement over the best-known parameterized bound for evolutionary algorithms on VertexCover. For the k-FeedbackVertexSet problem in tournaments, we prove that the EA finds a feasible feedback set in O(2kk!n2log⁡n) iterations in expectation, and for OddCycleTransversal, we prove the optimization time for the EA is O(3kkmn2log⁡n). For the latter two problems, this constitutes the first parameterized result for any evolutionary algorithm. We discuss how to generalize the framework to other parameterized graph problems closed under induced subgraphs and report experimental results that illustrate the behavior of the algorithm on a concrete instance class.

A Parameterized Complexity View on Collapsing k-Cores

We study the NP-hard graph problem Collapsed k-Core where, given an undirected graph G and integers b, x, and k, we are asked to remove b vertices such that the k-core of remaining graph, that is, the (uniquely determined) largest induced subgraph with minimum degree k, has size at most x. Collapsed k-Core was introduced by Zhang et al. (2017) and it is motivated by the study of engagement behavior of users in a social network and measuring the resilience of a network against user drop outs. Collapsed k-Core is a generalization of r-Degenerate Vertex Deletion (which is known to be NP-hard for all r ≥ 0) where, given an undirected graph G and integers b and r, we are asked to remove b vertices such that the remaining graph is r-degenerate, that is, every its subgraph has minimum degree at most r. We investigate the parameterized complexity of Collapsed k-Core with respect to the parameters b, x, and k, and several structural parameters of the input graph. We reveal a dichotomy in the computational complexity of Collapsed k-Core for k ≤ 2 and k ≥ 3. For the latter case it is known that for all x ≥ 0 Collapsed k-Core is W[P]-hard when parameterized by b. For k ≤ 2 we show that Collapsed k-Core is W[1]-hard when parameterized by b and in FPT when parameterized by (b + x). Furthermore, we outline that Collapsed k-Core is in FPT when parameterized by the treewidth of the input graph and presumably does not admit a polynomial kernel when parameterized by the vertex cover number of the input graph.

Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer

NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp's 21 NP-hard problems, have several applications in computational chemistry, biochemistry and computer network security. Adiabatic quantum annealers can search for the optimum value of such NP-hard optimization problems, given the problem can be embedded on their hardware. However, this is often not possible due to certain limitations of the hardware connectivity structure of the annealer. This paper studies a general framework for a decomposition algorithm for NP-hard graph problems aiming to identify an optimal set of vertices. Our generic algorithm allows us to recursively divide an instance until the generated subproblems can be embedded on the quantum annealer hardware and subsequently solved. The framework is applied to the maximum clique and minimum vertex cover problems, and we propose several pruning and reduction techniques to speed up the recursive decomposition. The performance of both algorithms is assessed in a detailed simulation study.

Solving the Maximum Clique Problem using a Hybrid Particle Swarm Optimization Algorithm

This article tackles the maximum clique problem MCP known as an NP-hard graph problem. The maximum clique problem consists in finding in an undirected graph a complete sub-graph (clique) of maximum cardinality. As the MCP is a classical graph problem extensively studied, the main contribution of this paper is to use for the first time particle swarm to solve it. A hybrid particle swarm optimization algorithm HPSOD is proposed. First a PSO algorithm is designed, based on a sub-graph extraction approach named circular-arc graph CAG, then a local search heuristic is integrated to enhance its performance. Experimental tests carried out on DIMACS benchmarks show a globally good performance of the proposed algorithm and that it outperforms many existent approaches.

More results on weighted independent domination

Weighted independent domination is an NP-hard graph problem, which remains computationally intractable in many restricted graph classes. In particular, the problem is NP-hard in the classes of sat-graphs and chordal graphs. We strengthen these results by showing that the problem is NP-hard in a proper subclass of the intersection of sat-graphs and chordal graphs. On the other hand, we identify two new classes of graphs where the problem admits polynomial-time solutions.

Boundary classes for graph problems involving non-local properties

We continue the study of boundary classes for NP-hard problems and focus on seven NP-hard graph problems involving non-local properties: Hamiltonian Cycle, Hamiltonian Cycle Through Specified Edge, Hamiltonian Path, Feedback Vertex Set, Connected Vertex Cover, Connected Dominating Set and GraphVCconDimension. Our main result is the determination of the first boundary class for Feedback Vertex Set. We also determine boundary classes for Hamiltonian Cycle Through Specified Edge and Hamiltonian Path and give some insights on the structure of some boundary classes for the remaining problems.

The Behavior of Clique-Width under Graph Operations and Graph Transformations

Clique-width is a well-known graph parameter. Many NP-hard graph problems admit polynomial-time solutions when restricted to graphs of bounded clique-width. The same holds for NLC-width. In this paper we study the behavior of clique-width and NLC-width under various graph operations and graph transformations. We give upper and lower bounds for the clique-width and NLC-width of the modified graphs in terms of the clique-width and NLC-width of the involved graphs.

On scalable parallel recursive backtracking

Supercomputers are equipped with an increasingly large number of cores to use computational power as a way of solving problems that are otherwise intractable. Unfortunately, getting serial algorithms to run in parallel to take advantage of these computational resources remains a challenge for several application domains. Many parallel algorithms can scale to only hundreds of cores. The limiting factors of such algorithms are usually communication overhead and poor load balancing. Solving NP-hard graph problems to optimality using exact algorithms is an example of an area in which there has so far been limited success in obtaining large scale parallelism. Many of these algorithms use recursive backtracking as their core solution paradigm. In this paper, we propose a lightweight, easy-to-use, scalable approach for transforming almost any recursive backtracking algorithm into a parallel one. Our approach incurs minimal communication overhead and guarantees a load-balancing strategy that is implicit, i.e., does not require any problem-specific knowledge. The key idea behind our approach is the use of efficient traversal operations on an indexed search tree that is oblivious to the problem being solved. We test our approach with parallel implementations of algorithms for the well-known Vertex Cover and Dominating Set problems. On sufficiently hard instances, experimental results show nearly linear speedups for thousands of cores, reducing running times from days to just a few minutes.

Variable neighbourhood search for the k-labelled spanning forest problem

In this paper we present an efficient Variable Neighbourhood Search for the k-labelled spanning forest (kLSF) problem, an NP-hard graph problem related to the minimum labelling spanning tree problem, and with strong implications in multimodal transportation networks. Given an undirected input graph whose edges are labelled, and an integer positive value k¯, the aim is to find a spanning forest of the input graph having the minimum number of connected components and the upper bound k¯ on the number of labels to use.

A new augmentation based algorithm for extracting maximal chordal subgraphs

A graph is chordal if every cycle of length greater than three contains an edge between non-adjacent vertices. Chordal graphs are of interest both theoretically, since they admit polynomial time solutions to a range of NP-hard graph problems, and practically, since they arise in many applications including sparse linear algebra, computer vision, and computational biology. A maximal chordal subgraph is a chordal subgraph that is not a proper subgraph of any other chordal subgraph. Existing algorithms for computing maximal chordal subgraphs depend on dynamically ordering the vertices, which is an inherently sequential process and therefore limits the algorithms’ parallelizability.In this paper we explore techniques to develop a scalable parallel algorithm for extracting a maximal chordal subgraph. We demonstrate that an earlier attempt at developing a parallel algorithm may induce a non-optimal vertex ordering and is therefore not guaranteed to terminate with a maximal chordal subgraph. We then give a new algorithm that first computes and then repeatedly augments a spanning chordal subgraph. After proving that the algorithm terminates with a maximal chordal subgraph, we then demonstrate that this algorithm is more amenable to parallelization and that the parallel version also terminates with a maximal chordal subgraph. That said, the complexity of the new algorithm is higher than that of the previous parallel algorithm, although the earlier algorithm computes a chordal subgraph which is not guaranteed to be maximal.We experimented with our augmentation-based algorithm on both synthetic and real-world graphs. We provide scalability results and also explore the effect of different choices for the initial spanning chordal subgraph on both the running time and on the number of edges in the maximal chordal subgraph.

Roulette Wheel Selection based Heuristic Algorithm for the Orienteering Problem

Orienteering problem (OP) is an NP-Hard graph problem. The nodes of the graph are associated with scores or rewards and the edges with time delays. The goal is to obtain a Hamiltonian path connecting the two necessary check points, i.e. the source and the target along with a set of control points such that the total collected score is maximized within a specified time limit. OP finds application in several fields like logistics, transportation networks, tourism industry, etc. Most of the existing algorithms for OP can only be applied on complete graphs that satisfy the triangle inequality. Real-life scenario does not guarantee that there exists a direct link between all control point pairs or the triangle inequality is satisfied. To provide a more practical solution, we propose a stochastic greedy algorithm (RWS_OP) that uses the roulette wheel selectionmethod, does not require that the triangle inequality condition is satisfied and is capable of handling both complete as well as incomplete graphs. Based on several experiments on standard benchmark data we show that RWS_OP is faster, more efficient in terms of time budget utilization and achieves a better performance in terms of the total collected score ascompared to a recently reported algorithm for incomplete graphs.

Confluence in Data Reduction: Bridging Graph Transformation and Kernelization

Kernelization is a core tool of parameterized algorithmics for coping with computationally intractable problems. A kernelization reduces in polynomial time an input instance to an equivalent instance whose size is bounded by a function only depending on some problem-specific parameter k; this new instance is called problem kernel. Typically, problem kernels are achieved by performing efficient data reduction rules. So far, there was little study in the literature concerning the mutual interaction of data reduction rules, in particular whether data reduction rules for a specific problem always lead to the same reduced instance, no matter in which order the rules are applied. This corresponds to the concept of confluence from the theory of rewriting systems. We argue that it is valuable to study whether a kernelization is confluent, using the NP-hard graph problems (Edge) Clique Cover and Partial Clique Cover as running examples. We apply the concept of critical pair analysis from graph transformation theory, supported by the AGG software tool. These results support the main goal of our work, namely, to establish a fruitful link between (parameterized) algorithmics and graph transformation theory, two so far unrelated fields.

Colorings with few Colors: Counting, Enumeration and Combinatorial Bounds

Edge coloring, total coloring and L(2,1)-labeling are well-studied NP-hard graph problems. Even the versions asking whether a graph has a coloring with few colors or a labeling with few labels remain NP-hard on graphs of small maximum degree. This paper studies enumeration and counting problems on edge colorings, total colorings and L(2,1)-labelings of graphs. One part deals with the enumeration of all edge 3-colorings, all total 4-colorings and all L(2,1)-labelings of span 5 of a given connected cubic graph. Branching algorithms to solve these enumeration problems are established. They imply upper bounds on the maximum number of edge 3-colorings, total 4-colorings and L(2,1)-labelings of span 5 in any n-vertex connected cubic graphs. Corresponding combinatorial lower bounds are also provided. The other part of the paper studies dynamic programming algorithms solving counting problems. On one hand, algorithms to count the number of edge k-colorings and total k-colorings for graphs of bounded pathwidth are given. On the other hand, an algorithm to count the number of L(2,1)-labelings of span 4 for graphs of maximum degree three are given.

Inapproximability Results for Maximum Edge Biclique, Minimum Linear Arrangement, and Sparsest Cut

We consider the Minimum Linear Arrangement problem and the (Uniform) Sparsest Cut problem. So far, these two notorious NP-hard graph problems have resisted all attempts to prove inapproximability results. We show that they have no polynomial time approximation scheme, unless NP-complete problems can be solved in randomized subexponential time. Furthermore, we show that the same techniques can be used for the Maximum Edge Biclique problem, for which we obtain a hardness factor similar to previous results but under a more standard assumption.

Improving an upper bound on the size of k-regular induced subgraphs

This paper considers the NP-hard graph problem of determining a maximum cardinality subset of vertices inducing a k-regular subgraph. For any graph G, this maximum will be denoted by ? k (G). From a well known Motzkin-Straus result, a relationship is deduced between ? k (G) and the independence number ?(G). Next, it is proved that the upper bounds ? k (G) introduced in Cardoso et al. (J. Comb. Optim., 14, 455---463, 2007) can easily be computed from ? 0(G), for any positive integer k. This relationship also allows one to present an alternative proof of the Hoffman bound extension introduced in the above paper. The paper continues with the introduction of a new upper bound on ? k (G) improving ? k (G). Due to the difficulty of computing this improved bound, two methods are provided for approximating it. Finally, some computational experiments which were performed to compare all bounds studied are reported.

Variable neighbourhood search for the minimum labelling Steiner tree problem

We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running times.

Finding a dominating set on bipartite graphs

Finding a dominating set of minimum cardinality is an NP-hard graph problem, even when the graph is bipartite. In this paper we are interested in solving the problem on graphs having a large independent set. Given a graph G with an independent set of size z, we show that the problem can be solved in time O ∗ ( 2 n − z ) , where n is the number of vertices of G. As a consequence, our algorithm is able to solve the dominating set problem on bipartite graphs in time O ∗ ( 2 n / 2 ) . Another implication is an algorithm for general graphs whose running time is O ( 1.7088 n ) .

The Bidimensionality Theory and Its Algorithmic Applications

Our newly developing theory of bidimensional graph problems provides general techniques for designing efficient fixed-parameter algorithms and approximation algorithms for NP-hard graph problems in broad classes of graphs. This theory applies to graph problems that are bidimensional in the sense that (1) the solution value for the k × k grid graph (and similar graphs) grows with k, typically as Ω(k2), and (2) the solution value goes down when contracting edges and optionally when deleting edges. Examples of such problems include feedback vertex set, vertex cover, minimum maximal matching, face cover, a series of vertex-removal parameters, dominating set, edge dominating set, r-dominating set, connected dominating set, connected edge dominating set, connected r-dominating set, and unweighted TSP tour (a walk in the graph visiting all vertices). Bidimensional problems have many structural properties; for example, any graph embeddable in a surface of bounded genus has treewidth bounded above by the square root of the problem's solution value. These properties lead to efficient---often subexponential---fixed-parameter algorithms, as well as polynomial-time approximation schemes, for many minor-closed graph classes. One type of minor-closed graph class of particular relevance has bounded local treewidth, in the sense that the treewidth of a graph is bounded above in terms of the diameter; indeed, we show that such a bound is always at most linear. The bidimensionality theory unifies and improves several previous results. The theory is based on algorithmic and combinatorial extensions to parts of the Robertson-Seymour Graph Minor Theory, in particular initiating a parallel theory of graph contractions. The foundation of this work is the topological theory of drawings of graphs on surfaces and our results regarding the relation (the linearity) of the size of the largest grid minor in terms of treewidth in bounded-genus graphs and more generally in graphs excluding a fixed graph H as a minor. In this thesis, we also develop the algorithmic theory of vertex separators, and its relation to the embeddings of certain metric spaces. Unlike in the edge case, we show that embeddings into L1 (and even Euclidean embeddings) are insufficient, but that the additional structure provided by many embedding theorems does suffice for our purposes. We obtain an O( logn ) approximation for min-ratio vertex cuts in general graphs, based on a new semidefinite relaxation of the problem, and a tight analysis of the integrality gap which is shown to be Θ( logn ). We also prove various approximate max-flow/min-vertex-cut theorems, which in particular give a constant-factor approximation for min-ratio vertex cuts in any excluded-minor family of graphs. Previously, this was known only for planar graphs, and for general excluded-minor families the best-known ratio was O(log n). These results have a number of applications. We exhibit an O( logn ) pseudo-approximation for finding balanced vertex separators in general graphs. Furthermore, we obtain improved approximation ratios for treewidth: In any graph of treewidth k, we show how to find a tree decomposition of width at most O(k logk ), whereas previous algorithms yielded O( k log k). For graphs excluding a fixed graph as a minor, we give a constant-factor approximation for the treewidth; this via the bidimensionality theory can be used to obtain the first polynomial-time approximation schemes for problems like minimum feedback vertex set and minimum connected dominating set in such graphs. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

NP-hard graph problems and boundary classes of graphs

Any graph problem, which is NP-hard in general graphs, becomes polynomial-time solvable when restricted to graphs in special classes. When does a difficult problem become easy? To answer this question we study the notion of boundary classes. In the present paper we define this notion in its most general form, describe several approaches to identify boundary classes and apply them to various graph problems.