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1989, Information Processing Letters
AI
The paper introduces a decomposition strategy for the minimum weight vertex cover problem (WVC) on graphs. It leverages the relationship between vertex covers and stable sets, focusing on how to break down the WVC into smaller, manageable subproblems defined on induced subgraphs of the original graph. By controlling the number of these subproblems and ensuring each can be solved in polynomial time, the method aims to provide an efficient way to find the optimal solution to WVC for specific classes of graphs, including certain triangulated and circular graphs.
Lecture Notes in Computer Science, 2015
A complete graph is the graph in which every two vertices are adjacent. For a graph G = (V, E), the complete width of G is the minimum k such that there exist k independent sets N i ⊆ V , 1 ≤ i ≤ k, such that the graph G ′ obtained from G by adding some new edges between certain vertices inside the sets N i , 1 ≤ i ≤ k, is a complete graph. The complete width problem is to decide whether the complete width of a given graph is at most k or not. In this paper we study the complete width problem. We show that the complete width problem is NP-complete on 3K 2-free bipartite graphs and polynomially solvable on 2K 2-free bipartite graphs and on (2K 2 , C 4)-free graphs. As a by-product, we obtain the following new results: the edge clique cover problem is NP-complete on 3K 2-free co-bipartite graphs and polynomially solvable on C 4-free co-bipartite graphs and on (2K 2 , C 4)-free graphs. We also give a characterization for k-probe complete graphs which implies that the complete width problem admits a kernel of at most 2 k vertices. This provides another proof for the known fact that the edge clique cover problem admits a kernel of at most 2 k vertices. Finally we determine all graphs of small complete width k ≤ 3.
Indagationes Mathematicae (Proceedings), 1977
Theoretical Computer Science, 2007
Clique separators in graphs are a helpful tool used by Tarjan as a divide-and-conquer approach for solving various graph problems such as the Maximum Weight Stable Set (MWS) Problem, Maximum Clique, Graph Coloring and Minimum Fill-in, but few examples of graph classes having clique separators are known. We use this method to solve MWS in polynomial time for two classes where the unweighted Maximum Stable Set (MS) Problem is solvable in polynomial time by augmenting techniques but the complexity of the MWS problem was open. Another example, namely a result by Alekseev for the MWS problem on a subclass of P 5-free graphs obtained by clique separators, can be improved by our techniques. We also combine clique separators with decomposition by homogeneous sets in graphs and use the following notion: A graph is nearly Π if for each of its vertices, the subgraph induced by the set of its nonneighbors has property Π. We deal with the cases Π ∈ {chordal, perfect}. This also simplifies a result obtained by a method called struction.
Theory of Computing Systems, 2020
Let be a family of graphs. In the classical-VERTEX DELETION problem, given a graph G and a positive integer k, the objective is to check whether there exists a subset S of at most k vertices such that G − S is in. In this paper, we introduce the conflict free version of this classical problem, namely CONFLICT FREE-VERTEX DELETION (CF-VD), and study this problem from the viewpoint of classical and parameterized complexity. In the CF-VD problem, given two graphs G and H on the same vertex set and a positive integer k, the objective is to determine whether there exists a set S ⊆ V (G), of size at most k, such that G − S is in and H [S] is edgeless. Initiating a systematic study of these problems is one of the main conceptual contribution of this work. We obtain several results on the conflict free versions of several classical problems. Our first result shows that if is characterized by a finite family of forbidden induced subgraphs then CF-VD is Fixed Parameter Tractable (FPT). Furthermore, we obtain improved algorithms for conflict free versions of several well studied problems. Next, we show that if is characterized by a "well-behaved" infinite family of forbidden induced subgraphs, then CF-VD is W[1]-hard. Motivated by this hardness result, we consider the parameterized complexity of CF-VD when H is restricted to well studied families of graphs. In particular, we show that the conflict free version of several well-known problems such as FEEDBACK VERTEX SET, ODD CYCLE TRANSVERSAL, CHORDAL VER-TEX DELETION and INTERVAL VERTEX DELETION are FPT when H belongs to the families of d-degenerate graphs and nowhere dense graphs.
A γ-quasi-clique in a simple undirected graph is a set of vertices which induces a subgraph with the edge density of at least γ for 0 < γ < 1. A cover of a graph by γ-quasi-cliques is a set of γ-quasi-cliques where each edge of the graph is contained in at least one quasi-clique. The minimum cover by γ-quasi-cliques problem asks for a γ-quasi-clique cover with the minimum number of quasi-cliques. A partition of a graph by γ-quasi-cliques is a set of γ-quasi-cliques where each vertex of the graph belongs to exactly one quasi-clique. The minimum partition by γ-quasicliques problem asks for a vertex partition by γ-quasi-cliques with the minimum number of quasicliques. We show that the decision versions of the minimum cover and partition by γ-quasi-cliques problems are NP-complete for any fixed γ satisfying 0 < γ < 1.
Algorithms and Combinatorics, 1993
In this chapter we survey the results of the polyhedral approach to a particular %&-hard combinatorial optimization problem, the stable set problem in graphs. (Alternative names for this problem used in the literature are vertex packing, or coclique, or independent set problem.) Our basic technique will be to look for various classes of inequalities valid for the stable set polytope, and then develop polynomial time algorithms to check if a given vector satisfies all these constraints. Such an algorithm solves a relaxation of the stable set problem in polynomial time, i. e., provides an upper bound for the maximum weight of a stable set. If certain graphs have the property that every facet of the stable set polytope occurs in the given family of valid inequalities, then, for these graphs, the stable set problem can be solved in polynomial time. It turns out that there are very interesting classes of graphs which are in fact characterized by such a condition, most notably the class of perfect graphs. Using this approach, we shall develop a polynomial time algorithm for the stable set problem for perfect graphs. So far no purely combinatorial algorithm has been found to solve this problem in polynomial time. Let us mention that all algorithms presented in this chapter can be made strongly polynomial using Theorem (6.6.5), with the natural exception of the algorithm designed to prove Theorem (9.3.30), which optimizes a linear objective function over a non polyhedral set.
Journal of Graph Theory
Throughout, G = (V , E) refers to a simple loopless graph and n denotes the number of vertices in G when the graph in question is clear. Standard graph theory notation is used throughout, but we make the following definitions explicit. We denote by G [X ] the subgraph of G induced by X ⊆ V (G). A clique is a set Q ⊆ V (G) such that G [Q ] is a complete graph. The edge clique cover number of a graph G , denoted ecc(G), is the minimum number of complete subgraphs of G whose union contains every edge of G. This parameter, also known as the intersection number of a graph, was introduced by Erdős, Goodman, and Posa [1]. In addition to being an interesting parameter in its own right, it is also related to a parameter known as the competition number of a graph, introduced by Cohen [2] in the study of food webs.
Information Processing Letters, 1981
Annals of Operations Research, 2002
A clique-transversal of a graph G is a subset of vertices intersecting all the cliques of G. A cliqueindependent set is a subset of pairwise disjoint cliques of G. Denote by τ C (G) and α C (G) the cardinalities of the minimum clique-transversal and maximum clique-independent set of G, respectively. Say that G is clique-perfect when τ C (H ) = α C (H ), for every induced subgraph H of G. In this paper, we prove that every graph not containing a 4-wheel nor a 3-fan as induced subgraphs and such that every odd cycle of length greater than 3 has a short chord is clique-perfect. The proof leads to polynomial time algorithms for finding the parameters τ C (G) and α C (G), for graphs belonging to this class. In addition, we prove that to decide whether or not a given subset of vertices of a graph is a clique-transversal is Co-NP-Complete. The complexity of this problem has been mentioned as unknown in the literature. Finally, we describe a family of highly clique-imperfect graphs, that is, a family of graphs G whose difference τ C (G) − α C (G) is arbitrarily large.
A graph G is well-covered if all its maximal independent sets are of the same cardinality. Assume that a weight function w is defined on its vertices. Then G is w-well-covered if all maximal independent sets are of the same weight. For every graph G, the set of weight functions w such that G is w-well-covered is a vector space, denoted WCW(G). Let B be a complete bipartite induced subgraph of G on vertex sets of bipartition B_X and B_Y. Then B is generating if there exists an independent set S such that S \cup B_X and S \cup B_Y are both maximal independent sets of G. A relating edge is a generating subgraph in the restricted case that B = K_{1,1}. Deciding whether an input graph G is well-covered is co-NP-complete. Therefore finding WCW(G) is co-NP-hard. Deciding whether an edge is relating is co-NP-complete. Therefore, deciding whether a subgraph is generating is co-NP-complete as well. In this article we discuss the connections among these problems, provide proofs for NP-complete...
2009
A {0, 1}-matrix is balanced if it contains no square submatrix of odd order with exactly two 1's per row and per column. Balanced matrices lead to ideal formulations for both set packing and set covering problems. Balanced graphs are those graphs whose clique-vertex incidence matrix is balanced. While a forbidden induced subgraph characterization of balanced graphs is known, there is no such characterization by minimal forbidden induced subgraphs.
Asian Research Journal of Mathematics
Let G be a nontrivial, undirected, simple graph. Let S be a subset of V (G). S is a restrained cost effective set of G if for each vertex v in S, degS(v) \(\leq\) degV (G)rS(v) and the subgraph induced by the vertex set, V (G) r S has no isolated vertex. The maximum cardinality of a restrained cost effective set is the restrained cost effective number, CEr(G). In this paper, the restrained cost effective sets of paths, cycles, complete graphs, complete product of graphs and graphs resulting from line graph of graphs with maximum degree of 2 were characterized. As a direct consequence, the bounds or exact values for the restrained cost effective number were determined as well.
Journal of Combinatorial Optimization, 2011
We consider a set V of elements and an optimization problem on V : the search for a maximum (or minimum) cardinality subset of V verifying a given property P. A d-transversal is a subset of V which intersects any optimum solution in at least d elements while a d-blocker is a subset of V whose removal deteriorates the value of an optimum solution by at least d. We present some general characteristics of these problems, we review some situations which have been studied (matchings, s-t paths and s-t cuts in graphs) and we study d-transversals and d-blockers of stable sets or vertex covers in bipartite and in split graphs.
In the theory of complexity, NP (nondeterministic polynomial time) is a set of decision problems in polynomial time to be resolved in the nondeterministic Turing machine. Equivalently, it is a set of problems whose solutions can be verified on a deterministic Turing machine in polynomial time. The importance of this class of decision problems is that it contains many interesting problems of search and optimization, where we want to know if there is a solution to the problem. The contents of this paper are now handled NP-complete problems in graph theory.
We say that a graph G has the CIS-property and call it a CIS-graph if every maximal clique and every maximal stable set of G intersect. By definition, G is a CIS-graph if and only if the complementary graph G is a CISgraph. Let us substitute a vertex v of a graph G ′ by a graph G ′′ and denote the obtained graph by G. It is also easy to see that G is a CIS-graph if and only if both G ′ and G ′′ are CIS-graphs. In other words, CIS-graphs respect complementation and substitution. Yet, this class is not hereditary, that is, an induced subgraph of a CIS-graph may have no CIS-property. Perhaps, for this reason, the problems of efficient characterization and recognition of CIS-graphs are difficult and remain open. In this paper we only give some necessary and some sufficient conditions for the CIS-property to hold. There are obvious sufficient conditions. It is known that P 4-free graphs have the CIS-property and it is easy to see that G is a CIS-graph whenever each maximal clique of G has a simplicial vertex. However, these conditions are not necessary. There are also obvious necessary conditions. Given an integer k ≥ 2, a comb (or k-comb) S k is a graph with 2k vertices k of which, v 1 ,. .. , v k , form a clique C, while others, v ′ 1 ,. .. , v ′ k , form a stable set S, and (v i , v ′ i) is an edge for all i = 1,. .. , k, and there are no other edges. The complementary graph S k is called an anti-comb (or k-anti-comb). Clearly, S and C switch in the complementary graphs. Obviously, the combs and anti-combs are not CIS-graphs, since C ∩ S = ∅. Hence, if a CIS-graph G contains an induced comb or anti-comb then it must be settled, that is, G must contain a vertex v connected to all vertices of C and to no vertex of S. However, these conditions are only necessary. The following sufficient conditions are more difficult to prove: G is a CIS-graph whenever G contains no induced 3-combs and 3-anti-combs, and every induced 2comb is settled in G. It is an open question whether G is a CIS-graph if G contains no induced 4-combs and 4-anti-combs, and all induced 3-combs, 3-anti-combs, and 2-combs are settled in G. We generalize the concept of CIS-graphs as follows. For an integer d ≥ 2 we define a d-graph G = (V ; E 1 ,. .. , E d) as a complete graph whose edges are colored by d colors (that is, partitioned into d sets). We say that G is a CIS-d-graph (has the CIS-d-property) if d i=1 C i = ∅ whenever for each i = 1,. .. , d the set C i is a maximal color i-free subset of V , that is, (v, v ′) ∈ E i for any v, v ′ ∈ C i. Clearly, in case d = 2 we return to the concept of CIS-graphs. (More accurately, CIS-2-graph is a pair of two complementary CIS-graphs.) We conjecture that each CIS-d-graph is a Gallai graph, that is, it contains no triangle colored by 3 distinct colors. We obtain results supporting this conjecture and also show that if it holds then characterization and recognition of CIS-d-graphs are easily reduced to characterization and recognition of CIS-graphs. We also prove the following statement. Let G = (V ; E 1 ,. .. , E d) be a Gallai d-graph such that at least d − 1 of its d chromatic components are CIS-graphs, then G has the CIS-d-property. In particular, the remaining chromatic component of G is a CIS-graph too. Moreover, all 2 d unions of d chromatic components of G are CISgraphs.
The min-sum vertex cover (msvc) is a vertex labeling that minimizes the vertex cover number µ s (G) = e∈E(G) g(e). The minimum such sum is called the msvc cost. In this paper, we give both general bounds and exact results for the msvc cost on several classes of graphs.
2010
A maximum-clique transversal set of a graph G is a subset of vertices intersecting all maximum cliques of G. The maximum-clique transversal set problem is to find a maximum-clique transversal set of G of minimum cardinality. Motivated by the placement of transmitters for cellular telephones, Chang, Kloks, and Lee introduced the concept of maximum-clique transversal sets on graphs in 2001. In this paper, we introduce the concept of maximum-clique perfect and some variations of the maximum-clique transversal set problem such as the {k}-maximum-clique, k-fold maximum-clique, signed maximumclique, and minus maximum-clique transversal problems. We show that balanced graphs, strongly chordal graphs, and distance-hereditary graphs are maximum-clique perfect. Besides, we present a unified approach to these four problems on strongly chordal graphs and give complexity results for the following classes of graphs:
ISRN Discrete Mathematics, 2011
Amaximum-clique transversal setof a graphGis a subset of vertices intersecting all maximum cliques ofG. Themaximum-clique transversal set problemis to find a maximum-clique transversal set ofGof minimum cardinality. Motivated by the placement of transmitters for cellular telephones, Chang, Kloks, and Lee introduced the concept of maximum-clique transversal sets on graphs in 2001. In this paper, we study theweighted versionof the maximum-clique transversal set problem for split graphs, balanced graphs, strongly chordal graph, Helly circular-arc graphs, comparability graphs, distance-hereditary graphs, and graphs of bounded treewidth.
Journal of Discrete Algorithms, 2012
Let G = (V , E) be a graph in which every vertex v ∈ V has a weight w(v) 0 and a cost c(v) 0. Let S G be the family of all maximum-weight stable sets in G. For any integer d 0, a minimum d-transversal in the graph G with respect to S G is a subset of vertices T ⊆ V of minimum total cost such that |T ∩ S| d for every S ∈ S G. In this paper, we present a polynomial-time algorithm to determine minimum d-transversals in bipartite graphs. Our algorithm is based on a characterization of maximum-weight stable sets in bipartite graphs. We also derive results on minimum d-transversals of minimum-weight vertex covers in weighted bipartite graphs.
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