Strong matching preclusion of burnt pancake graphs

The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. This is an extension of the matching preclusion problem that was introduced by Park and Ihm. The burnt pancake graph is a more complex variant of the pancake graph. In this paper, we examine the properties of burnt pancake graphs by finding its strong matching preclusion number and categorising all optimal solutions.     

Strong matching preclusion

The matching preclusion problem, introduced by Brigham et al. [R.C. Brigham, F. Harary, E.C. Violin, and J. Yellen, Perfect-matching preclusion, Congressus Numerantium 174 (2005) 185-192], studies how to effectively make a graph have neither perfect matchings nor almost perfect matchings by deleting as small a number of edges as possible. Extending this concept, we consider a more general matching preclusion problem, called the strong matching preclusion, in which deletion of vertices is additionally permitted. We establish the strong matching preclusion number and all possible minimum strong matching preclusion sets for various classes of graphs.     

Matching preclusion and conditional matching preclusion for pancake and burnt pancake graphs

The matching preclusion number of a graph with an even number of vertices is the minimum number of edges whose deletion destroys all perfect matchings in the graph. The optimal matching preclusion sets are often precisely those which are induced by a single vertex of minimum degree. To look for obstruction sets beyond these, the conditional matching preclusion number was introduced, which is defined similarly with the additional restriction that the resulting graph has no isolated vertices. In this paper we find the matching preclusion and conditional matching preclusion numbers and classify all optimal sets for the pancake graphs and burnt pancake graphs.     

Conditional matching preclusion sets

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those induced by a single vertex. In this paper, we look for obstruction sets beyond these sets. We introduce the conditional matching preclusion number of a graph. It is the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. We find this number and classify all optimal sets for several basic classes of graphs.     

Conditional matching preclusion for the alternating group graphs and split-stars

The matching preclusion number of a graph is the minimum number of edges the deletion of which results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those induced by a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those induced by a single vertex. It is defined to be the minimum number of edges the deletion of which results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this article, we find this number and classify all optimal sets for the alternating group graphs, one of the most popular interconnection networks, and their companion graphs, the split-stars. Moreover, some general results on the conditional matching preclusion problems are also presented.     

Conditional matching preclusion for the arrangement graphs

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those induced by a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those induced by a single vertex. It is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this paper we find this number and classify all optimal sets for the arrangement graphs, one of the most popular interconnection networks.     

Matching preclusion for the (n,k)-bubble-sort graphs

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. In this paper, we find this number for the (n, k)-bubble-sort graphs and classify all the optimal solutions.     

2r-正则图连通圈网络的Hamilton分解

互连网络是超级计算机的重要组成部分。互连网络通常模型化为一个图,图的顶点代表处理机,图的边代表通信链路。2010年师海忠提出互连网络的正则图连通圈网络模型,设计出了多种互连网络,也提出了一系列猜想。文中证明了2r -正则图连通圈网络可分解为边不交的一个Hamilton圈和一个完美对集的并,从而证明了当原图为2r-正则连通图时,这一系列猜想成立。     

Uniquely Restricted Matchings

A matching in a graph is a set of edges no two of which share a common vertex. In this paper we introduce a new, specialized type of matching which we call uniquely restricted matchings, originally motivated by the problem of determining a lower bound on the rank of a matrix having a specified zero/ non-zero pattern. A uniquely restricted matching is defined to be a matching M whose saturated vertices induce a subgraph which has only one perfect matching, namely M itself. We introduce the two problems of recognizing a uniquely restricted matching and of finding a maximum uniquely restricted matching in a given graph, and present algorithms and complexity results for certain special classes of graphs. We demonstrate that testing whether a given matching M is uniquely restricted can be done in O(|M||E|) time for an arbitrary graph G=(V,E) and in linear time for cacti, interval graphs, bipartite graphs, split graphs and threshold graphs. The maximum uniquely restricted matching problem is shown to be NP-complete for bipartite graphs, split graphs, and hence for chordal graphs and comparability graphs, but can be solved in linear time for threshold graphs, proper interval graphs, cacti and block graphs. Received April 12, 1998; revised June 21, 1999.     

A structure theorem for maximum internal matchings in graphs

A counterpart of the Gallai-Edmonds Structure Theorem is proved for matchings that are not required to cover the external vertices of graphs. The appropriate counterpart of Tutte's Theorem is derived from this result for the existence of perfect internal matchings in graphs.     

Perfect stables in graphs

A perfect stable in a graph G is a stable S with the property that any vertex of G is either in S or adjacent with at least two vertices which are in S. This concept is an obvious generalization of the notion of perfect matching. In this note, the problem of deciding if a given graph has a perfect stable is considered. This problem is shown to be in general NP-complete, but polynomial for K_1,3-free graphs.     

Graphical condensation of plane graphs: A combinatorial approach

The method of graphical vertex-condensation for enumerating perfect matchings of plane bipartite graph was found by Propp [Generalized Domino-shuffling, Theoret. Comput. Sci. 303 (2003) 267–301], and was generalized by Kuo [Applications of graphical condensation for enumerating matchings and tilings, Theoret. Comput. Sci. 319 (2004) 29–57] and Yan and Zhang [Graphical condensation for enumerating perfect matchings, J. Combin. Theory Ser. A 110 (2005) 113–125]. In this paper, by a purely combinatorial method some explicit identities on graphical vertex-condensation for enumerating perfect matchings of plane graphs (which do not need to be bipartite) are obtained. As applications of our results, some results on graphical edge-condensation for enumerating perfect matchings are proved, and we count the sum of weights of perfect matchings of weighted Aztec diamond.     

Assigning a kekulé structure to a conjugated molecule

The Edmonds Matching Algorithm, which leads easily to finding a perfect matching in a chemical graph, which is equivalent to a Kekulé structure in a conjugated molecule, is recalled. An extension is made to the case where only vertices of a specified set must be covered by edges of the matching which is sought.     

New theorem for the wiener molecular branching index of trees with perfect matchings

A novel theorem is proven for the values assumed by the Wiener topological branching index for trees possessed of perfect matchings. It is demonstrated that for such trees the index will always be of the form |T|/2 (mod 4), and for trees with an equal number of vertices that the indices will be congruent (mod 4).     

Almost Exact Matchings

In the exact matching problem we are given a graph G, some of whose edges are colored red, and a positive integer k. The goal is to determine if G has a perfect matching, exactly k edges of which are red. More generally if the matching number of G is m=m(G), the goal is to find a matching with m edges, exactly k edges of which are red, or determine that no such matching exists. This problem is one of the few remaining problems that have efficient randomized algorithms (in fact, this problem is in RNC), but for which no polynomial time deterministic algorithm is known. Our first result shows that, in a sense, this problem is as close to being in P as one can get. We give a polynomial time deterministic algorithm that either correctly decides that no maximum matching has exactly k red edges, or exhibits a matching with m(G)?1 edges having exactly k red edges. Hence, the additive error is one. We also present an efficient algorithm for the exact matching problem in families of graphs for which this problem is known to be tractable. We show how to count the number of exact perfect matchings in K _3,3-minor free graphs (these include all planar graphs as well as many others) in O(n ^3.19) worst case time. Our algorithm can also count the number of perfect matchings in K _3,3-minor free graphs in O(n ^2.19) time.     

On the induced matching problem

We study extremal questions on induced matchings in certain natural graph classes. We argue that these questions should be asked for twinless graphs, that is graphs not containing two vertices with the same neighborhood. We show that planar twinless graphs always contain an induced matching of size at least n/40 while there are planar twinless graphs that do not contain an induced matching of size (n+10)/27. We derive similar results for outerplanar graphs and graphs of bounded genus. These extremal results can be applied to the area of parameterized computation. For example, we show that the induced matching problem on planar graphs has a kernel of size at most 40k that is computable in linear time; this significantly improves the results of Moser and Sikdar (2007). We also show that we can decide in time O(k⁹¹+n) whether a planar graph contains an induced matching of size at least k.     

The Complexity of König Subgraph Problems and Above-Guarantee Vertex Cover

A graph is König-Egerváry if the size of a minimum vertex cover equals that of a maximum matching in the graph. These graphs have been studied extensively from a graph-theoretic point of view. In this paper, we introduce and study the algorithmic complexity of finding König-Egerváry subgraphs of a given graph. In particular, given a graph G and a nonnegative integer k, we are interested in the following questions:

1. 1.
   does there exist a set of k vertices (edges) whose deletion makes the graph König-Egerváry?
    
2. 2.
   does there exist a set of k vertices (edges) that induce a König-Egerváry subgraph?
    

We show that these problems are NP-complete and study their complexity from the points of view of approximation and parameterized complexity. Towards this end, we first study the algorithmic complexity of Above Guarantee Vertex Cover, where one is interested in minimizing the additional number of vertices needed beyond the maximum matching size for the vertex cover. Further, while studying the parameterized complexity of the problem of deleting k vertices to obtain a König-Egerváry graph, we show a number of interesting structural results on matchings and vertex covers which could be useful in other contexts.

     

Paired-domination in inflated graphs

The inflation G_I of a graph G with n(G) vertices and m(G) edges is obtained from G by replacing every vertex of degree d of G by a clique K_d. A set S of vertices in a graph G is a paired dominating set of G if every vertex of G is adjacent to some vertex in S and if the subgraph induced by S contains a perfect matching. The paired domination number γ_p(G) is the minimum cardinality of a paired dominating set of G. In this paper, we show that if a graph G has a minimum degree δ(G)2, then n(G)γ_p(G_I)4m(G)/[δ(G)+1], and the equality γ_p(G_I) = n(G) holds if and only if G has a perfect matching. In addition, we present a linear time algorithm to compute a minimum paired-dominating set for an inflation tree.     

The labeled perfect matching in bipartite graphs

In this paper, we deal with both the complexity and the approximability of the labeled perfect matching problem in bipartite graphs. Given a simple graph G=(V,E) with |V|=2n vertices such that E contains a perfect matching (of size n), together with a color (or label) function , the labeled perfect matching problem consists in finding a perfect matching on G that uses a minimum or a maximum number of colors.     

Counting the Number of Perfect Matchings in K5-Free Graphs

Counting the number of perfect matchings in graphs is a computationally hard problem. However, in the case of planar graphs, and even for K_3,3-free graphs, the number of perfect matchings can be computed efficiently. The technique to achieve this is to compute a Pfaffian orientation of a graph. In the case of K₅-free graphs, this technique will not work because some K₅-free graphs do not have a Pfaffian orientation. We circumvent this problem and show that the number of perfect matchings in K₅-free graphs can be computed in polynomial time. We also parallelize the sequential algorithm and show that the problem is in TC². We remark that our results generalize to graphs without singly-crossing minor.