1,485 research outputs found
Weighted Well-Covered Claw-Free Graphs
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. Given an input claw-free graph G, we present an O(n^6)algortihm,
whose input is a claw-free graph G, and output is the vector space of weight
functions w, for which G is w-well-covered. A graph G is equimatchable if all
its maximal matchings are of the same cardinality. Assume that a weight
function w is defined on the edges of G. Then G is w-equimatchable if all its
maximal matchings are of the same weight. For every graph G, the set of weight
functions w such that G is w-equimatchable is a vector space. We present an
O(m*n^4 + n^5*log(n)) algorithm which receives an input graph G, and outputs
the vector space of weight functions w such that G is w-equimatchable.Comment: 14 pages, 1 figur
Computing Well-Covered Vector Spaces of Graphs using Modular Decomposition
A graph is well-covered if all its maximal independent sets have the same
cardinality. This well studied concept was introduced by Plummer in 1970 and
naturally generalizes to the weighted case. Given a graph , a real-valued
vertex weight function is said to be a well-covered weighting of if all
its maximal independent sets are of the same weight. The set of all
well-covered weightings of a graph forms a vector space over the field of
real numbers, called the well-covered vector space of . Since the problem of
recognizing well-covered graphs is --complete, the
problem of computing the well-covered vector space of a given graph is
--hard. Levit and Tankus showed in 2015 that the
problem admits a polynomial-time algorithm in the class of claw-free graph. In
this paper, we give two general reductions for the problem, one based on
anti-neighborhoods and one based on modular decomposition, combined with
Gaussian elimination. Building on these results, we develop a polynomial-time
algorithm for computing the well-covered vector space of a given fork-free
graph, generalizing the result of Levit and Tankus. Our approach implies that
well-covered fork-free graphs can be recognized in polynomial time and also
generalizes some known results on cographs.Comment: 25 page
Independent sets of maximum weight in apple-free graphs
We present the first polynomial-time algorithm to solve the maximum weight independent set problem for apple-free graphs, which is a common generalization of several important classes where the problem can be solved efficiently, such as claw-free graphs, chordal graphs, and cographs. Our solution is based on a combination of two algorithmic techniques (modular decomposition and decomposition by clique separators) and a deep combinatorial analysis of the structure of apple-free graphs. Our algorithm is robust in the sense that it does not require the input graph G to be apple-free; the algorithm either finds an independent set of maximum weight in G or reports that G is not apple-free
Minimum Weight Perfect Matching via Blossom Belief Propagation
Max-product Belief Propagation (BP) is a popular message-passing algorithm
for computing a Maximum-A-Posteriori (MAP) assignment over a distribution
represented by a Graphical Model (GM). It has been shown that BP can solve a
number of combinatorial optimization problems including minimum weight
matching, shortest path, network flow and vertex cover under the following
common assumption: the respective Linear Programming (LP) relaxation is tight,
i.e., no integrality gap is present. However, when LP shows an integrality gap,
no model has been known which can be solved systematically via sequential
applications of BP. In this paper, we develop the first such algorithm, coined
Blossom-BP, for solving the minimum weight matching problem over arbitrary
graphs. Each step of the sequential algorithm requires applying BP over a
modified graph constructed by contractions and expansions of blossoms, i.e.,
odd sets of vertices. Our scheme guarantees termination in O(n^2) of BP runs,
where n is the number of vertices in the original graph. In essence, the
Blossom-BP offers a distributed version of the celebrated Edmonds' Blossom
algorithm by jumping at once over many sub-steps with a single BP. Moreover,
our result provides an interpretation of the Edmonds' algorithm as a sequence
of LPs
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