2,503 research outputs found
New bounds for the max--cut and chromatic number of a graph
We consider several semidefinite programming relaxations for the max--cut
problem, with increasing complexity. The optimal solution of the weakest
presented semidefinite programming relaxation has a closed form expression that
includes the largest Laplacian eigenvalue of the graph under consideration.
This is the first known eigenvalue bound for the max--cut when that is
applicable to any graph. This bound is exploited to derive a new eigenvalue
bound on the chromatic number of a graph. For regular graphs, the new bound on
the chromatic number is the same as the well-known Hoffman bound; however, the
two bounds are incomparable in general. We prove that the eigenvalue bound for
the max--cut is tight for several classes of graphs. We investigate the
presented bounds for specific classes of graphs, such as walk-regular graphs,
strongly regular graphs, and graphs from the Hamming association scheme
Sum Coloring : New upper bounds for the chromatic strength
The Minimum Sum Coloring Problem (MSCP) is derived from the Graph Coloring
Problem (GCP) by associating a weight to each color. The aim of MSCP is to find
a coloring solution of a graph such that the sum of color weights is minimum.
MSCP has important applications in fields such as scheduling and VLSI design.
We propose in this paper new upper bounds of the chromatic strength, i.e. the
minimum number of colors in an optimal solution of MSCP, based on an
abstraction of all possible colorings of a graph called motif. Experimental
results on standard benchmarks show that our new bounds are significantly
tighter than the previous bounds in general, allowing to reduce substantially
the search space when solving MSCP .Comment: pre-prin
A characterization and an application of weight-regular partitions of graphs
A natural generalization of a regular (or equitable) partition of a graph,
which makes sense also for non-regular graphs, is the so-called weight-regular
partition, which gives to each vertex a weight that equals the
corresponding entry of the Perron eigenvector . This
paper contains three main results related to weight-regular partitions of a
graph. The first is a characterization of weight-regular partitions in terms of
double stochastic matrices. Inspired by a characterization of regular graphs by
Hoffman, we also provide a new characterization of weight-regularity by using a
Hoffman-like polynomial. As a corollary, we obtain Hoffman's result for regular
graphs. In addition, we show an application of weight-regular partitions to
study graphs that attain equality in the classical Hoffman's lower bound for
the chromatic number of a graph, and we show that weight-regularity provides a
condition under which Hoffman's bound can be improved
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