97 research outputs found
Message passing for the coloring problem: Gallager meets Alon and Kahale
Message passing algorithms are popular in many combinatorial optimization
problems. For example, experimental results show that {\em survey propagation}
(a certain message passing algorithm) is effective in finding proper
-colorings of random graphs in the near-threshold regime. In 1962 Gallager
introduced the concept of Low Density Parity Check (LDPC) codes, and suggested
a simple decoding algorithm based on message passing. In 1994 Alon and Kahale
exhibited a coloring algorithm and proved its usefulness for finding a
-coloring of graphs drawn from a certain planted-solution distribution over
-colorable graphs. In this work we show an interpretation of Alon and
Kahale's coloring algorithm in light of Gallager's decoding algorithm, thus
showing a connection between the two problems - coloring and decoding. This
also provides a rigorous evidence for the usefulness of the message passing
paradigm for the graph coloring problem. Our techniques can be applied to
several other combinatorial optimization problems and networking-related
issues.Comment: 11 page
The price of re-establishing perfect, almost perfect or public monitoring in games with arbitrary monitoring
This paper establishes a connection between the notion of observation (or
monitoring) structure in game theory and the one of communication channels in
Shannon theory. One of the objectives is to know under which conditions an
arbitrary monitoring structure can be transformed into a more pertinent
monitoring structure. To this end, a mediator is added to the game. The
objective of the mediator is to choose a signalling scheme that allows the
players to have perfect, almost perfect or public monitoring and all of this,
at a minimum cost in terms of signalling. Graph coloring, source coding, and
channel coding are exploited to deal with these issues. A wireless power
control game is used to illustrate these notions but the applicability of the
provided results and, more importantly, the framework of transforming
monitoring structures go much beyond this example.Comment: Proc. of the 4th ACM International Workshop on Game Theory in
Communication Networks, 201
A Fast and Scalable Graph Coloring Algorithm for Multi-core and Many-core Architectures
Irregular computations on unstructured data are an important class of
problems for parallel programming. Graph coloring is often an important
preprocessing step, e.g. as a way to perform dependency analysis for safe
parallel execution. The total run time of a coloring algorithm adds to the
overall parallel overhead of the application whereas the number of colors used
determines the amount of exposed parallelism. A fast and scalable coloring
algorithm using as few colors as possible is vital for the overall parallel
performance and scalability of many irregular applications that depend upon
runtime dependency analysis.
Catalyurek et al. have proposed a graph coloring algorithm which relies on
speculative, local assignment of colors. In this paper we present an improved
version which runs even more optimistically with less thread synchronization
and reduced number of conflicts compared to Catalyurek et al.'s algorithm. We
show that the new technique scales better on multi-core and many-core systems
and performs up to 1.5x faster than its predecessor on graphs with high-degree
vertices, while keeping the number of colors at the same near-optimal levels.Comment: To appear in the proceedings of Euro Par 201
Convergence Times of Decentralized Graph Coloring Algorithms
Ordinary graph coloring algorithms are nothing without their calculations, memorizations, and inter-vertex communications. We investigate a class of ultra simple algorithms which can find (Delta+1)-colorings despite drastic restrictions. For each procedure, conflicted vertices randomly recolor one at a time until the graph coloring is valid. We provide an array of run time bounds for these processes, including an O(n*log(Delta)) bound for a variant we propose, and an O(n*Delta) bound which applies to even the most adversarial scenarios
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