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Book ChapterDOI

A simple and faster branch-and-bound algorithm for finding a maximum clique

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TLDR
This paper proposes new approximate coloring and other related techniques which markedly improve the run time of the branch-and-bound algorithm MCR, previously shown to be the fastest maximum-clique-finding algorithm for a large number of graphs.
Abstract
This paper proposes new approximate coloring and other related techniques which markedly improve the run time of the branch-and-bound algorithm MCR (J. Global Optim., 37, 95–111, 2007), previously shown to be the fastest maximum-clique-finding algorithm for a large number of graphs. The algorithm obtained by introducing these new techniques in MCR is named MCS. It is shown that MCS is successful in reducing the search space quite efficiently with low overhead. Consequently, it is shown by extensive computational experiments that MCS is remarkably faster than MCR and other existing algorithms. It is faster than the other algorithms by an order of magnitude for several graphs. In particular, it is faster than MCR for difficult graphs of very high density and for very large and sparse graphs, even though MCS is not designed for any particular type of graphs. MCS can be faster than MCR by a factor of more than 100,000 for some extremely dense random graphs.

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Citations
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An exact algorithm for the maximum clique problem with accelerated pruning

TL;DR: A partially enumerative algorithm is presented for the maximum clique problem which is very simple to implement and Computational results for an efficient implementation on an IBM 3090 computer are provided.
Journal ArticleDOI

A review on algorithms for maximum clique problems

TL;DR: This review provides an updated and comprehensive review on both exact and heuristic MCP algorithms, with a special focus on recent developments, and identifies the general framework followed by these algorithms and pinpoint the key ingredients that make them successful.
Journal ArticleDOI

Branch-and-reduce exponential/FPT algorithms in practice

TL;DR: The results indicate that branch-and-reduce algorithms are actually quite practical and competitive with other state-of-the-art approaches for several kinds of instances, thus showing the practical impact of theoretical research on branching algorithms.
Proceedings ArticleDOI

Scalable maximum clique computation using MapReduce

TL;DR: This work presents a scalable and fault-tolerant solution for the maximum clique problem based on the MapReduce framework that is more scalable than an MPI algorithm, and is simpler and more fault tolerant.
Journal ArticleDOI

An improved bit parallel exact maximum clique algorithm

TL;DR: New improvements for BB-MaxClique are described, a leading maximum clique algorithm which uses bit strings to efficiently compute basic operations during search by bit masking, which established that recoloring is mainly useful for graphs with high densities.
References
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Book ChapterDOI

The maximum clique problem

TL;DR: A survey of results concerning algorithms, complexity, and applications of the maximum clique problem is presented and enumerative and exact algorithms, heuristics, and a variety of other proposed methods are discussed.
Journal ArticleDOI

The worst-case time complexity for generating all maximal cliques and computational experiments

TL;DR: A depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in the Bron-Kerbosch algorithm, which proves that its worst-case time complexity is O(3n/3) for an n-vertex graph.
Journal ArticleDOI

A fast algorithm for the maximum clique problem

TL;DR: A branch-and-bound algorithm for the maximum clique problem--which is computationally equivalent to the maximum independent (stable) set problem--is presented with the vertex order taken from a coloring of the vertices and with a new pruning strategy.
Journal ArticleDOI

Finding a maximum independent set

TL;DR: An algorithm is presented which finds a maximum independent set in an n-vertex graph in 0($2^{n/3}$) time and can thus handle graphs roughly three times as large as could be analyzed using a naive algorithm.
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