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

  • Authors:

  • Etsuji Tomita;Yoichi Sutani;Takanori Higashi;Shinya Takahashi;Mitsuo Wakatsuki

  • Affiliations:

  • Advanced Algorithms Research Laboratory, Department of Information and Communication Engineering, The University of Electro-Communications, Tokyo, Japan;Advanced Algorithms Research Laboratory, Department of Information and Communication Engineering, The University of Electro-Communications, Tokyo, Japan;Advanced Algorithms Research Laboratory, Department of Information and Communication Engineering, The University of Electro-Communications, Tokyo, Japan;Advanced Algorithms Research Laboratory, Department of Information and Communication Engineering, The University of Electro-Communications, Tokyo, Japan;Advanced Algorithms Research Laboratory, Department of Information and Communication Engineering, The University of Electro-Communications, Tokyo, Japan

  • Venue:
  • WALCOM'10 Proceedings of the 4th international conference on Algorithms and Computation
  • Year:
  • 2010

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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.