Statistical mechanics of combinatorial search

The statistical mechanics of combinatorial search problems is described using the example of the well-known NP-complete graph coloring problem. A simple parameter describing the problem structure predicts the difficulty of solving the problem, on average. However, because of the large variance associated with this prediction, it is of limited direct use for individual instances. Additional parameters, describing the problem structure as well as the heuristic effectiveness, are introduced to address this issue. This also highlights the distinction between the statistical mechanics of combinatorial search problems, with their exponentially large search spaces, and physical systems, whose interactions are often governed by a simple Euclidean metric.<<ETX>>

[1]  Tad Hogg,et al.  The Hardest Constraint Problems: A Double Phase Transition , 1994, Artif. Intell..

[2]  Patrick Prosser,et al.  An Empirical Study of Phase Transitions in Binary Constraint Satisfaction Problems , 1996, Artif. Intell..

[3]  Tad Hogg,et al.  Extending Deep Structure , 1993, AAAI.

[4]  Mark T. Jones,et al.  A Parallel Graph Coloring Heuristic , 1993, SIAM J. Sci. Comput..

[5]  Hector J. Levesque,et al.  Hard and Easy Distributions of SAT Problems , 1992, AAAI.

[6]  A. Thomason GRAPHICAL EVOLUTION An Introduction to the Theory of Random Graphs (Wiley-Interscience Series in Discrete Mathematics) , 1987 .

[7]  Jesfis Peral,et al.  Heuristics -- intelligent search strategies for computer problem solving , 1984 .

[8]  Tad Hogg,et al.  Exploiting Problem Structure in Genetic Algorithms , 1994, AAAI.

[9]  Toby Walsh,et al.  Easy Problems are Sometimes Hard , 1994, Artif. Intell..

[10]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[11]  Anne Condon,et al.  Experiments with parallel graph coloring heuristics and applications of graph coloring , 1993, Cliques, Coloring, and Satisfiability.

[12]  J. Hartigan REPRESENTATION OF SIMILARITY MATRICES BY TREES , 1967 .

[13]  Richard E. Korf,et al.  An Average-Case Analysis of Branch-and-Bound with Applications: Summary of Results , 1992, AAAI.

[14]  S Kirkpatrick,et al.  Critical Behavior in the Satisfiability of Random Boolean Expressions , 1994, Science.

[15]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[16]  P. Cheeseman,et al.  Computational Complexity And Phase Transitions , 1992, Workshop on Physics and Computation.

[17]  Tad Hogg,et al.  Solving the Really Hard Problems with Cooperative Search , 1993, AAAI.

[18]  Barbara M. Smith The phase transition in constraint satisfaction problems: A closer look at the mushy region , 1994 .

[19]  Tad Hogg,et al.  Exploiting the Deep Structure of Constraint Problems , 1994, Artif. Intell..

[20]  Alan K. Mackworth Constraint Satisfaction , 1985 .

[21]  Martin E. Dyer,et al.  The Solution of Some Random NP-Hard Problems in Polynomial Expected Time , 1989, J. Algorithms.

[22]  E. Palmer Graphical evolution: an introduction to the theory of random graphs , 1985 .

[23]  Jonathan S. Turner,et al.  Almost All k-Colorable Graphs are Easy to Color , 1988, J. Algorithms.

[24]  Pang C. Chen Heuristic Sampling: A Method for Predicting the Performance of Tree Searching Programs , 1992, SIAM J. Comput..

[25]  Tad Hogg,et al.  Using Deep Structure to Locate Hard Problems , 1992, AAAI.

[26]  Hector J. Levesque,et al.  A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.

[27]  Yumi K. Tsuji,et al.  EVIDENCE FOR A SATISFIABILITY THRESHOLD FOR RANDOM 3CNF FORMULAS , 1992 .

[28]  James M. Crawford,et al.  Experimental Results on the Crossover Point inSatis ability , 1993 .

[29]  Cecilia R. Aragon,et al.  Optimization by Simulated Annealing: An Experimental Evaluation; Part II, Graph Coloring and Number Partitioning , 1991, Oper. Res..

[30]  Steven Minton,et al.  Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems , 1992, Artif. Intell..

[31]  Tad Hogg,et al.  Phase Transitions in Artificial Intelligence Systems , 1987, Artif. Intell..

[32]  Pedro S. de Souza,et al.  Asynchronous organizations for multi-algorithm problems , 1993, SAC '93.