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Incomplete Dynamic Backtracking for Linear Pseudo-Boolean Problems

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  • Steven Prestwich

Abstract

Many combinatorial problems can be modeled as 0/1 integer linear programs. Problems expressed in this form are usually solved by Operations Research algorithms, but good results have also been obtained using generalised SAT algorithms based on backtracking or local search, after transformation to pseudo-Boolean form. A third class of SAT algorithm uses non-systematic backtracking to combine constraint propagation with local search-like scalability, at the cost of completeness. This paper describes such an algorithm for pseudo-Boolean models. Experimental results on a variety of problems are encouraging, in some cases yielding improved solutions or performance compared to previous algorithms. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Steven Prestwich, 2004. "Incomplete Dynamic Backtracking for Linear Pseudo-Boolean Problems," Annals of Operations Research, Springer, vol. 130(1), pages 57-73, August.
    • Handle: RePEc:spr:annopr:v:130:y:2004:i:1:p:57-73:10.1023/b:anor.0000032570.90510.8f
    DOI: 10.1023/B:ANOR.0000032570.90510.8f
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    Cited by:

    1. Steven Prestwich, 2007. "Exploiting relaxation in local search for LABS," Annals of Operations Research, Springer, vol. 156(1), pages 129-141, December.
    2. Russell, Robert A. & Urban, Timothy L., 2010. "Multicriteria models for planning power-networking events," European Journal of Operational Research, Elsevier, vol. 207(1), pages 83-91, November.

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