Learning in a compiler for MINSAT algorithms

Authors:
Anja Remshagen;Klaus Truemper
Affiliations:
Department of Computer Science, State University of West Georgia, 1600 Maple St, Carrollton, GA 30118, USA (email: anja@westga.edu);University of Texas at Dallas, Department of Computer Science, EC31, Box 830688, Richardson, TX 75083-0688, USA (email: klaus@utdallas.edu)
Venue:
Theory and Practice of Logic Programming
Year:
2003

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Abstract

This paper describes learning in a compiler for algorithms solving classes of the logic minimization problem MINSAT, where the underlying propositional formula is in conjunctive normal form (CNF) and where costs are associated with the True/False values of the variables. Each class consists of all instances that may be derived from a given propositional formula and costs for True/False values by fixing or deleting variables, and by deleting clauses. The learning step begins once the compiler has constructed a solution algorithm for a given class. The step applies that algorithm to comparatively few instances of the class, analyses the performance of the algorithm on these instances, and modifies the underlying propositional formula, with the goal that the algorithm will perform much better on all instances of the class.