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Decomposing SAT instances with pseudo backbones. (English) Zbl 1453.68170
Hu, Bin (ed.) et al., Evolutionary computation in combinatorial optimization. 17th European conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19–21, 2017. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 10197, 75-90 (2017).
Summary: Two major search paradigms have been proposed for SAT solving: Systematic Search (SS) and Stochastic Local Search (SLS). In SAT competitions, while SLS solvers are effective on uniform random instances, SS solvers dominate SLS solvers on application instances with internal structures. One important structural property is decomposability. SS solvers have long been exploited the decomposability of application instances with success. We conjecture that SLS solvers can be improved by exploiting decomposability of application instances, and propose the first step toward exploiting decomposability with SLS solvers using pseudo backbones. We then propose two SAT-specific optimizations that lead to better decomposition than on general pseudo Boolean optimization problems. Our empirical study suggests that pseudo backbones can vastly simplify SAT instances, which further results in decomposing the instances into thousands of connected components. This decomposition serves as a key stepping stone for applying the powerful recombination operator, partition crossover, to the SAT domain. Moreover, we establish a priori analysis for identifying problem instances with potential decomposability using visualization of MAXSAT instances and treewidth.
For the entire collection see [Zbl 1360.68014].
MSC:
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68R07 Computational aspects of satisfiability
90C59 Approximation methods and heuristics in mathematical programming
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