Stochastic Systematic Search Algorithms for Satisfiability

Lynce, Inês; Baptista, Luís; Marques-Silva, João

doi:10.1016/s1571-0653(04)00322-1

Cited by 24 publications

(10 citation statements)

References 14 publications

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“…Global search restarts with a degree of randomness to compensate for heavy tails in the search cost distribution (Gomes, Selman, et al, 2000). Randomness can be introduced to break ties in variable and value selection, to decide whether to apply inference procedures after a value assignment, or to select a backtrack point (Gomes, 2003;Lynce, Baptista, et al, 2001). …”

Section: Related Workmentioning

confidence: 99%

Random Subsets Support Learning a Mixture of Heuristics

Petrovic

Epstein

2008

Int. J. Artif. Intell. Tools

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Problem solvers, both human and machine, have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the problem class, and their mutual interactions may not be well understood. The long-term goal of our work is to learn how to select appropriately from among a large body of heuristics, and how to combine them into a mixture that works well on a specific class of problems. The principal result reported here is that randomly chosen subsets of heuristics can improve the identification of an appropriate mixture of heuristics. A self-supervised learner uses this method here to learn to solve constraint satisfaction problems quickly and effectively.

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Section: Related Workmentioning

confidence: 99%

Random Subsets Support Learning a Mixture of Heuristics

Petrovic

Epstein

2008

Int. J. Artif. Intell. Tools

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“…Recently, Lynce et al [19] proposed and empirically evaluated combining random restarts with random backtracking. In this scheme, the diagnosis engine periodically backtracks nonchronologically to a decision level involving any literal in the conflict-induced clause.…”

Section: Random Restarts and Backtrackingmentioning

confidence: 99%

Solution and Optimization of Systems of Pseudo-Boolean Constraints

Aloul

Ramani

Sakallah

et al. 2007

IEEE Trans. Comput.

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Abstract-Optimized solvers for the Boolean Satisfiability (SAT) problem have many applications in areas such as hardware and software verification, FPGA routing, planning, and so forth. Further uses are complicated by the need to express "counting constraints" in conjunctive normal form (CNF). Expressing such constraints by pure CNF leads to more complex SAT instances. Alternatively, those constraints can be handled by Integer Linear Programming (ILP), but generic ILP solvers may ignore the Boolean nature of 0-1 variables. Therefore, specialized 0-1 ILP solvers extend SAT solvers to handle these so-called "pseudo-Boolean" (PB) constraints. This work provides an update on the on-going competition between generic ILP techniques and specialized 0-1 ILP techniques. To make a fair comparison, we generalize recent ideas for fast SAT-solving to more general 0-1 ILP problems that may include counting constraints and optimization. This generalization is embodied in our PB constraint solver and optimizer PBS, which is compared with state-of-the-art CNF and generic ILP solvers. Another aspect of our comparison is the evaluation on 0-1 ILP benchmarks that originate in Electronic Design Automation (EDA) but that cannot be directly solved by an SAT solver. Specifically, we solve instances of the Max-SAT and Max-ONEs optimization problems, which seek to maximize the number of satisfied clauses and the "true" values over all satisfying assignments, respectively. Those problems have straightforward applications to SAT-based routing and are additionally important due to reductions from Max-Cut, Max-Clique, and Min Vertex Cover. Our experimental results show that specialized 0-1 techniques implemented in PBS tend to outperform generic ILP techniques on Boolean optimization problems, as well as on general EDA SAT problems.Index Terms-Boolean satisfiability (SAT), integer linear programming (ILP), backtrack search, conjunctive normal form (CNF), pseudo-Boolean (PB), Max-SAT, Max-ONE, global routing.

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“…Jquest is a SAT platform in Java containing various heuristics, data structures and search strategies [40]. The solver was configured with lazy data structures (inspired by both SATO and Chaff), non-chronological backtracking and clause recording (like in Grasp), chaff-like heuristic, randomized backtracking [41] and rapid restarts strategy. JQuest source code is available at http://sat.inesc.pt/sat/soft/ jquest/jquest-src.tgz.…”

Section: Solver Submissionmentioning

confidence: 99%