On Tackling the Limits of Resolution in SAT Solving

The practical success of Boolean Satisfiability (SAT) solvers stems from the CDCL (Conflict-Driven Clause Learning) approach to SAT solving. However, from a propositional proof complexity perspective, CDCL is no more powerful than the resolution proof system, for which many hard examples exist. This paper proposes a new problem transformation, which enables reducing the decision problem for formulas in conjunctive normal form (CNF) to the problem of solving maximum satisfiability over Horn formulas. Given the new transformation, the paper proves a polynomial bound on the number of MaxSAT resolution steps for pigeonhole formulas. This result is in clear contrast with earlier results on the length of proofs of MaxSAT resolution for pigeonhole formulas. The paper also establishes the same polynomial bound in the case of modern core-guided MaxSAT solvers. Experimental results, obtained on CNF formulas known to be hard for CDCL SAT solvers, show that these can be efficiently solved with modern MaxSAT solvers

Propositional Satisfiability (SAT) as a language problem

We present an approach to propositional satisfiability as a Finite State Automata automata construction problem. From a theoretical point of view it has consequences for languages beyond context free power. There are no consequences on complexity issues due to Automata construction (using intersection) is PSPACE-complete. From a practical point of view it was shown that this approach is competitive with ALL-SAT approaches and even with state of the art SAT solvers on traditional hard problems. Here, we show that techniques used in DPLL can be used in an automata approach. This kind of approach opens a new path of research on propositional satisfiability.Presentado en el II Workshop Aspectos Teóricos de Ciencia de la Computación (WATCC)Red de Universidades con Carreras en Informática (RedUNCI

A finite state intersection approach to propositional satisfiability

AbstractWe use a finite state (FSA) construction approach to address the problem of propositional satisfiability (SAT). We present a very simple translation from formulas in conjunctive normal form (CNF) to regular expressions and use regular expressions to construct an FSA. As a consequence of the FSA construction, we obtain an ALL-SAT solver and model counter. This automata construction can be considered essentially a finite state intersection grammar (FSIG). We also show how an FSIG approach can be encoded. Several variable ordering (state ordering) heuristics are compared in terms of the running time of the FSA and FSIG construction. We also present a strategy for clause ordering (automata composition). Running times of state-of-the-art model counters and BDD based SAT solvers are compared and we show that both the FSA and FSIG approaches obtain an state-of-the-art performance on some hard unsatisfiable benchmarks. It is also shown that clause learning techniques can help improve performance. This work brings up many questions on the possible use of automata and grammar models to address SAT

A Restriction of Extended Resolution for Clause Learning SAT Solvers

International audienc

Towards Next Generation Sequential and Parallel SAT Solvers

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology