On using floating-point computations to help an exact linear arithmetic decision procedure

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Abstract

We consider the decision problem for quantifier-free formulas whose atoms are linear inequalities interpreted over the reals or rationals. This problem may be decided using satisfiability modulo theory (SMT), using a mixture of a SAT solver and a simplex-based decision procedure for conjunctions. State-of-the-art SMT solvers use simplex implementations over rational numbers, which perform well for typical problems arising from model-checking and program analysis (sparse inequalities, small coefficients) but are slow for other applications (denser problems, larger coefficients). We propose a simple preprocessing phase that can be adapted to existing SMT solvers and that may be optionally triggered. Despite using floating-point computations, our method is sound and complete - it merely affects efficiency. We implemented the method and provide benchmarks showing that this change brings a naive and slow decision procedure ("textbook simplex" with rational numbers) up to the efficiency of recent SMT solvers, over test cases arising from model-checking, and makes it definitely faster than state-of-the-art SMT solvers on dense examples. © 2009 Springer Berlin Heidelberg.

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APA

Monniaux, D. (2009). On using floating-point computations to help an exact linear arithmetic decision procedure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5643 LNCS, pp. 570–583). https://doi.org/10.1007/978-3-642-02658-4_42

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