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

TL;DR

This paper introduces a floating-point based preprocessing step for SMT solvers that enhances efficiency in solving linear inequalities without sacrificing correctness, especially on dense problems.

Contribution

It presents a simple, adaptable preprocessing method using floating-point computations that improves the performance of simplex-based decision procedures in SMT solving.

Findings

Achieves comparable efficiency to modern SMT solvers on typical problems.

Significantly faster on dense inequality examples.

Maintains soundness and completeness despite using floating-point arithmetic.

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 on 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.