Counterexample guided inductive optimization based on satisfiability modulo theories

Abstract

This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the optimization domain. The present algorithms are evaluated using a large set of benchmarks typically employed for evaluating optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithms, which find the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima. • We describe three counterexample guided inductive optimization (CEGIO) approaches based on Satisfiability Modulo Theories. • CEGIO-Generalized is used for solving any constrained optimization problem. • CEGIO-Simplified is employed if information about the minima location is known. • CEGIO-Fast is employed for convex functions to speed up the optimization process. • Our proposed approaches outperform traditional optimization techniques employed for convex and non-convex functions.