SMT Solver

Satisfiability modulo theories (SMT) solvers are powerful tools for determining the satisfiability of formulas in various logical theories, crucial for tasks like program verification and hardware design. Current research focuses on improving solver efficiency, particularly for handling quantified formulas and complex constraints, often leveraging machine learning techniques like gradient boosting and neural networks to guide search strategies or approximate solutions. These advancements are impacting diverse fields, enabling more robust verification of software and hardware systems, improved cost analysis of programs, and more efficient solutions for optimization problems in areas such as chip design and multi-agent control.