Satisfiability Modulo Theories (SMT), Solver Scheduling, Algorithm Selection, Dynamic Scheduling

This paper introduces innovative concepts for improving the process of selecting solvers from a portfolio to tackle Satisfiability Modulo Theories (SMT) problems. We propose a novel solver scheduling approach that significantly enhances solving performance, measured by the PAR-2 metric, on selected benchmarks. Our investigation reveals that, in certain cases, scheduling based on a crude statistical analysis of training data can perform just as well, if not better, than a machine learning predictor. Additionally, we present a dynamic scheduling approach that adapts in real-time, taking into account the changing likelihood of solver success. These findings shed light on the nuanced nature of solver selection and scheduling, providing insights into situations where data-driven methods may not offer clear advantages.