Efficient Solver Scheduling and Selection for Satisfiability Modulo Theories (SMT) Problems

David Mojžíšek, Jan Hůla

2024

Abstract

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.

Paper Citation

in Harvard Style

Mojžíšek D. and Hůla J. (2024). Efficient Solver Scheduling and Selection for Satisfiability Modulo Theories (SMT) Problems. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 360-369. DOI: 10.5220/0012393600003654

in Bibtex Style

@conference{icpram24,
author={David Mojžíšek and Jan Hůla},
title={Efficient Solver Scheduling and Selection for Satisfiability Modulo Theories (SMT) Problems},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={360-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012393600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Efficient Solver Scheduling and Selection for Satisfiability Modulo Theories (SMT) Problems
SN - 978-989-758-684-2
AU - Mojžíšek D.
AU - Hůla J.
PY - 2024
SP - 360
EP - 369
DO - 10.5220/0012393600003654
PB - SciTePress