Strategies for Runtime Prediction and Mathematical Solvers Tuning

Michael Barry, René Schumann

2019

Abstract

Mathematical solvers have evolved to become complex software and thereby have become a difficult subject for Runtime Prediction and parameter tuning. This paper studies various Machine Learning methods and data generation techniques to compare their effectiveness for both Runtime Prediction and parameter tuning. We show that machine Learning methods and Data Generation strategies that perform well for Runtime Prediction do not necessary result in better results for solver tuning. We show that Data Generation algorithms with an emphasis on exploitation combined with Random Forest is successful and random trees are effective for Runtime Prediction. We apply these methods to a hydro power model and present results from two experiments.

Paper Citation

in Harvard Style

Barry M. and Schumann R. (2019). Strategies for Runtime Prediction and Mathematical Solvers Tuning.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 669-676. DOI: 10.5220/0007387606690676

in Bibtex Style

@conference{icaart19,
author={Michael Barry and René Schumann},
title={Strategies for Runtime Prediction and Mathematical Solvers Tuning},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={669-676},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007387606690676},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Strategies for Runtime Prediction and Mathematical Solvers Tuning
SN - 978-989-758-350-6
AU - Barry M.
AU - Schumann R.
PY - 2019
SP - 669
EP - 676
DO - 10.5220/0007387606690676