Abstract
This paper addresses the problem of tuning parameters of mathematical solvers to increase their performance. We investigate how solvers can be tuned for models that undergo two types of configuration: variable configuration and constraint configuration. For each type, we investigate search algorithms for data generation that emphasizes exploration or exploitation. We show the difficulties for solver tuning in constraint configuration and how data generation methods affects a training sets learning potential.
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Acknowledgment
Parts of this work have been funded by the Swiss National Science Foundation as part of the project 407040_153760 Hydro Power Operation and Economic Performance in a Changing Market Environment.
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Barry, M., Abgottspon, H., Schumann, R. (2018). Solver Tuning and Model Configuration. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_13
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DOI: https://doi.org/10.1007/978-3-030-00111-7_13
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