Algorithmic configuration by learning and optimization

Abstract : We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance. First, we employ a set of solved instances and configurations in order to learn a performance function of the solver. Secondly, we solve a mixed-integer nonlinear program in order to find the best algorithmic configuration based on the performance function.
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Submitted on : Wednesday, November 6, 2019 - 5:49:30 AM
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Gabriele Iommazzo, Claudia D'ambrosio, Antonio Frangioni, Leo Liberti. Algorithmic configuration by learning and optimization. Cologne-Twente Workshop on Graphs and Combinatorial Optimization, Jul 2019, Twente, Netherlands. ⟨hal-02350282⟩

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