Algorithmic configuration by learning and optimization

Gabriele Iommazzo; Claudia D'Ambrosio; Antonio Frangioni; Leo Liberti

Algorithmic configuration by learning and optimization

Gabriele Iommazzo, Claudia D'Ambrosio, Antonio Frangioni, Leo Liberti

University of Pisa

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Pages	77-80
Number of pages	4
Publication status	Published - 1 Jan 2019
Event	17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019 - Enschede, Netherlands Duration: 1 Jul 2019 → 3 Jul 2019

Conference

Conference	17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019
Country/Territory	Netherlands
City	Enschede
Period	1/07/19 → 3/07/19

Cite this

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title = "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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AU - D'Ambrosio, Claudia

AU - Frangioni, Antonio

AU - Liberti, Leo

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PY - 2019/1/1

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N2 - 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.

AB - 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.

M3 - Paper

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T2 - 17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019

Y2 - 1 July 2019 through 3 July 2019

ER -

Algorithmic configuration by learning and optimization

Abstract

Conference

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Cite this