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
Dipartimento di Informatica

Résultats de recherche: Contribution à une conférence › Papier › Revue par des pairs

1 Citation (Scopus)

Résumé

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.

langue originale	Anglais
Pages	77-80
Nombre de pages	4
état	Publié - 1 janv. 2019
Evénement	17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019 - Enschede, Pays-Bas Durée: 1 juil. 2019 → 3 juil. 2019

Une conférence

Une conférence	17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019
Pays/Territoire	Pays-Bas
La ville	Enschede
période	1/07/19 → 3/07/19

Contient cette citation

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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.",

author = "Gabriele Iommazzo and Claudia D'Ambrosio and Antonio Frangioni and Leo Liberti",

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AU - Iommazzo, Gabriele

AU - D'Ambrosio, Claudia

AU - Frangioni, Antonio

AU - Liberti, Leo

N1 - Publisher Copyright: © Proceedings of the 17th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2019.

PY - 2019/1/1

Y1 - 2019/1/1

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

AN - SCOPUS:85084366548

SP - 77

EP - 80

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

Résumé

Une conférence

Empreinte digitale

Contient cette citation