Towards distributed algorithm portfolios

Matteo Gagliolo; Jürgen Schmidhuber

doi:10.1007/978-3-540-85863-8_75

Towards distributed algorithm portfolios

Matteo Gagliolo, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

In recent work we have developed an online algorithm selection technique, in which a model of algorithm performance is learned incrementally while being used. The resulting exploration-exploitation trade-off is solved as a bandit problem. The candidate solvers are run in parallel on a single machine, as an algorithm portfolio, and computation time is shared among them according to their expected performances. In this paper, we extend our technique to the more interesting and practical case of multiple CPUs. © 2009 Springer-Verlag Berlin Heidelberg.

Original language	English (US)
Title of host publication	Advances in Soft Computing
Pages	634-643
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-540-85863-8_75
State	Published - Jan 9 2009
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Computational Mechanics
Computer Science (miscellaneous)
Computer Science Applications

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10.1007/978-3-540-85863-8_75

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title = "Towards distributed algorithm portfolios",

abstract = "In recent work we have developed an online algorithm selection technique, in which a model of algorithm performance is learned incrementally while being used. The resulting exploration-exploitation trade-off is solved as a bandit problem. The candidate solvers are run in parallel on a single machine, as an algorithm portfolio, and computation time is shared among them according to their expected performances. In this paper, we extend our technique to the more interesting and practical case of multiple CPUs. {\textcopyright} 2009 Springer-Verlag Berlin Heidelberg.",

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Y1 - 2009/1/9

N2 - In recent work we have developed an online algorithm selection technique, in which a model of algorithm performance is learned incrementally while being used. The resulting exploration-exploitation trade-off is solved as a bandit problem. The candidate solvers are run in parallel on a single machine, as an algorithm portfolio, and computation time is shared among them according to their expected performances. In this paper, we extend our technique to the more interesting and practical case of multiple CPUs. © 2009 Springer-Verlag Berlin Heidelberg.

AB - In recent work we have developed an online algorithm selection technique, in which a model of algorithm performance is learned incrementally while being used. The resulting exploration-exploitation trade-off is solved as a bandit problem. The candidate solvers are run in parallel on a single machine, as an algorithm portfolio, and computation time is shared among them according to their expected performances. In this paper, we extend our technique to the more interesting and practical case of multiple CPUs. © 2009 Springer-Verlag Berlin Heidelberg.

UR - http://link.springer.com/10.1007/978-3-540-85863-8_75

UR - http://www.scopus.com/inward/record.url?scp=58149185412&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-85863-8_75

DO - 10.1007/978-3-540-85863-8_75

M3 - Conference contribution

SN - 9783540858621

SP - 634

EP - 643

BT - Advances in Soft Computing

ER -

Towards distributed algorithm portfolios

Abstract

Bibliographical note

ASJC Scopus subject areas

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