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Optimization of algorithms with OPAL

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Abstract

Opal is a general-purpose system for modeling and solving algorithm optimization problems. Opal takes an algorithm as input, and as output it suggests parameter values that maximize some user-defined performance measure. In order to achieve this, the user provides a Python script describing how to launch the target algorithm, and defining the performance measure. Opal then models this question as a blackbox optimization problem which is then solved by a state-of-the-art direct search solver. Opal handles a wide variety of parameter types, it can exploit multiple processors in parallel at different levels, and can take advantage of a surrogate blackbox. Several features of Opal are illustrated on a problem consisting in the design of a hybrid sort strategy.

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Notes

  1. OPtimization of ALgorithms.

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Authors and Affiliations

  1. GERAD and Department of Mathematics and Industrial Engineering, Ecole Polytechnique, Montreal, QC, Canada

    Charles Audet & Dominique Orban

  2. GERAD, Montreal, QC, Canada

    Kien-Cong Dang

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  1. Charles Audet
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  2. Kien-Cong Dang
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  3. Dominique Orban
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Corresponding author

Correspondence to Dominique Orban.

Additional information

Research partially supported by NSERC Discovery Grant 239436, Afosr FA9550-09-1-0160, and ExxonMobil Upstream Research Company EM02562.

Research partially supported by NSERC Discovery Grant 299010-04.

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Audet, C., Dang, KC. & Orban, D. Optimization of algorithms with OPAL. Math. Prog. Comp. 6, 233–254 (2014). https://doi.org/10.1007/s12532-014-0067-x

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