Optimization of algorithms with OPAL

  • 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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Metadaten
Author:Charles Audet, Kien-Cong Dang, Dominique Orban
DOI:https://doi.org/10.1007/s12532-014-0067-x
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2014
Tag:Software; Theoretical Computer Science
Volume:6
Issue:3
Page Number:22
First Page:233
Last Page:254
Mathematical Programming Computation :MPC 2014 - Issue 3
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