OPAL - A Framework for Optimization of Algorithms. OPAL is a Python modeling language for algorithmic optimization. Most algorithms depend on parameters. Although changing the values of those parameters doesn’t affect the correctness of the algorithm, it typically affects its performance, where performance is understood broadly. How can we best choose those parameter values so as to maximize a certain measure of performance? OPAL is a framework that allows to easily declare algorithms and the parameters on which they depend along with representative test cases. It provides a convenient syntax to formulate the optimization problem to be solved. A black-box optimization solver takes care of the rest.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Albi, Giacomo; Bongini, Mattia; Cristiani, Emiliano; Kalise, Dante: Invisible control of self-organizing agents leaving unknown environments (2016)
- Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)
- Domes, Ferenc; Fuchs, Martin; Schichl, Hermann; Neumaier, Arnold: The optimization test environment (2014)
- Audet, C.; Dang, C.-K.; Orban, D.: Efficient use of parallelism in algorithmic parameter optimization applications (2013)
- Le Digabel, Sébastien: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm (2011)