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Benchmarking Multivariate Solvers of SciPy on the Noiseless Testbed

Konstantinos Varelas 1, 2 Marie-Ange Dahito 1, 3 
1 RANDOPT - Randomized Optimisation
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
Abstract : In this article we benchmark eight multivariate local solvers as well as the global Differential Evolution algorithm from the Python SciPy library on the BBOB noiseless testbed. We experiment with different parameter settings and termination conditions of the solvers. More focus is given to the L-BFGS-B and Nelder-Mead algorithms. For the first we investigate the effect of the maximum number of variable metric corrections used for the Hessian approximation and show that larger values than the default are of advantage. For the second we investigate the effect of adaptation of parameters, which is proved crucial for the performance of the method with increasing dimensionality.
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Submitted on : Wednesday, June 19, 2019 - 12:37:40 PM
Last modification on : Friday, February 4, 2022 - 3:32:53 AM


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Konstantinos Varelas, Marie-Ange Dahito. Benchmarking Multivariate Solvers of SciPy on the Noiseless Testbed. GECCO 2019 - The Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. ⟨10.1145/3319619.3326891⟩. ⟨hal-02160099⟩



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