Open AccessBenchmarking multivariate solvers of SciPy on the noiseless testbed
Author(s) -
Konstantinos Varelas,
Marie-Ange Dahito
Publication year - 2019
Publication title -
proceedings of the genetic and evolutionary computation conference companion
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-4503-6748-6
DOI - 10.1145/3319619.3326891
Subject(s) - benchmarking , computer science , testbed , benchmark (surveying) , python (programming language) , hessian matrix , curse of dimensionality , metric (unit) , cma es , multivariate statistics , pooling , mathematical optimization , broyden–fletcher–goldfarb–shanno algorithm , algorithm , focus (optics) , artificial intelligence , mathematics , machine learning , covariance function , covariance matrix , asynchronous communication , physics , geography , computer network , business , geodesy , optics , operating system , economics , marketing , operations management
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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