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Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation

A difficulty in using Simultaneous Perturbation Stochastics Approximation (SPSA) is its performance sensitivity to the step sizes chosen at the initial stage of the iteration. If the step size is too large, the solution estimate may fail to converge. The proposed adaptive stepping method automatical...

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Detalles Bibliográficos
Autores principales: Ito, Keiichi, Dhaene, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769712/
https://www.ncbi.nlm.nih.gov/pubmed/27026896
http://dx.doi.org/10.1186/s40064-016-1823-3
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author Ito, Keiichi
Dhaene, Tom
author_facet Ito, Keiichi
Dhaene, Tom
author_sort Ito, Keiichi
collection PubMed
description A difficulty in using Simultaneous Perturbation Stochastics Approximation (SPSA) is its performance sensitivity to the step sizes chosen at the initial stage of the iteration. If the step size is too large, the solution estimate may fail to converge. The proposed adaptive stepping method automatically reduces the initial step size of the SPSA so that reduction of the objective function value occurs more reliably. Ten mathematical functions each with three different noise levels were used to empirically show the effectiveness of the proposed idea. A parameter estimation example of a nonlinear dynamical system is also included.
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spelling pubmed-47697122016-03-29 Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation Ito, Keiichi Dhaene, Tom Springerplus Methodology A difficulty in using Simultaneous Perturbation Stochastics Approximation (SPSA) is its performance sensitivity to the step sizes chosen at the initial stage of the iteration. If the step size is too large, the solution estimate may fail to converge. The proposed adaptive stepping method automatically reduces the initial step size of the SPSA so that reduction of the objective function value occurs more reliably. Ten mathematical functions each with three different noise levels were used to empirically show the effectiveness of the proposed idea. A parameter estimation example of a nonlinear dynamical system is also included. Springer International Publishing 2016-02-27 /pmc/articles/PMC4769712/ /pubmed/27026896 http://dx.doi.org/10.1186/s40064-016-1823-3 Text en © Ito and Dhaene. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Methodology
Ito, Keiichi
Dhaene, Tom
Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation
title Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation
title_full Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation
title_fullStr Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation
title_full_unstemmed Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation
title_short Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation
title_sort adaptive initial step size selection for simultaneous perturbation stochastic approximation
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769712/
https://www.ncbi.nlm.nih.gov/pubmed/27026896
http://dx.doi.org/10.1186/s40064-016-1823-3
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AT dhaenetom adaptiveinitialstepsizeselectionforsimultaneousperturbationstochasticapproximation