Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/61014

TitleA stochastic coordinate descent for bound constrained global optimization
Author(s)Rocha, Ana Maria A. C.
Costa, M. Fernanda P.
Fernandes, Edite Manuela da G. P.
Issue date2019
PublisherAmerican Institute of Physics
JournalAIP Conference Proceedings
Abstract(s)This paper presents a stochastic coordinate descent algorithm for solving bound constrained global optimization problems. The algorithm borrows ideas from some stochastic optimization methods available for the minimization of expected and empirical risks that arise in large-scale machine learning. Initially, the algorithm generates a population of points although only a small subpopulation of points is randomly selected and moved at each iteration towards the global optimal solution. Each point of the subpopulation is moved along one component only of the negative gradient direction. Preliminary experiments show that the algorithm is effective in reaching the required solution.
TypeConference paper
URIhttp://hdl.handle.net/1822/61014
ISBN9780735417984
DOI10.1063/1.5089981
ISSN0094-243X
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

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