Global optimization by random perturbation of the gradient method with a fixed parameter
type: Item
instance of: scholarly article
title: Global optimization by random perturbation of the gradient method with a fixed parameter
zbMATH Open document ID: 0827.90132
DOI: 10.1007/BF01100691
published in: Journal of Global Optimization
publication date: 1994-10-30T00:00:00Z
review text: An objective function is supposed multimodal, bounded and differentiable, and a feasible region is a ball in Euclidean space. The algorithm is the implementation of a randomly perturbed gradient method. The perturbation is a random vector \(Z\) multiplied by the decreasing factor converging to zero where \(Z\) almost surely belongs to the feasible region. Convergence with probability 1 is proved. Results of experiments are reported.
reviewed by: Antanas Žilinskas
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 679572
zbMATH Keywords: global optimization
zbMATH Keywords: Monte Carlo methods
zbMATH Keywords: randomly perturbed gradient method
author: M. Pogu
author: Eduardo Souza de Cursi
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 679572