Global optimization using diffusion perturbations with large noise intensity
type: Item
instance of: scholarly article
title: Global optimization using diffusion perturbations with large noise intensity
zbMATH Open document ID: 1116.60043
author: K. Yin
author: G. Yin
published in: Acta Mathematicae Applicatae Sinica. English Series
publication date: 2007-01-29T00:00:00Z
review text: The authors determine an algorithm for global optimization of gradient ascent type and use random perturbations. It shows by properly varying the noise intensity, approximations of the global maximum can be achieved. It builds algorithms in discrete-time, recursive algorithms with occasional perturbations involving large noise intensity.
reviewed by: Ion C. Vladimirescu
Mathematics Subject Classification ID: 60J60
Mathematics Subject Classification ID: 62L20
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 5119461
zbMATH Keywords: global optimization
zbMATH Keywords: optimization algorithm
zbMATH Keywords: random perturbation
MaRDI profile type: Publication
full work available at URL: s10255-006-0328-1
OpenAlex ID: W2075370873
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DOI: 10.1007/S10255-006-0328-1
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description: scientific article; zbMATH DE number 5119461