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Link to original content: http://lod.nl.go.kr/page/CNTS-00131802999
Convergence of Supermemory Gradient Method | lod.nl.go.kr
dcterms:abstract
  • In this paper we consider the global convergence of a new supermemory gradient method for unconstrained optimization problems. New trust region radius is proposed to make the new method converge stably and averagely, and it will be suitable to solve large scale minimization problems. Some global convergence results are obtained under some mild conditions. Numerical results show that this new method is effective and stable in practical computation (xsd:string)
dcterms:accessRights
nlon:antecedentSource
  • born digital (xsd:string)
nlon:audienceNote
  • 일반이용자 (xsd:string)
nlon:bibliography
  • Includes bibliographical references (xsd:string)
dc:creator
  • Jie Shen (xsd:string)
nlon:datePublished
  • 2024-10-11T17:14:36 (xsd:dateTime)
bibframe:extent
  • p. 367-376 (xsd:string)
dc:format
  • application/pdf (xsd:string)
nlon:genre
  • 학술지 (xsd:string)
nlon:holdingInstitution
  • 국립중앙도서관 (xsd:string)
rdfs:label
  • Convergence of supermemory gradient method (xsd:string)
nlon:medium
  • 전자자료(Application) (xsd:string)
nlon:newsPosition
  • p. 367-376 (xsd:string)
nlon:publicationPlace
  • 한국 (xsd:string)
nlon:relatedParts
  • Vol.24 no.1·2(2007 May) (xsd:string)
nlon:restriction
  • 외부공개 (xsd:string)
dcterms:subject
dcterms:title
  • Convergence of supermemory gradient method (xsd:string)
nlon:titleOfHostItem
  • Journal of applied mathematics & computing (xsd:string)
rdf:type
nlon:typeOfResource