On memory gradient method with trust region for unconstrained optimization
dc.contributor.author | Shen, Jie | en_US |
dc.contributor.author | Shi, Zhen-Jun | en_US |
dc.date.accessioned | 2006-09-11T16:03:34Z | |
dc.date.available | 2006-09-11T16:03:34Z | |
dc.date.issued | 2006-02 | en_US |
dc.identifier.citation | Shi, Zhen-Jun; Shen, Jie; (2006). "On memory gradient method with trust region for unconstrained optimization." Numerical Algorithms 41(2): 173-196. <http://hdl.handle.net/2027.42/45437> | en_US |
dc.identifier.issn | 1017-1398 | en_US |
dc.identifier.issn | 1572-9265 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45437 | |
dc.description.abstract | In this paper we present a new memory gradient method with trust region for unconstrained optimization problems. The method combines line search method and trust region method to generate new iterative points at each iteration and therefore has both advantages of line search method and trust region method. It sufficiently uses the previous multi-step iterative information at each iteration and avoids the storage and computation of matrices associated with the Hessian of objective functions, so that it is suitable to solve large scale optimization problems. We also design an implementable version of this method and analyze its global convergence under weak conditions. This idea enables us to design some quick convergent, effective, and robust algorithms since it uses more information from previous iterative steps. Numerical experiments show that the new method is effective, stable and robust in practical computation, compared with other similar methods. | en_US |
dc.format.extent | 337412 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers-Plenum Publishers; Springer Science+Business Media, Inc. | en_US |
dc.subject.other | Unconstrained Optimization | en_US |
dc.subject.other | Global Convergence | en_US |
dc.subject.other | 90C30 | en_US |
dc.subject.other | 49M37 | en_US |
dc.subject.other | 65K05 | en_US |
dc.subject.other | Memory Gradient Method | en_US |
dc.title | On memory gradient method with trust region for unconstrained optimization | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Computer & Information Science, University of Michigan, Dearborn, MI, 48128, USA, | en_US |
dc.contributor.affiliationum | College of Operations Research and Management, Qufu Normal University, Rizhao, Shandong, 276826, People’s Republic of China, ; Department of Computer & Information Science, University of Michigan, Dearborn, MI, 48128, USA, | en_US |
dc.contributor.affiliationumcampus | Dearborn | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45437/1/11075_2005_Article_9008.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s11075-005-9008-0 | en_US |
dc.identifier.source | Numerical Algorithms | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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