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dc.contributor.authorLin, SYen_US
dc.contributor.authorLin, SSen_US
dc.date.accessioned2014-12-08T15:41:17Z-
dc.date.available2014-12-08T15:41:17Z-
dc.date.issued2003-03-01en_US
dc.identifier.issn1561-8625en_US
dc.identifier.urihttp://hdl.handle.net/11536/28073-
dc.description.abstractIn this paper, we propose a modified parallel block scaled gradient method for solving block additive unconstrained optimization problems of large distributed systems. Our method makes two major modifications to the typical parallel block scaled gradient method: First, we include a pre-processing step which reduces the computational time; second, we propose a decentralized Armijo-type step-size rule. This rule circumvents the difficulty of determining a step-size in a distributed computing environment and enables the proposed parallel algorithm to execute in a distributed computer network with a limited amount of data transfer. We have applied our method to the weighted-least-square problems of power system state estimation and demonstrated the convergence of our method by testing numerous examples on a PC network. The speedup ratio of the distributed version of our method tends to increase proportionally with the number of subsystems (or computers).en_US
dc.language.isoen_USen_US
dc.subjectunconstrained optimizationen_US
dc.subjectnonlinear programmingen_US
dc.subjectparallel computationen_US
dc.subjectlarge distributed systemsen_US
dc.subjectleast-square problemsen_US
dc.titleA parallel block scaled gradient method with decentralized step-size for block additive unconstrained optimization problems of large distributed systemsen_US
dc.typeArticleen_US
dc.identifier.journalASIAN JOURNAL OF CONTROLen_US
dc.citation.volume5en_US
dc.citation.issue1en_US
dc.citation.spage104en_US
dc.citation.epage115en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000185513500010-
dc.citation.woscount11-
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