Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/52765
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dc.titleA superlinearly convergent algorithm for large scale multi-stage stochastic nonlinear programming
dc.contributor.authorMeng, F.
dc.contributor.authorTan, R.C.E.
dc.contributor.authorZhao, G.
dc.date.accessioned2014-05-19T02:49:45Z
dc.date.available2014-05-19T02:49:45Z
dc.date.issued2004-06
dc.identifier.citationMeng, F.,Tan, R.C.E.,Zhao, G. (2004-06). A superlinearly convergent algorithm for large scale multi-stage stochastic nonlinear programming. International Journal of Computational Engineering Science 5 (2) : 327-344. ScholarBank@NUS Repository.
dc.identifier.issn14658763
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/52765
dc.description.abstractThis paper presents an algorithm for solving a class of large scale nonlinear programming which is originally derived from the multi-stage stochastic convex nonlinear programming. With the Lagrangian-dual method and the Moreau-Yosida regularization, the primal problem is transformed into a smooth convex problem. By introducing a self-concordant barrier function, an approximate generalized Newton method is designed in this paper. The algorithm is shown to be of superlinear convergence. At last, some preliminary numerical results are provided.
dc.sourceScopus
dc.subjectLagrangian-dual Functions
dc.subjectMoreau-Yosida Regularization
dc.subjectMulti-stage Stochastic Programming
dc.subjectSelf-concordant Functions
dc.typeArticle
dc.contributor.departmentMATHEMATICS
dc.description.sourcetitleInternational Journal of Computational Engineering Science
dc.description.volume5
dc.description.issue2
dc.description.page327-344
dc.identifier.isiutNOT_IN_WOS
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