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dc.contributor.authorHaarala, Marjo
dc.contributor.authorMäkelä, Marko M.
dc.date.accessioned2015-11-06T09:50:52Z
dc.date.available2015-11-06T09:50:52Z
dc.date.issued2006
dc.identifier.isbn951-39-2418-1
dc.identifier.otheroai:jykdok.linneanet.fi:1001225
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/47586
dc.description.abstractTypically, practical optimization problems involve nonsmooth functions of hundreds or thousands of variables. As a rule, the variables in such problems are restricted to certain meaningful intervals. In this paper, we propose an efficient adaptive limited memory bundle method for large-scale nonsmooth, possibly nonconvex, bound constrained optimization. The method combines the nonsmooth variable metric bundle method and the smooth limited memory variable metric method, while the constraint handling is based on the projected gradient method and the dual subspace minimization. The preliminary numerical experiments to be presented confirm the usability of the method.en
dc.format.extent20, [2] sivua : kuvitettu
dc.language.isoeng
dc.publisherUniversity of Jyväskylä
dc.relation.ispartofseriesReports of the Department of Mathematical Information Technology. Series B, Scientific computing
dc.titleLimited memory bundle algorithm for large bound constrained nonsmooth minization problems
dc.typebook
dc.identifier.urnURN:ISBN:951-39-2418-1
dc.relation.issn1456-436X
dc.relation.numberinseries1/2006
dc.rights.accesslevelopenAccessfi


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