dc.contributor.author | Karmitsa, Napsu | |
dc.contributor.author | Mäkelä, Marko M. | |
dc.contributor.author | Ali, Montaz M. | |
dc.date.accessioned | 2015-11-06T09:43:03Z | |
dc.date.available | 2015-11-06T09:43:03Z | |
dc.date.issued | 2007 | |
dc.identifier.isbn | 978-951-39-2785-1 | |
dc.identifier.other | oai:jykdok.linneanet.fi:1026454 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/47584 | |
dc.description.abstract | Many practical optimization problems involve nonsmooth (that is, not
necessarily differentiable) functions of hundreds or thousands of variables
with various constraints. In this paper, we describe a new efficient adaptive
limited memory interior point bundle method for large, possible nonconvex,
nonsmooth inequality constrained optimization. The method is a hybrid
of the nonsmooth variable metric bundle method and the smooth limited
memory variable metric method, and the constraint handling is based
on the primal-dual feasible direction interior point approach. The preliminary
numerical experiments to be presented confirm the effectiveness of the
method. | |
dc.format.extent | 26, [3] sivua : kuvitettu | |
dc.language.iso | eng | |
dc.publisher | University of Jyväskylä | |
dc.relation.ispartofseries | Reports of the Department of Mathematical Information Technology. Series B, Scientific computing | |
dc.title | Limited memory bundle algorithm for inequality constrained nondifferentiable optimization | |
dc.type | book | |
dc.identifier.urn | URN:ISBN:978-951-39-2785-1 | |
dc.relation.issn | 1456-436X | |
dc.relation.numberinseries | 3/2007 | |
dc.rights.accesslevel | openAccess | fi |