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New limited memory bundle method for large-scale nonsmooth optimization
Helsinki School of Economics.
Department of Mathematical Information Technology, University of Jyväskylä.
2004 (English)In: Optimization Methods and Software, ISSN 1055-6788, E-ISSN 1029-4937, Vol. 19, no 6, p. 673-692Article in journal (Refereed) Published
Abstract [en]

Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables. In such problems, the direct application of smooth gradient-based methods may lead to a failure due to the nonsmooth nature of the problem. On the other hand, none of the current general nonsmooth optimization methods is efficient in large-scale settings. In this article, we describe a new limited memory variable metric based bundle method for nonsmooth large-scale optimization. In addition, we introduce a new set of academic test problems for large-scale nonsmooth minimization. Finally, we give some encouraging results from numerical experiments using both academic and practical test problems.

Place, publisher, year, edition, pages
2004. Vol. 19, no 6, p. 673-692
Keyword [en]
Nondifferentiable programming, Large-scale optimization, Bundle methods, Variable metric methods, Limited memory methods, Test problems
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-84215DOI: 10.1080/10556780410001689225ISI: 000225220100002OAI: oai:DiVA.org:kth-84215DiVA, id: diva2:499185
Note
QC 20120220Available from: 2012-02-13 Created: 2012-02-13 Last updated: 2018-01-12Bibliographically approved

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