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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, 673-692 p.Article 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, 673-692 p.
Keyword [en]
Nondifferentiable programming, Large-scale optimization, Bundle methods, Variable metric methods, Limited memory methods, Test problems
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-84215DOI: 10.1080/10556780410001689225ISI: 000225220100002OAI: oai:DiVA.org:kth-84215DiVA: diva2:499185
Note
QC 20120220Available from: 2012-02-13 Created: 2012-02-13 Last updated: 2017-12-07Bibliographically approved

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