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Globally Convergent Limited Memory Bundle Method for Large-Scale Nonsmooth Optimization
School of Computational and Applied Mathematics, University of the Witwatersrand.
Helsinki School of Economics.
Department of Mathematical Information Technology, University of Jyväskylä.
2007 (English)In: Mathematical programming, ISSN 0025-5610, E-ISSN 1436-4646, Vol. 109, no 1, 181-205 p.Article in journal (Refereed) Published
Abstract [en]

Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of thousands of variables. In the paper [Haarala, Miettinen, Mäkelä, Optimization Methods and Software, 19, (2004), pp. 673-692] we have described an efficient method for large-scale nonsmooth optimization. In this paper, we introduce a new variant of this method and prove its global convergence for locally Lipschitz continuous objective functions, which are not necessarily differentiable or convex. In addition, we give some encouraging results from numerical experiments.

Place, publisher, year, edition, pages
2007. Vol. 109, no 1, 181-205 p.
Keyword [en]
Nondifferentiable programming - Large-scale optimization - Bundle methods - Variable metric methods - Limited memory methods - Global convergence
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-83539DOI: 10.1007/s10107-006-0728-2ISI: 000242463900008OAI: oai:DiVA.org:kth-83539DiVA: diva2:498817
Note
QC 20120220Available from: 2012-02-12 Created: 2012-02-12 Last updated: 2017-12-07Bibliographically approved

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