Globally convergent limited memory bundle method for large-scale nonsmooth optimization

Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of thousands of variables. In the paper [Haarala, Miettinen, Makela, Optimization Methods and Software, 19, (2004), pp. 673-692] we have described an efficient method for large-scale nonsmooth...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical programming 2007, Vol.109 (1), p.181-205
Main Authors: HAARALA, Napsu, MIETTINEN, Kaisa, MÄKELÄ, Marko M
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Convergence
Exact sciences and technology
Experiments
Mathematical models
Mathematical programming
Methods
Nondifferentiable programming - Large-scale optimization - Bundle methods - Variable metric methods - Limited memory methods - Global convergence
Operational research and scientific management
Operational research. Management science
Optimization techniques
Studies
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of thousands of variables. In the paper [Haarala, Miettinen, Makela, 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. [PUBLICATION ABSTRACT]
ISSN:0025-5610
1436-4646
1436-4646
DOI:10.1007/s10107-006-0728-2