Limited memory bundle algorithm for large bound constrained nonsmooth minization problems
Abstract
Typically, practical optimization problems involve nonsmooth functions
of hundreds or thousands of variables. As a rule, the variables in such problems
are restricted to certain meaningful intervals. In this paper, we propose
an efficient adaptive limited memory bundle method for large-scale nonsmooth,
possibly nonconvex, bound constrained optimization. The method
combines the nonsmooth variable metric bundle method and the smooth
limited memory variable metric method, while the constraint handling is
based on the projected gradient method and the dual subspace minimization.
The preliminary numerical experiments to be presented confirm the
usability of the method.
Main Authors
Format
Report
Published
2006
Series
ISBN
951-39-2418-1
Publisher
University of Jyväskylä
The permanent address of the publication
https://urn.fi/URN:ISBN:951-39-2418-1Käytä tätä linkitykseen.
ISSN
1456-436X
Language
English
Published in
Reports of the Department of Mathematical Information Technology. Series B, Scientific computing
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