Titel
LMBOPT: a limited memory method for bound-constrained optimization
Autor*in
Behzad Azmi
Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences
Abstract
Recently, Neumaier and Azmi gave a comprehensive convergence theory for a generic algorithm for bound constrained optimization problems with a continuously differentiable objective function. The algorithm combines an active set strategy with a gradient-free line search CLS along a piecewise linear search path defined by directions chosen to reduce zigzagging. This paper describes LMBOPT, an efficient implementation of this scheme. It employs new limited memory techniques for computing the search directions, improves CLS by adding various safeguards relevant when finite precision arithmetic is used, and adds many practical enhancements in other details. The paper compares LMBOPT and several other solvers on the unconstrained and bound constrained problems from the CUTEst collection and makes recommendations on which solver to use and when. Depending on the problem class, the problem dimension, and the precise goal, the best solvers are LMBOPT, ASACG, and LMBFG-EIG-MS.
Stichwort
Bound constrained optimizationExact gradientLimited memory techniqueRobust line search method
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
Erschienen in
Titel
Mathematical Programming Computation
Band
14
Ausgabe
2
ISSN
1867-2949
Erscheinungsdatum
2022
Seitenanfang
271
Seitenende
318
Publication
Springer Science and Business Media LLC
Erscheinungsdatum
2022
Zugänglichkeit
Rechte
Rechteangabe
© The Author(s) 2021
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