LMBOPT: a limited memory method for bound-constrained optimization

  • AbstractRecently, 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 along a piecewise linear search path defined by directions chosen to reduce zigzagging. This paper describes , an efficient implementation of this scheme. It employs new limited memory techniques for computing the search directions, improves by adding various safeguards relevant when finite precision arithmetic is used, and adds many practical enhancements in other details. The paper compares and several other solvers on the unconstrained and bound constrained problems from the 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 and .

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Metadaten
Author:Morteza Kimiaei, Arnold Neumaier, Behzad Azmi
DOI:https://doi.org/10.1007/s12532-021-00213-x
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2022
Volume:14
Issue:2
Page Number:48
First Page:271
Last Page:318
Mathematical Programming Computation :MPC 2022 - Issue 2
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