Large-scale quasi-Newton trust-region methods with low-dimensional linear equality constraints

Autor:	Cosmin G. Petra, Roummel F. Marcia, Johannes J. Brust
Rok vydání:	2019
Předmět:	Trust region Mathematical optimization 021103 operations research Control and Optimization Scale (ratio) Applied Mathematics 0211 other engineering and technologies Structure (category theory) 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Computational Mathematics Broyden–Fletcher–Goldfarb–Shanno algorithm Convergence (routing) 0101 mathematics Variety (universal algebra) Representation (mathematics) Mathematics
Zdroj:	Computational Optimization and Applications. 74:669-701
ISSN:	1573-2894 0926-6003
DOI:	10.1007/s10589-019-00127-4
Popis:	We propose two limited-memory BFGS (L-BFGS) trust-region methods for large-scale optimization with linear equality constraints. The methods are intended for problems where the number of equality constraints is small. By exploiting the structure of the quasi-Newton compact representation, both proposed methods solve the trust-region subproblems nearly exactly, even for large problems. We derive theoretical global convergence results of the proposed algorithms, and compare their numerical effectiveness and performance on a variety of large-scale problems.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b42ca6b2d6bd34fc01569a82c91ce171 https://doi.org/10.1007/s10589-019-00127-4 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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