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Variable Metric Methods for Unconstrained Optimization and Nonlinear Least Squares
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SYSNO ASEP 0403533 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Variable Metric Methods for Unconstrained Optimization and Nonlinear Least Squares Author(s) Lukšan, Ladislav (UIVT-O) SAI, RID
Spedicato, E. (IT)Source Title Journal of Computational and Applied Mathematics. - : Elsevier - ISSN 0377-0427
Roč. 124, č. 1-2 (2000), s. 61-95Number of pages 35 s. Language eng - English Country NL - Netherlands Keywords quasi-Newton methods ; variable metric methods ; unconstrained optimization ; nonlinear least squares ; sparse problems ; partially separable problems ; limited-memory methods Subject RIV BA - General Mathematics R&D Projects GA201/00/0080 GA ČR - Czech Science Foundation (CSF) CEZ 1030915 UT WOS 000165411000005 EID SCOPUS 0034544838 DOI 10.1016/S0377-0427(00)00420-9 Annotation Variable metric or quasi-Newton methods were originally developed for small- and moderate-size dense problems, their modifications based either on sparse, partitioned or limited-memory updates are very efficient on large-scale sparse problems. Very significant applications of these methods also appear in nonlinear least-squares approximation and nonsmooth optimization. An extensive review of variable metric methods and their use in various optimization fields. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2001
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