Variable Metric Methods for Constrained Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1983

AUTHORS

M. J. D. Powell

ABSTRACT

Variable metric methods solve nonlinearly constrained optimization problems, using calculated first derivatives and a single positive definite matrix, which holds second derivative information that is obtained automatically. The theory of these methods is shown by analysing the global and local convergence properties of a basic algorithm, and we find that superlinear convergence requires less second derivative information than in the unconstrained case. Moreover, in order to avoid the difficulties of inconsistent linear approximations to constraints, careful consideration is given to the calculation of search directions by unconstrained minimization subproblems. The Maratos effect and relations to reduced gradient algorithms are studied briefly. More... »

PAGES

288-311

Book

TITLE

Mathematical Programming The State of the Art

ISBN

978-3-642-68876-8
978-3-642-68874-4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-68874-4_12

DOI

http://dx.doi.org/10.1007/978-3-642-68874-4_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038037500


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