Superlinearly convergent variable metric algorithms for general nonlinear programming problems View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1976-12

AUTHORS

Shih-Ping Han

ABSTRACT

In this paper sufficient conditions for local and superlinear convergence to a Kuhn—Tucker point are established for a class of algorithms which may be broadly defined and comprise a quadratic programming algorithm for repeated solution of a subproblem and a variable metric update to develop the Hessian in the subproblem. In particular the DFP update and an update attributed to Powell are shown to provide a superlinear convergent subclass of algorithms provided a start is made sufficiently close to the solution and the initial Hessian in the subproblem is sufficiently close to the Hessian of the Lagrangian at this point. More... »

PAGES

263-282

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01580395

DOI

http://dx.doi.org/10.1007/bf01580395

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1053302753


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