Newton-type methods for unconstrained and linearly constrained optimization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1974-12

AUTHORS

Philip E. Gill, Walter Murray

ABSTRACT

This paper describes two numerically stable methods for unconstrained optimization and their generalization when linear inequality constraints are added. The difference between the two methods is simply that one requires the Hessian matrix explicitly and the other does not. The methods are intimately based on the recurrence of matrix factorizations and are linked to earlier work on quasi-Newton methods and quadratic programming. More... »

PAGES

311-350

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01585529

DOI

http://dx.doi.org/10.1007/bf01585529

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045559631


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