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Exploiting additional structure in equality constrained optimization by structured SQP secant algorithms

Identifieur interne : 002C34 ( Main/Exploration ); précédent : 002C33; suivant : 002C35

Exploiting additional structure in equality constrained optimization by structured SQP secant algorithms

Auteurs : J. Huschens [Allemagne]

Source :

Journal of Optimization Theory and Applications [ 0022-3239 ] ; 1993-05-01.

RBID : ISTEX:413D2BA509A30DB35DE91F245EBBA8DA5001BDD6

Descripteurs français

Pascal (Inist)
- Contrainte égalité, Convergence, Optimisation sous contrainte, Problème moindre carré, Programmation quadratique, Secant method.

English descriptors

KwdEn :
- Constrained optimization, Convergence, Equality constraint, Least squares problem, Quadratic programming.

Abstract

Abstract: In 1988, Tapia (Ref. 1) developed and analyzed SQP secant methods in equality constrained optimization taking explicitly the additive structure of the problem setting into account. In this paper, we extend Tapia's augmented scale Lagrangian secant method to the case where additional structure coming from the objective function is available. Using the example of nonlinear least squares with equality constraints, we demonstrate these ideas and develop a convergence theory proving local and q-superlinear convergence for this kind of structured SQP-algorithms.

Url:

https://api.istex.fr/document/413D2BA509A30DB35DE91F245EBBA8DA5001BDD6/fulltext/pdf

DOI: 10.1007/BF00940716

Affiliations:

Allemagne

Le document en format XML

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<front><div type="abstract" xml:lang="en">Abstract: In 1988, Tapia (Ref. 1) developed and analyzed SQP secant methods in equality constrained optimization taking explicitly the additive structure of the problem setting into account. In this paper, we extend Tapia's augmented scale Lagrangian secant method to the case where additional structure coming from the objective function is available. Using the example of nonlinear least squares with equality constraints, we demonstrate these ideas and develop a convergence theory proving local and q-superlinear convergence for this kind of structured SQP-algorithms.</div>
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Exploiting additional structure in equality constrained optimization by structured SQP secant algorithms

Exploiting additional structure in equality constrained optimization by structured SQP secant algorithms

Source :

Descripteurs français

English descriptors

Abstract

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