Exploiting additional structure in equality constrained optimization by structured SQP secant algorithms
Identifieur interne : 003185 ( Main/Merge ); précédent : 003184; suivant : 003186Exploiting 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.
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.
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DOI: 10.1007/BF00940716
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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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