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Properties of the augmented Lagrangian in nonlinear semidefinite optimization

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Indexed by:期刊论文
Journal:JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
Included Journals:SCIE、Scopus
Volume:129
Issue:3
Page Number:437-456
ISSN No.:0022-3239
Key Words:semidefinite programming; augmented Lagrangians; convergence
Abstract:We study the properties of the augmented Lagrangian function for nonlinear semidefinite programming. It is shown that, under a set of sufficient conditions, the augmented Lagrangian algorithm is locally convergent when the penalty parameter is larger than a certain threshold. An error estimate of the solution, depending on the penalty parameter, is also established.
Date of Publication:2006-06-01

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