PENNON - A generalized augmented Lagrangian method for semidefinite programming

Beitrag in einem Sammelwerk
(Originalarbeit)


Details zur Publikation

Autorinnen und Autoren: Kocvara M, Stingl M
Herausgeber: Gianni Di Pillo, Almerico Murli
Titel Sammelwerk: High Performance Algorithms and Software for Nonlinear Optimization
Jahr der Veröffentlichung: 2003
Titel der Reihe: Applied Optimization
Band: 82
Seitenbereich: 303-321
ISSN: 1384-6485


Abstract


This article describes a generalization of the PBM method by Ben-Tal and Zibulevsky to convex semidefinite programming problems. The algorithm used is a generalized version of the Augmented Lagrangian method. We present details of this algorithm as implemented in a new code PENNON. The code can also solve second-order conic programming (SOCP) problems, as well as problems with a mixture of SDP, SOCP and NLP constraints. Results of extensive numerical tests and comparison with other SDP codes are presented.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Stingl, Michael Prof. Dr.
Professur für Angewandte Mathematik (Kontinuierliche Optimierung)


Zitierweisen

APA:
Kocvara, M., & Stingl, M. (2003). PENNON - A generalized augmented Lagrangian method for semidefinite programming. In Gianni Di Pillo, Almerico Murli (Eds.), High Performance Algorithms and Software for Nonlinear Optimization. (pp. 303-321).

MLA:
Kocvara, Michal, and Michael Stingl. "PENNON - A generalized augmented Lagrangian method for semidefinite programming." High Performance Algorithms and Software for Nonlinear Optimization. Ed. Gianni Di Pillo, Almerico Murli, 2003. 303-321.

BibTeX: 

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