Kocvara M, Stingl M (2003)
Publication Status: Published
Publication Type: Book chapter / Article in edited volumes
Publication year: 2003
Edited Volumes: High Performance Algorithms and Software for Nonlinear Optimization
Series: Applied Optimization
Book Volume: 82
Pages Range: 303-321
DOI: 10.1007/978-1-4613-0241-4_14
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.
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.
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