A Generalized Augmented Lagrangian Method for Semidefinite Programming
  • Michal Kočvara
  • Michael Stingl
Part of the Applied Optimization book series (APOP, volume 82)


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.


semidefinite programming cone programming method of augmented Lagrangians 


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Copyright information

© Kluwer Academic Publishers B.V. 2003

Authors and Affiliations

  • Michal Kočvara
    • 1
  • Michael Stingl
    • 1
  1. 1.Institute of Applied MathematicsUniversity of ErlangenErlangenGermany

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