0411229 20240103182311.420060210235959.9 1-4020-7532-4 Dordrecht Kluwer 2003 19 s. 297-315di PilloG.MurliA. semidefinite programming cone programming method of augmented Langrangians cav_un_auth*0101131 Kočvara Michal UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0021060 Stingl M. DE 12A GA201/00/0080 GA ČR cav_un_auth*0005674 03ZOM3ER BMBF DE cav_un_auth*0046476 CEZ:AV0Z1075907 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 are presented. cav_un_auth*0213128 High Performance Algorithms and Software for Nonlinear Optimization Erice IT 30.06.2001-08.07.2001 BA MTR http://hdl.handle.net/11104/0131315 UTIA-B 20030216 2003 2003 Dordrecht Kluwer 1-4020-7532-4 High Performance Algorithms and Software for Nonlinear Optimization 297 315 di Pillo G. 340 Murli A. 340