0411069 20240103182259.520060210235959.9 17 s. cav_un_epca*02545881055-6788183 (2003)317-333Taylor & Francis convex programming semidefinite programming large-scale problems 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 We introduce a computer program PENNON for the solution of problems of convex Nonlinear and Semidefinite Programming (NLP-SDP). The algorithm used in PENNON is a generalized version of the Augmented Lagrangian method, originally introduced by Ben-Tal and Zibulevsky for convex NLP problems. We present generalization of this algorithm to convex NLP-SDP problems, as implemented in PENNON and details of its implementation. Results of numerical tests and comparison with other optimization codes are presented. BB MTR http://hdl.handle.net/11104/0131156 UTIA-B 20030056 2003 cav_un_epca*0254588 Optimization Methods & Software 1055-6788 1029-4937 Roč. 18 č. 3 2003 317 333 Taylor & Francis