PENNON: A code for convex nonlinear and semidefinite programming

0411069 - UTIA-B 20030056 RIV GB eng J - Článek v odborném periodiku
Kočvara, Michal - Stingl, M.
PENNON: A code for convex nonlinear and semidefinite programming.
Optimization Methods & Software. Roč. 18, č. 3 (2003), s. 317-333. ISSN 1055-6788. E-ISSN 1029-4937
Grant CEP: GA ČR GA201/00/0080
Grant ostatní: BMBF(DE) 03ZOM3ER
Výzkumný záměr: CEZ:AV0Z1075907
Klíčová slova: convex programming * semidefinite programming * large-scale problems
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
Impakt faktor: 0.306, rok: 2003

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.
Trvalý link: http://hdl.handle.net/11104/0131156