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PENNON: A code for convex nonlinear and semidefinite programming
- 1.0411069 - UTIA-B 20030056 RIV GB eng J - Journal Article
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
R&D Projects: GA ČR GA201/00/0080
Grant - others:BMBF(DE) 03ZOM3ER
Institutional research plan: CEZ:AV0Z1075907
Keywords : convex programming * semidefinite programming * large-scale problems
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 0.306, year: 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.
Permanent Link: http://hdl.handle.net/11104/0131156
Number of the records: 1