PENNON: A code for convex nonlinear and semidefinite programming

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SYSNO ASEP	0411069
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Ostatní články
Title	PENNON: A code for convex nonlinear and semidefinite programming
Author(s)	Kočvara, Michal (UTIA-B)_{RID, ORCID} Stingl, M. (DE)
Source Title	Optimization Methods & Software. - : Taylor & Francis - ISSN 1055-6788 Roč. 18, č. 3 (2003), s. 317-333
Number of pages	17 s.
Language	eng - English
Country	GB - United Kingdom
Keywords	convex programming ; semidefinite programming ; large-scale problems
Subject RIV	BB - Applied Statistics, Operational Research
R&D Projects	GA201/00/0080 GA ČR - Czech Science Foundation (CSF)
CEZ	AV0Z1075907 - UTIA-B
Annotation	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.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.

Number of the records: 1