bibtype J - Journal Article
ARLID 0411069
utime 20240103182259.5
mtime 20060210235959.9
title (primary) (eng) PENNON: A code for convex nonlinear and semidefinite programming
specification
page_count 17 s.
serial
ARLID cav_un_epca*0254588
ISSN 1055-6788
title Optimization Methods & Software
volume_id 18
volume 3 (2003)
page_num 317-333
publisher
name Taylor & Francis
keyword convex programming
keyword semidefinite programming
keyword large-scale problems
author (primary)
ARLID cav_un_auth*0101131
name1 Kočvara
name2 Michal
institution UTIA-B
full_dept Department of Decision Making Theory
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0021060
name1 Stingl
name2 M.
country DE
COSATI 12A
cas_special
project
project_id GA201/00/0080
agency GA ČR
ARLID cav_un_auth*0005674
project
project_id 03ZOM3ER
agency BMBF
country DE
ARLID cav_un_auth*0046476
research CEZ:AV0Z1075907
abstract (eng) 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.
RIV BB
department MTR
permalink http://hdl.handle.net/11104/0131156
ID_orig UTIA-B 20030056
arlyear 2003
mrcbU63 cav_un_epca*0254588 Optimization Methods & Software 1055-6788 1029-4937 Roč. 18 č. 3 2003 317 333 Taylor & Francis