Nonlinear programming algorithms using trust regions and augmented Lagrangians with nonmonotone penalty parameters |
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| Authors: | Francisco A M Gomes María Cristina Maciel José Mario Martínez |
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| Institution: | (1) Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970 Campinas SP, Brazil, chico@ime.unicamp.br. This author was supported by FAPESP (Grant 90-3724-6), FINEP and FAEP-UNICAMP, BR;(2) Department of Mathematics, Universidad Nacional del Sur, Av. Alem 1253, 8000 Bahía Blanca, Argentina, immaciel@criba.edu.ar. This author was supported by FAPESP (Grant 94-1503-3) and by Fundación ANTORCHAS (Grant A-13219/1-000067), AR;(3) Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970 Campinas SP, Brazil, martinez@ime.unicamp.br. This author was supported by FAPESP (Grant 90-3724-6), CNPq and FAEP-UNICAMP, BR |
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| Abstract: | The strategy for obtaining global convergence is based on the trust region approach. The merit function is a type of augmented
Lagrangian. A new updating scheme is introduced for the penalty parameter, by means of which monotone increase is not necessary.
Global convergence results are proved and numerical experiments are presented.
Received May 31, 1995 / Revised version received December 12, 1997 Published online October 21, 1998 |
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| Keywords: | : nonlinear programming – successive quadratic programming – trust regions – augmented Lagrangians – Lipschitz conditions |
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