Thursday, 13:45 - 14:10 h, Room: H 0107


Xiao Wang
An augmented Lagrangian trust region method for nonlinear programming

Coauthor: Ya-xiang Yuan


We present a new trust region method for solving
equality constrained optimization problems, which is motivated by the famous augmented Lagrangian function. Different from the standard augmented Lagrangian method where the augmented Lagrangian function is minimized at each iteration, the new method, for fixed Lagrange multiplier and penalty parameter, tries to minimize an approximation model to the augmented Lagrangian function in a trust region to generate next iterate. Besides, new update strategies for Lagrange multipliers and penalty parameters are proposed. Global convergence of the new algorithm is proved in this paper. Moreover, we analyze the behavior of penalty parameters and figure out in which case when they are bounded. At last, we do some numerical experiments on the equality constrained problems from CUTEr collection. We also consider extending the idea to general constrained optimization. Some numerical results are reported too.


Talk 2 of the invited session Thu.2.H 0107
"Algorithms and applications I" [...]
Cluster 16
"Nonlinear programming" [...]


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