Outer Trust-Region Method for Constrained Optimization

Ernesto G. Birgin (), Emerson V. Castelani, André L. M. Martinez and J. M. Martínez
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Ernesto G. Birgin: University of São Paulo
Emerson V. Castelani: University of Campinas
André L. M. Martinez: University of Campinas
J. M. Martínez: University of Campinas

Journal of Optimization Theory and Applications, 2011, vol. 150, issue 1, No 9, 142-155

Abstract: Abstract Given an algorithm A for solving some mathematical problem based on the iterative solution of simpler subproblems, an outer trust-region (OTR) modification of A is the result of adding a trust-region constraint to each subproblem. The trust-region size is adaptively updated according to the behavior of crucial variables. The new subproblems should not be more complex than the original ones, and the convergence properties of the OTR algorithm should be the same as those of Algorithm A. In the present work, the OTR approach is exploited in connection with the “greediness phenomenon” of nonlinear programming. Convergence results for an OTR version of an augmented Lagrangian method for nonconvex constrained optimization are proved, and numerical experiments are presented.

Keywords: Nonlinear programming; Augmented Lagrangian method; Trust regions (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10957-011-9815-5

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