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A New Trust Region algorithm for Equality Constrained Optimization

Author
Coleman, Thomas F.; Yuan, Wei
Abstract
We present a new trust region algorithms for solving nonlinear equality constrained optimization problems. At each iterate a change of variables is performed to improve the ability of the algorithm to follow the constraint level sets. The algorithm employs $L_{2}$ penalty functions for obtaining global convergence. Under certain assumptions we prove that this algorithm globally converges to a point satisfying the second order necessary optimality conditions; the local convergence rate is quadratic. Results of preliminary numerical experiments are presented.
Date Issued
1995-03Publisher
Cornell University
Subject
computer science; technical report
Previously Published As
http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR95-1477
Type
technical report