Abstract
We present a new trust-region algorithm for solving nonlinear equality constrained optimization problems. Quadratic penalty functions are employed to obtain global convergence. At each iteration a local change of variables is performed to improve the ability of the algorithm to follow the constraint level set. Under certain assumptions we prove that this algorithm globally converges to a point satisfying the second-order necessary optimality conditions. Results of preliminary numerical experiments are reported.
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Coleman, T.F., Liu, J. & Yuan, W. A New Trust-Region Algorithm for Equality Constrained Optimization. Computational Optimization and Applications 21, 177–199 (2002). https://doi.org/10.1023/A:1013764800871
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DOI: https://doi.org/10.1023/A:1013764800871