A New Trust-Region Algorithm for General Nonlinear Programming
A new trust-region algorithm for solving the general nonlinear programming problem is introduced. In this algorithm, an active set strategy is used together with a projected Hessian technique to convert the computation of the trial step to two easy trust-region subproblems similar to those for the unconstrained case. To force global convergence, the augmented Lagrangian for general nonlinear programming is used as a merit function. A convergence theory for this algorithm is presented. Under reasonable assumptions, it is shown that the algorithm is globally convergent. Numerical experiment on the algorithm is presented. The performance of the algorithm is reported. The numerical results show that our approach is of value and merits further investigation.
Citable link to this pagehttps://hdl.handle.net/1911/101894
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- CAAM Technical Reports