A Retrospective Trust-Region Method for Unconstrained Optimiation, M.Sc. Thesis Sharif University of Technology ; Mahdavi-Amiri, Nezamoddin (Supervisor)
Abstract
We explain a new trust-region method for solving unconstrained optimization problems recently introduced in the literature, where the radius update is computed using the information at the current iterate rather than at the preceeding one. The update is then performed according to how well the current model retrospectively predicts the value of the objective function at the last iterate. Global convergence to first- and second-order critical ponits is proved under classical assumptions. Preliminary numerical expriments on CUTEr problems with MATLAB7.7 indicate that the new method is very competitive
Cataloging briefA Retrospective Trust-Region Method for Unconstrained Optimiation, M.Sc. Thesis Sharif University of Technology ; Mahdavi-Amiri, Nezamoddin (Supervisor)
Abstract
We explain a new trust-region method for solving unconstrained optimization problems recently introduced in the literature, where the radius update is computed using the information at the current iterate rather than at the preceeding one. The update is then performed according to how well the current model retrospectively predicts the value of the objective function at the last iterate. Global convergence to first- and second-order critical ponits is proved under classical assumptions. Preliminary numerical expriments on CUTEr problems with MATLAB7.7 indicate that the new method is very competitive
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