A retrospective trust-region method for unconstrained optimization

作者：Bastin Fabian^*; Malmedy Vincent; Mouffe Melodie; Toint Philippe L; Tomanos Dimitri

来源：Mathematical Programming, 2010, 123(2): 395-418.

DOI：10.1007/s10107-008-0258-1

• Unconstrained minimization
• Trust-region methods
• Convergence theory
• Numerical experiments

We introduce a new trust-region method for unconstrained optimization where the radius update is computed using the model information at the current iterate rather than at the preceding one. The update is then performed according to how well the current model retrospectively predicts the value of the objective function at last iterate. Global convergence to first- and second-order critical points is proved under classical assumptions and preliminary numerical experiments on CUTEr problems indicate that the new method is very competitive.

• 出版日期2010-6