An efficient nonmonotone trust‐region method for unconstrained optimization

An efficient nonmonotone trust‐region method for unconstrained optimization

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Article ID: iaor20122833
Volume: 59
Issue: 4
Start Page Number: 523
End Page Number: 540
Publication Date: Apr 2012
Journal: Numerical Algorithms
Authors: ,
Keywords: trust regions
Abstract:

The monotone trust‐region methods are well‐known techniques for solving unconstrained optimization problems. While it is known that the nonmonotone strategies not only can improve the likelihood of finding the global optimum but also can improve the numerical performance of approaches, the traditional nonmonotone strategy contains some disadvantages. In order to overcome to these drawbacks, we introduce a variant nonmonotone strategy and incorporate it into trust‐region framework to construct more reliable approach. The new nonmonotone strategy is a convex combination of the maximum of function value of some prior successful iterates and the current function value. It is proved that the proposed algorithm possesses global convergence to first‐order and second‐order stationary points under some classical assumptions. Preliminary numerical experiments indicate that the new approach is considerably promising for solving unconstrained optimization problems.

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