A Nonmonotone Trust Region Algorithm Based on the Average of the Successive Penalty Function Values for Nonlinear Optimization | Zendy

Zhensheng Yu | Zendy; Jinhong Yu | Zendy

Open Access

A Nonmonotone Trust Region Algorithm Based on the Average of the Successive Penalty Function Values for Nonlinear Optimization

Author(s) -

Zhensheng Yu,

Jinhong Yu

Publication year - 2013

Publication title -

isrn operations research

Language(s) - English

Resource type - Journals

ISSN - 2314-6397

DOI - 10.1155/2013/495378

Subject(s) - trust region , penalty method , convergence (economics) , reduction (mathematics) , mathematical optimization , nonlinear system , function (biology) , mathematics , algorithm , optimization algorithm , optimization problem , computer science , physics , geometry , computer security , quantum mechanics , evolutionary biology , economics , radius , biology , economic growth

We present a nonmonotone trust region algorithm for nonlinear equality constrained optimization problems. In our algorithm, we use the average of the successive penalty function values to rectify the ratio of predicted reduction and the actual reduction. Compared with the existing nonmonotone trust region methods, our method is independent of the nonmonotone parameter. We establish the global convergence of the proposed algorithm and give the numerical tests to show the efficiency of the algorithm.

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