A nonmonotone trust region method based on simple conic models for unconstrained optimization

A new nonmonotone trust region algorithm with simple conic models for unconstrained optimization is proposed. Compared to traditional conic trust region methods, the new method needs less memory capacitance and computational complexity. The global convergence and fast local convergence rate of the p...

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Bibliographic Details
Published in:Applied mathematics and computation 2013-12, Vol.225, p.295-305
Main Authors: Zhou, Qunyan, Zhou, Fen, Cao, Fengxue
Format: Article
Language:English
Subjects:
Algorithms
Capacitance
Computation
Conics
Convergence
Global convergence
Mathematical analysis
Mathematical models
Nonmonotone trust region method
Optimization
Simple conic model
Unconstrained optimization
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Summary:A new nonmonotone trust region algorithm with simple conic models for unconstrained optimization is proposed. Compared to traditional conic trust region methods, the new method needs less memory capacitance and computational complexity. The global convergence and fast local convergence rate of the proposed algorithm are established under some reasonable conditions. Numerical tests indicate that the new algorithm is efficient and robust.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2013.09.038