Nonmonotone retrospective conic trust region method for unconstrained optimization

We propose a retrospective conic trust region method for unconstrained optimization. It can be regarded as an extension of the retrospective trust region method based on a quadratic model which was first proposed by Bastin et al (2010). Nonmonotone technique is added to accelerate the speed of the algorithm. Under some mild conditions, the sequence generated by our algorithm converges to a stationary point. Numerical tests on a set of standard testing problems confirm the efficiency of our new method.

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