A randomized nonmonotone adaptive trust region method based on the simulated annealing strategy for unconstrained optimization. Issue 3 (12th August 2019) - Babaie-Kafaki, Saman; Rezaee, Saeed
Abstract : Purpose: The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems. Design/methodology/approach: The well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region (TR) algorithm. Findings: An adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula. Also, a (heuristic) randomized adaptive TR algorithm is developed for solving unconstrained optimization problems. Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods. Practical implications: The algorithm can be effectively used for solving the optimization problems which appear in engineering, economics, management, industry and other areas. Originality/value: The proposed randomization scheme improves computational costs of the classical TR algorithm. Especially, the suggested algorithm avoids resolving the TR subproblems for many times.