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A hybrid global optimization method: The multi-dimensional case. (English) Zbl 1020.65033
Summary: We extend the hybrid global optimization method proposed by P. Xu [J. Comput. Appl. Math. 147, 301-314 (2002; Zbl 1013.65063)] for the one-dimensional case to the multi-dimensional case. The method consists of two basic components: local optimizers and feasible point finders. Local optimizers guarantee efficiency and speed of producing a local optimal solution in the neighbourhood of a feasible point. Feasible point finders provide the theoretical guarantee for the new method to always produce the global optimal solution(s) correctly. If a nonlinear nonconvex inverse problem has multiple global optimal solutions, our algorithm is capable of finding all of them correctly. Three synthetic examples, which have failed simulated annealing and genetic algorithms, are used to demonstrate the proposed method.
65K05Mathematical programming (numerical methods)
90C30Nonlinear programming
90C53Methods of quasi-Newton type