Stochastic global optimization methods part II: Multi level methods

Author:

Rinnooy Kan A. H. G.,Timmer G. T.

Publisher

Springer Science and Business Media LLC

Subject

General Mathematics,Software

Reference17 articles.

1. J.L. Bentley, B.W. Weide and A.C. Yao, “Optimal expected-time algorithms for closest point problems,”ACM Transactions on Mathematical Software 6 (1980) 563–580.

2. C.G.E. Boender and A.H.G. Rinnooy Kan, “Bayesian stopping rules for a class of stochastic global optimization methods,” Technical Report, Econometric Institute, Erasmus University Rotterdam (1985).

3. F.H. Branin and S.K. Hoo, “A method for finding multiple extrema of a function ofn variables,” in: F.A. Lootsma, ed.,Numerical Methods of Nonlinear Optimization (Academic Press, London, 1972) pp. 231–237.

4. H. Bremmerman, “A method of unconstrained global optimization,”Mathematical Biosciences 9 (1970) 1–15.

5. L. De Biase and F. Frontini, “A stochastic method for global optimization: its structure and numerical performance” (1978), in: Dixon and Szegö (1978a) pp. 85–102.

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