Pseudorandom lattices for global optimization
Rationalization processes are proposed to improve uniformity in small samples for pseudorandom lattices in (0, 1)(n) constructed from sequences produced by random number generators. On this basis, the space filtration and space contraction algorithms are developed for the solution of multimodal global optimization problems. Strong convergence to the global minimum value and convergence in measure onto the set of all global minimizers are proved. Numerical experiments are presented to illustrate a better uniformity provided by a rationalization process and the use of the space filtration algorithm for global optimization. (C) 2003 Elsevier Science Ltd. All rights reserved.
global optimization random lattices small sample uniformity