Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Programs to generate Niederreiter's low-discrepancy sequences
ACM Transactions on Mathematical Software (TOMS)
Application of Deterministic Low-Discrepancy Sequences in Global Optimization
Computational Optimization and Applications
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In this paper a brief survey of recent developments in the field ofstochastic global optimization methods will be presented. Most methodsdiscussed fall in the category of two-phase algorithms, consisting in aglobal or exploration phase, obtained through sampling in the feasibledomain, and a second or local phase, consisting of refinement of localknowledge, obtained through classical descent routines. A new class ofmethods is also introduced, characterized by the fact that sampling isperformed through deterministic, well distributed, sample points. It isargued that for moderately sized problems this approach might prove moreefficient than those based upon uniform random samples.