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Exploring or reducing noise?

A global optimization algorithm in the presence of noise


We consider the problem of the global minimization of a function observed with noise. This problem occurs for example when the objective function is estimated through stochastic simulations. We propose an original method for iteratively partitioning the search domain when this area is a finite union of simplexes. On each subdomain of the partition, we compute an indicator measuring if the subdomain is likely or not to contain a global minimizer. Next areas to be explored are chosen in accordance with this indicator. Confidence sets for minimizers are given. Numerical applications show empirical convergence results, and illustrate the compromise to be made between the global exploration of the search domain and the focalization around potential minimizers of the problem.

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The authors would like to thank the anonymous reviewers and professor Ragnar Norberg for their valuable comments and suggestions.

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Correspondence to Didier Rullière.

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This work was presented in part at the Noisy Kriging-based Optimization (NKO) workshop, Bern, November 2010. It has been partially funded by ANR Research project ANR-08-BLAN-0314-01, by a grant from ANRT with reference 177/2008, by MIRACCLEGICC project, and Chaire BNP Paribas Cardif Management de la modélisation.

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Rullière, D., Faleh, A., Planchet, F. et al. Exploring or reducing noise?. Struct Multidisc Optim 47, 921–936 (2013).

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  • Global optimization
  • Noise
  • Potential
  • Branch-and-Bound
  • Simplex
  • Kriging