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Randomized algorithm for global optimization with bounded memory

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  • Calvin, James M.

Abstract

We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of Rd. The algorithms are adaptive versions of Monte Carlo search and use a memory of a fixed number of past observations. By choosing a large enough memory, the convergence rate can be made to exceed any power of the convergence rate obtained with standard Monte Carlo search.

Suggested Citation

  • Calvin, James M., 2010. "Randomized algorithm for global optimization with bounded memory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(6), pages 1068-1081.
    • Handle: RePEc:eee:matcom:v:80:y:2010:i:6:p:1068-1081
    DOI: 10.1016/j.matcom.2008.11.001
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