Randomized algorithm for global optimization with bounded memory

James M. Calvin

Mathematics and Computers in Simulation (MATCOM), 2010, vol. 80, issue 6, 1068-1081

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.

Keywords: Global optimization; Monte Carlo methods; Parallel algorithm; Point process; Randomized algorithm (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:80:y:2010:i:6:p:1068-1081

DOI: 10.1016/j.matcom.2008.11.001

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