Randomized algorithm for global optimization with bounded memory

We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of R d . 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 ra...

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Bibliographic Details
Published inMathematics and computers in simulation Vol. 80; no. 6; pp. 1068 - 1081
Main Author Calvin, James M.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.02.2010
Subjects
Global optimization
Monte Carlo methods
Parallel algorithm
Point process
Randomized algorithm
Global optimization
Monte Carlo methods
Parallel algorithm
Randomized algorithm
Point process
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Summary:We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of R d . 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.
ISSN:0378-4754
1872-7166
DOI:10.1016/j.matcom.2008.11.001