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 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.

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Year of publication:	2010
Authors:	Calvin, James M.
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 80.2010, 6, p. 1068-1081
Publisher:	Elsevier
Subject:	Global optimization \| Monte Carlo methods \| Parallel algorithm \| Point process \| Randomized algorithm

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010869991