Randomized algorithm for global optimization with bounded memory

Authors

James M. Calvin, Department of Computer Science

Document Type

Article

Publication Date

2-1-2010

Abstract

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. © 2008 IMACS.

Identifier

77649271570 (Scopus)

Publication Title

Mathematics and Computers in Simulation

External Full Text Location

https://doi.org/10.1016/j.matcom.2008.11.001

ISSN

03784754

First Page

1068

Last Page

1081

Issue

6

Volume

80

Fund Ref

National Science Foundation

Recommended Citation

Calvin, James M., "Randomized algorithm for global optimization with bounded memory" (2010). Faculty Publications. 6407.
https://digitalcommons.njit.edu/fac_pubs/6407