DocumentCode
1624804
Title
Polynomial acceleration of Monte-Carlo global search
Author
Calvin, James M.
Author_Institution
Dept. of Comput. & Inf. Sci., New Jersey Inst. of Technol., Newark, NJ, USA
Volume
1
fYear
1999
fDate
6/21/1905 12:00:00 AM
Firstpage
673
Abstract
We describe a class of algorithms for approximating the global minimum of a function defined on a subset of d-dimensional Euclidean space. The algorithms are based on adaptively composing a number of simple Monte Carlo searches, and use a memory of a fixed finite number of observations. Within the class of algorithms it is possible to obtain arbitrary polynomial speedup in the asymptotic convergence rate compared with simple Monte Carlo
Keywords
Monte Carlo methods; convergence; function approximation; search problems; simulation; Monte-Carlo global search; asymptotic convergence rate; d-dimensional Euclidean space; function approximation; global minimum; observations; polynomial acceleration; simulation; Acceleration; Adaptive algorithm; Algorithm design and analysis; Convergence; Information science; Minimization methods; Monte Carlo methods; Polynomials; Sampling methods; Space technology;
fLanguage
English
Publisher
ieee
Conference_Titel
Simulation Conference Proceedings, 1999 Winter
Conference_Location
Phoenix, AZ
Print_ISBN
0-7803-5780-9
Type
conf
DOI
10.1109/WSC.1999.823184
Filename
823184
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