Optimal Monte Carlo Algorithms

Author

Dimov, Ivan T.

fYear

2006

fDate

3-6 Oct. 2006

Firstpage

125

Lastpage

131

Abstract

The question "what Monte Carlo can do and cannot do efficiently" is discussed for some functional spaces that define the regularity of the input data. Important for practical computations data classes are considered: classes of functions with bounded derivatives and Holder type conditions. Theoretical performance analysis of some algorithms with unimprovable rate of convergence is given. Estimates of complexity of two classes of algorithms _eterministic and randomized for the solution of a class of integral equations are presented

Keywords

Monte Carlo methods; computational complexity; convergence; deterministic algorithms; integral equations; randomised algorithms; Holder type condition; algorithm complexity; algorithm performance analysis; data class; deterministic algorithm; functional space; integral equation; optimal Monte Carlo algorithm; randomized algorithm; unimprovable convergence rate; Electronic mail; Integral equations; Mathematics; Monte Carlo methods; Parallel algorithms; Parallel processing; Performance analysis; Physics; Power engineering and energy; Random variables; Monte Carlo algorithms; deterministic algorithms; integral equations; unimprovable rate of convergence.;

fLanguage

English

Publisher

ieee

Conference_Titel

Modern Computing, 2006. JVA '06. IEEE John Vincent Atanasoff 2006 International Symposium on

Conference_Location

Sofia

Print_ISBN

0-7695-2643-8

Type

conf

DOI

10.1109/JVA.2006.37

Filename

4022050