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Quasi-monte Carlo Method




Monte Carlo and quasi-Monte Carlo methods are stated in a similar way.
The problem is to approximate the integral of a function ''f'' as the average of the function evaluated at a set of points ''x''1, ..., ''x''''N''.

: \int_{\bar I^s} f(u)\,du \approx rac{1}{N}\,\sum_{i=1}^N f(x_i),

where Ī''s'' is the ''s''-dimensional unit cube, Ī''s'' = 1 × ... × 1 . (Thus each ''x''''i'' is a vector of ''s'' elements.)
In a Monte Carlo method,
the set ''x''1, ..., ''x''''N'' is a subsequence
of pseudorandom numbers.
In a quasi-Monte Carlo method,
the set is a subsequence of a low-discrepancy sequence.

The approximation error of a method of the above type is bounded by a term proportional to the discrepancy of the set ''x''1, ..., ''x''''N'', by the Koksma-Hlawka Inequality .
The discrepancy of sequences typically used for the quasi-Monte Carlo method is bounded by a constant times

: rac{(\log N)^s}{N}.

In comparison, with probability one, the expected discrepancy of a uniform random sequence (as used in the Monte Carlo method) has an order of convergence

: \sqrt{ rac{\log \log N}{2N}}

by the Law Of The Iterated Logarithm .

Thus it would appear that the accuracy of the quasi-Monte Carlo method increases faster than that of the Monte Carlo method. However, Morokoff and Caflisch cite examples of problems in which the advantage of the quasi-Monte Carlo is less than expected theoretically. Still, in the examples studied by Morokoff and Caflisch, the quasi-Monte Carlo method did yield a more accurate result than the Monte Carlo method with the same number of points.

Morokoff and Caflisch remark that the advantage of the quasi-Monte Carlo method is greater if the integrand is smooth, and the number of dimensions ''s'' of the integral is small. A technique, coined randomized quasi-Monte Carlo, that mixes quasi-Monte Carlo with traditional Monte Carlo, extends the benefits of quasi-Monte Carlo to medium to large ''s''.


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REFERENCES


  • Michael Drmota and Robert F. Tichy, ''Sequences, discrepancies and applications'', Lecture Notes in Math., 1651, Springer, Berlin, 1997, ISBN 3-540-62606-9

  • Harald Niederreiter. ''Random Number Generation and Quasi-Monte Carlo Methods.'' Society for Industrial and Applied Mathematics, 1992. ISBN 0-89871-295-5

  • Harald G. Niederreiter, ''Quasi-Monte Carlo methods and pseudo-random numbers'', Bull. Amer. Math. Soc. 84 (1978), no. 6, 957--1041

  • William J. Morokoff and Russel E. Caflisch, ''Quasi-random sequences and their discrepancies'', SIAM J. Sci. Comput. 15 (1994), no. 6, 1251--1279 ''(At CiteSeer : {Link without Title} )''

  • William J. Morokoff and Russel E. Caflisch, ''Quasi-Monte Carlo integration'', J. Comput. Phys. 122 (1995), no. 2, 218--230. ''(At CiteSeer : {Link without Title} )''