Journal ArticleDOI
High-dimensional integration: The quasi-Monte Carlo way
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A survey of recent developments in lattice methods, digital nets, and related themes can be found in this paper, where the authors present a contemporary review of QMC (quasi-Monte Carlo) methods, that is, equalweight rules for the approximate evaluation of high-dimensional integrals over the unit cube [0, 1] s, w heres may be large, or even infinite.Abstract:
This paper is a contemporary review of QMC (‘quasi-Monte Carlo’) methods, that is, equal-weight rules for the approximate evaluation of high-dimensional integrals over the unit cube [0, 1] s ,w heres may be large, or even infinite. After a general introduction, the paper surveys recent developments in lattice methods, digital nets, and related themes. Among those recent developments are methods of construction of both lattices and digital nets, to yield QMC rules that have a prescribed rate of convergence for sufficiently smooth functions, and ideally also guaranteed slow growth (or no growth) of the worst-case error as s increases. A crucial role is played by parameters called ‘weights’, since a careful use of the weight parameters is needed to ensure that the worst-case errors in an appropriately weighted function space are bounded, or grow only slowly, as the dimension s increases. Important tools for the analysis are weighted function spaces, reproducing kernel Hilbert spaces, and discrepancy, all of which are discussed with an appropriate level of detail.read more
Citations
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Multilevel Monte Carlo methods
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Quasi-Monte Carlo finite element methods for elliptic PDEs with lognormal random coefficients
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TL;DR: In this article, the authors studied a new closed set of functions normal and orthogonal on the interval (0, 1) for the interval 0 5 x 5 1, where each function takes only the values + 1 and − 1, except at a finite number of points of discontinuity, where it takes the value zero.
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Quasi-Monte Carlo methods and pseudo-random numbers
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Algorithm 659: Implementing Sobol's quasirandom sequence generator
Paul Bratley,Bennett L. Fox +1 more
TL;DR: It is compared empirically accuracy and speed of low-discrepancy sequence generators of Sobol' and Faure to find out which are more useful for multidimensional integration and global optimization.