1998
DOI: 10.1016/s0378-4754(98)00100-1
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Simulation of diffusion using quasi-random walk methods

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Cited by 12 publications
(7 citation statements)
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“…Brought to you by | Purdue University Libraries Authenticated Download Date | 5/29/15 1:45 PM A QMC version of the previous scheme has been analyzed in [2]. The basic idea of QMC methods is to replace the pseudo-random points of MC methods by low-discrepancy point sets.…”
Section: Random Walk On a Latticementioning
confidence: 99%
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“…Brought to you by | Purdue University Libraries Authenticated Download Date | 5/29/15 1:45 PM A QMC version of the previous scheme has been analyzed in [2]. The basic idea of QMC methods is to replace the pseudo-random points of MC methods by low-discrepancy point sets.…”
Section: Random Walk On a Latticementioning
confidence: 99%
“…This approach may Brought to you by | Purdue University Libraries Authenticated Download Date | 5/29/15 1:45 PM be extended to several dimensions. In [2], convergence of the algorithm is established in the multi-dimensional case; the results of computational experiments indicate that a significant improvement is achieved over standard random walk simulation.…”
Section: Random Walk On a Latticementioning
confidence: 99%
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“…[19]. On the other hand, some authors did use quasi-random sequences to obtain approximate solutions to partial differential equations simulating quasi-random paths of continuous stochastic processes, as it was done here, see for instance [19,22,27,28], to quote just a few. In [27], it was shown that simulating quasi-random walks can be advantageous even to solve certain nonlinear equations.…”
Section: The Numerical Errors In Computing Nodal Valuesmentioning
confidence: 99%
“…Of primary importance is the even distribution of the points. A review of the development of this area is given in the monograph [3] by Niederreiter. A randomized algorithm for solving the initial value problem for the finite dimensional system y (t) = f t, y(t) , 0 < t < T, (1) y(0) = y 0 (2) was recently proposed by Stengle in [5]. The hypothesis is that f is smooth in space (y) but no more than bounded and measurable in time (t).…”
Section: Introductionmentioning
confidence: 99%