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This study deals with a narrow escape problem, a well-know difficult problem of evaluating the probability for a diffusing particle to reach a small part of a boundary far away from the starting position of the particle. A direct simulation of the diffusion trajectories would take an enormous computer simulation time. Instead, we use a different approach which drastically improves the efficiency of the diffusion trajectory tracking algorithm by introducing an artificial drift velocity directed to the target position. The method can be efficiently applied to solve narrow escape problems for domains of long extension in one direction which is the case in many practical problems in biology and chemistry. The algorithm is meshless both in space and time, and is well applied to solve high-dimensional problems in complicated domains. We present in this paper a detailed numerical analysis of the method for the case of a rectangular parallelepiped. Both stationary and transient diffusion problems are handled. [ABSTRACT FROM AUTHOR]

The ziggurat method is a fast random variable generation method introduced by Marsaglia and Tsang in a series of papers. We discuss how the ziggurat method can be implemented for low-discrepancy sequences, and present algorithms and numerical results when the method is used to generate samples from the normal and gamma distributions. [ABSTRACT FROM AUTHOR]

Matsumoto, Saito and Matoba recently proposed the Walsh figure of merit (WAFOM), which is a computable criterion for quasi-Monte Carlo point sets using digital nets. Several algorithms have been proposed for finding low-WAFOM point sets. In the existing algorithms, the number of points is fixed in advance, but extensible point sets are preferred in some applications. In this paper, we propose a random search algorithm for extensible low-WAFOM point sets. For this, we introduce a method that uses lookup tables to compute WAFOM faster. Numerical results show that our extensible low-WAFOM point sets are comparable with Niederreiter-Xing sequences for some low-dimensional and smooth test functions. [ABSTRACT FROM AUTHOR]

The generation of appropriate parallel and high-quality quasirandom sequences (low-discrepancy sequences) is crucial to the success of quasi-Monte Carlo methods. The Kronecker sequence is well known to be one of the special types of low-discrepancy sequences, and one of its important advantages is that the Kronecker sequence is easy to implement due to its definition via the fractional parts of multiples of irrationals. However, the original Kronecker sequence suffers from correlations for different dimensions. These correlations result in poorly distributed two-dimensional projections. An approach to this is to find a modified Kronecker sequence via generalizing golden ratio and generate parallel sequences. This paper presents a new algorithm for finding a modified Kronecker sequence within special choices of irrationals. This modified sequence is numerically tested and shown empirically to be superior to the other widely used quasirandom sequences. In addition, based on analysis and insight into the correlations between dimensions of the Kronecker sequence, we illustrate why our algorithm is efficient for breaking these correlations. [ABSTRACT FROM AUTHOR]

In this paper, we describe a stochastic algorithm to solve sudoku puzzles. Our method consists in computing probabilities for each symbol of each cell updated at each step of the algorithm using estimation of distributions algorithms (EDA). This update is done using the empirical estimators of these probabilities for a fraction of the best puzzles according to a cost function. We develop also some partial restart techniques in the RESEDA algorithm to obtain a convergence for the most difficult puzzles. Our algorithm is tested numerically on puzzles with various levels of difficulty starting from very easy ones to very hard ones including the famous puzzle AI Escargot. The CPU times vary from few hundreds of a second for the easy ones to about one minute for the most difficult one. [ABSTRACT FROM AUTHOR]

In this paper we propose an improved quasi-Monte Carlo method for solving Linear Algebra problems. We show that by using low-discrepancy sequences both the convergence and the CPU time of the algorithm are improved. Two parallelization schemes using the Message Passing Interface with static and dynamic load balancing are proposed. The dynamic scheme is useful for computing in the GRID environment. [ABSTRACT FROM AUTHOR]