AbstractSequences of points with a low discrepancy are the basic building blocks for quasi-Monte Carlo methods. Traditionally these points are generated in a unit cube.To develop point sets on a simplex we will transform the low-discrepancy points from the unit cube to a simplex. An advantage of this approach is that most of the known results on low-discrepancy sequences can be re-used. After introducing several transformations, their efficiency as well as their quality will be evaluated. We present a Koksma–Hlawka inequality which says that under certain conditions the order of convergence using the new point set is the same as that of the original set
Abstract. Let P ⊂ [0, 1)S be a finite point set of cardinality N in an S-dimensional cube, and let f...
Low-discrepancy point sets and sequences play an important role in quasi-Monte Carlo method for n...
AbstractThe concepts of (t,m,s)-nets and (t,s)-sequences are among the best known classes of point s...
AbstractSequences of points with a low discrepancy are the basic building blocks for quasi-Monte Car...
summary:Many low-discrepancy sets are suitable for quasi-Monte Carlo integration. Skriganov showed t...
AbstractWe generalize and improve earlier constructions of low-discrepancy sequences by Sobol', Faur...
The computation of integrals in higher dimensions and on general domains, when no explicit cubature...
AbstractQuasi-Monte Carlo (QMC) methods have been successfully used to compute high-dimensional inte...
summary:Many low-discrepancy sets are suitable for quasi-Monte Carlo integration. Skriganov showed t...
AbstractFor numerical integration in higher dimensions, bounds for the star-discrepancy with polynom...
This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) m...
Monte Carlo (MQ) method is a powerful tool to approximate high dimensional integrals. The disadvanta...
This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists a...
We introduce two novel techniques for speeding up the generation of digital \((t,s)\)-sequences. Bas...
This talk gives an introduction to quasi-Monte Carlo methods for high-dimensional integrals. Such me...
Abstract. Let P ⊂ [0, 1)S be a finite point set of cardinality N in an S-dimensional cube, and let f...
Low-discrepancy point sets and sequences play an important role in quasi-Monte Carlo method for n...
AbstractThe concepts of (t,m,s)-nets and (t,s)-sequences are among the best known classes of point s...
AbstractSequences of points with a low discrepancy are the basic building blocks for quasi-Monte Car...
summary:Many low-discrepancy sets are suitable for quasi-Monte Carlo integration. Skriganov showed t...
AbstractWe generalize and improve earlier constructions of low-discrepancy sequences by Sobol', Faur...
The computation of integrals in higher dimensions and on general domains, when no explicit cubature...
AbstractQuasi-Monte Carlo (QMC) methods have been successfully used to compute high-dimensional inte...
summary:Many low-discrepancy sets are suitable for quasi-Monte Carlo integration. Skriganov showed t...
AbstractFor numerical integration in higher dimensions, bounds for the star-discrepancy with polynom...
This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) m...
Monte Carlo (MQ) method is a powerful tool to approximate high dimensional integrals. The disadvanta...
This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists a...
We introduce two novel techniques for speeding up the generation of digital \((t,s)\)-sequences. Bas...
This talk gives an introduction to quasi-Monte Carlo methods for high-dimensional integrals. Such me...
Abstract. Let P ⊂ [0, 1)S be a finite point set of cardinality N in an S-dimensional cube, and let f...
Low-discrepancy point sets and sequences play an important role in quasi-Monte Carlo method for n...
AbstractThe concepts of (t,m,s)-nets and (t,s)-sequences are among the best known classes of point s...