Summary. Many experiments in computer graphics imply that the average quality of quasi-Monte Carlo integro-approximation is improved as the minimal distance of the point set grows. While the definition of (t,m, s)-nets in base b guarantees extensive stratification properties, which are best for t = 0, sampling points can still lie arbitrarily close together. We remove this degree of freedom, report results of two computer searches for (0,m, 2)-nets in base 2 with maximized minimum distance, and present an inferred construction for general m. The findings are especially useful in computer graphics and, unexpectedly, some (0,m, 2)-nets with the best minimum distance properties cannot be generated in the classical way using generator matrices....
(t, m, s)-nets are point sets in Euclidean s-space satisfying certain uniformity conditions, for use...
A good experimental design in a non-parametric framework, such as Gaussian process modelling in comp...
(t, m, s)-nets are a powerful tool for the generation of low-discrepancy point sets. We find nets wi...
Abstract The quality parameter t of (t,m, s)-nets controls extensive stra-tification properties of t...
International audienceThe class of $(t,m,s)$-nets and $(t,s)$-sequences, introduced in their most ge...
International audienceThe class of $(t,m,s)$-nets and $(t,s)$-sequences, introduced in their most ge...
(t,m,s)-Nets were defined by Niederreiter [Monatshefte fur Mathematik, Vol. 104 (1987) pp. 273-337],...
(t,m,s)-Nets were defined by Niederreiter [Monatshefte fur Mathematik, Vol. 104 (1987) pp. 273-337],...
(t,m,s)-Nets were defined by Niederreiter [Monatshefte fur Mathematik, Vol. 104 (1987) pp. 273-337],...
Acesso restrito: Texto completo. p. 5265-5269A previously introduced concept of higher order neighbo...
AbstractThe concepts of (t,m,s)-nets and (t,s)-sequences are among the best known classes of point s...
(t,m, s)−nets were defined by Niederreiter [6], based on earlier work by Sobol ’ [7], in the context...
AbstractIn an article of A. B. Owen (1998, J. Complexity14, 466–489) the question about the distribu...
In solving location models, the effort expended and the quality of the solutions obtained often vari...
Quasi-Monte Carlo rules are equal weight integration formulas used to approximate integrals over the...
(t, m, s)-nets are point sets in Euclidean s-space satisfying certain uniformity conditions, for use...
A good experimental design in a non-parametric framework, such as Gaussian process modelling in comp...
(t, m, s)-nets are a powerful tool for the generation of low-discrepancy point sets. We find nets wi...
Abstract The quality parameter t of (t,m, s)-nets controls extensive stra-tification properties of t...
International audienceThe class of $(t,m,s)$-nets and $(t,s)$-sequences, introduced in their most ge...
International audienceThe class of $(t,m,s)$-nets and $(t,s)$-sequences, introduced in their most ge...
(t,m,s)-Nets were defined by Niederreiter [Monatshefte fur Mathematik, Vol. 104 (1987) pp. 273-337],...
(t,m,s)-Nets were defined by Niederreiter [Monatshefte fur Mathematik, Vol. 104 (1987) pp. 273-337],...
(t,m,s)-Nets were defined by Niederreiter [Monatshefte fur Mathematik, Vol. 104 (1987) pp. 273-337],...
Acesso restrito: Texto completo. p. 5265-5269A previously introduced concept of higher order neighbo...
AbstractThe concepts of (t,m,s)-nets and (t,s)-sequences are among the best known classes of point s...
(t,m, s)−nets were defined by Niederreiter [6], based on earlier work by Sobol ’ [7], in the context...
AbstractIn an article of A. B. Owen (1998, J. Complexity14, 466–489) the question about the distribu...
In solving location models, the effort expended and the quality of the solutions obtained often vari...
Quasi-Monte Carlo rules are equal weight integration formulas used to approximate integrals over the...
(t, m, s)-nets are point sets in Euclidean s-space satisfying certain uniformity conditions, for use...
A good experimental design in a non-parametric framework, such as Gaussian process modelling in comp...
(t, m, s)-nets are a powerful tool for the generation of low-discrepancy point sets. We find nets wi...