Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?
Journal of Complexity
The discrepancy and gain coefficients of scrambled digital nets
Journal of Complexity
Explicit evaluations and reciprocity theorems for finite trigonometric sums
Advances in Applied Mathematics
Bounds for the weighted Lp discrepancy and tractability of integration
Journal of Complexity
Constructions of (t ,m,s)-nets and (t,s)-sequences
Finite Fields and Their Applications
Tractability properties of the weighted star discrepancy
Journal of Complexity
Quasi-Monte Carlo Numerical Integration on $\mathbb{R}^s$: Digital Nets and Worst-Case Error
SIAM Journal on Numerical Analysis
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We study the mean square weighted L2 discrepancy of randomized digital (t, m, s)-nets over Zp. The randomization method considered here is a digital shift of depth m, i.e., for each coordinate the first m digits of each point are shifted by the same shift, whereas the remaining digits in each coordinate are shifted independently for each point. We also consider a simplified version of this shift.We give a formula for the mean square weighted L2 discrepancy using the generating matrices of the digital net and we prove an upper bound on this discrepancy. Further we investigate how the constant of the leading term depends on the choice of the base p.