Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Quasi-random sequences and their discrepancies
SIAM Journal on Scientific Computing
Algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
A generalized discrepancy and quadrature error bound
Mathematics of Computation
When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?
Journal of Complexity
Complexity and information
Weighted tensor product algorithms for linear multivariate problems
Journal of Complexity
Strong tractability of multivariate integration using quasi-Monte Carlo algorithms
Mathematics of Computation
The error bounds and tractability of quasi-Monte Carlo algorithms in infinite dimension
Mathematics of Computation
Low-Discrepancy Sequences and Global Function Fields with Many Rational Places
Finite Fields and Their Applications
The effective dimension and quasi-Monte Carlo integration
Journal of Complexity
Tractability properties of the weighted star discrepancy
Journal of Complexity
Hi-index | 0.00 |
We prove in a constructive way that multivariate integration in appropriate weighted Sobolev classes is strongly tractable and the ε-exponent of strong tractability is 1 (which is the best-possible value) under a stronger assumption than Sloan and Wozniakowski's assumption. We show that quasi-Monte Carlo algorithms based on the Sobol sequence and Halton sequence achieve the convergence order O(n-1+δ) for any δ 0 independent of the dimension with a worst-case deterministic guarantee (where n is the number of function evaluations). This implies that quasi-Monte Carlo algorithms based on the Sobol and Halton sequences converge faster and therefore are superior to Monte Carlo methods independent of the dimension for integrands in suitable weighted Sobolev classes.