Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Improved error bounds for lattice rules
Journal of Complexity - Festschrift for Joseph F. Traub, Part 1
Extensible Lattice Sequences for Quasi-Monte Carlo Quadrature
SIAM Journal on Scientific Computing
The error bounds and tractability of quasi-Monte Carlo algorithms in infinite dimension
Mathematics of Computation
Component-by-component construction of good lattice rules
Mathematics of Computation
Journal of Complexity
Searching for extensible Korobov rules
Journal of Complexity
Note: A note on the existence of sequences with small star discrepancy
Journal of Complexity
Shifted lattice rules based on a general weighted discrepancy for integrals over Euclidean space
Journal of Computational and Applied Mathematics
On the approximation of smooth functions using generalized digital nets
Journal of Complexity
Lattice point sets for deterministic learning and approximate optimization problems
IEEE Transactions on Neural Networks
Weighted compound integration rules with higher order convergence for all N
Numerical Algorithms
Constructions of general polynomial lattice rules based on the weighted star discrepancy
Finite Fields and Their Applications
Constructing adapted lattice rules using problem-dependent criteria
Proceedings of the Winter Simulation Conference
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Extensible integration lattices have the attractive property that the number of points in the node set may be increased while retaining the existing points. It is shown here that there exist generating vectors, h, for extensible rank-1 lattices such that for n = b, b2, ... points and dimensions s = 1, 2, ... the figures of merit Rα, Pα and discrepancy are all small. The upper bounds obtained on these figures of merit for extensible lattices are some power of log n worse than the best upper bounds for lattices where h is allowed to vary with n and s.