Construction of scrambled polynomial lattice rules over $\mathbb{F}_2$ with small mean square weighted $\mathcal{L}_2$ discrepancy
For researchers in quasi-Monte Carlo methods, this provides a constructive approach to generating point sets with near-optimal discrepancy, though the improvement is incremental over existing methods.
The authors construct scrambled polynomial lattice rules over 𝔽₂ with small mean square weighted ℒ₂ discrepancy, proving an upper bound that converges at nearly the optimal rate N^{-2+δ}. Numerical experiments show performance comparable or superior to Sobol' sequences.
The $\mathcal{L}_2$ discrepancy is one of several well-known quantitative measures for the equidistribution properties of point sets in the high-dimensional unit cube. The concept of weights was introduced by Sloan and Woźniakowski to take into account the relative importance of the discrepancy of lower dimensional projections. As known under the name of quasi-Monte Carlo methods, point sets with small weighted $\mathcal{L}_2$ discrepancy are useful in numerical integration. This study investigates the component-by-component construction of polynomial lattice rules over the finite field $\mathbb{F}_2$ whose scrambled point sets have small mean square weighted $\mathcal{L}_2$ discrepancy. An upper bound on this discrepancy is proved, which converges at almost the best possible rate of $N^{-2+δ}$ for all $δ>0$, where $N$ denotes the number of points. Numerical experiments confirm that the performance of our constructed polynomial lattice point sets is comparable or even superior to that of Sobol' sequences.