Quasi-Monte Carlo with a Hankel random digital net
For researchers in quasi-Monte Carlo methods, this offers a simpler and more efficient randomized digital net design.
This paper introduces a randomized digital net design using random Hankel matrices, simplifying construction and reducing random variables while maintaining convergence rates. Numerical experiments confirm the theoretical results.
This paper proposes a new randomized design of digital nets in which the generating matrices are chosen to be random Hankel matrices. Compared with previous randomized designs of digital nets, this approach simplifies the construction process and reduces the number of random variables required, while still achieving desirable convergence rates when combined with appropriate estimators. We analyze the properties of the proposed design, derive bounds for Walsh coefficients, and provide error analysis for both the median-of-means estimator and a newly proposed greedy selection estimator, i.e. the selection of the best design from a batch in terms of a worst-case error bound. Numerical experiments validate our theoretical findings and demonstrate the practical performance of the proposed methods.