Journal of Complexity ( IF 1.338 ) Pub Date : 2021-02-22 , DOI: 10.1016/j.jco.2021.101555
Adrian Ebert; Peter Kritzer; Dirk Nuyens; Onyekachi Osisiogu

Lattice rules are among the most prominently studied quasi-Monte Carlo methods to approximate multivariate integrals. A rank-1 lattice rule to approximate an $s$-dimensional integral is fully specified by its generating vector $\mathbit{z}\in {\mathbb{Z}}^{s}$ and its number of points $N$. While there are many results on the existence of “good” rank-1 lattice rules, there are no explicit constructions for good generating vectors for dimensions $s\ge 3$. This is why one usually resorts to computer search algorithms. Motivated by earlier work of Korobov from 1963 and 1982, we present two variants of search algorithms for good lattice rules and show that the resulting rules exhibit a convergence rate in weighted function spaces that can be arbitrarily close to the optimal rate. Moreover, contrary to most other algorithms, we do not need to know the smoothness of our integrands in advance, the generating vector will still recover the convergence rate associated with the smoothness of the particular integrand, and, under appropriate conditions on the weights, the error bounds can be stated without dependence on $s$. The search algorithms presented in this paper are two variants of the well-known component-by-component (CBC) construction, one of which is combined with a digit-by-digit (DBD) construction. We present numerical results for both algorithms using fast construction algorithms in the case of product weights. They confirm our theoretical findings.

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