Lattice rules are equal-weight quadrature/cubature rules for the approximation of multivariate integrals which use lattice points as the cubature nodes. The quality of such cubature rules is directly related to the discrepancy between the uniform distribution and the discrete distribution of these points in the unit cube, and so, they are a kind of low-discrepancy sampling points. As low-discrepancy based cubature rules look like Monte Carlo rules, except that they use cleverly chosen deterministic points, they are sometimes called quasi-Monte Carlo rules.
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The talk starts by motivating the usage of Monte Carlo and then quasi-Monte Carlo methods after which some more recent developments are discussed. Topics include: worst-case errors in reproducing kernel Hilbert spaces, weighted spaces and the construction of lattice rules and sequences.
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In the minds of many, quasi-Monte Carlo methods seem to share the bad stanza of the Monte Carlo method: a brute force method of last resort with slow order of convergence, i.e., $O(N^{-1/2})$. This is not so.
While the standard rate of convergence for quasi-Monte Carlo is rather slow, being $O(N^{-1})$, the theory shows that these methods achieve the optimal rate of convergence in many interesting function spaces.
E.g., in function spaces with higher smoothness one can have $O(N^{-\alpha})$, $\alpha > 1$. This will be illustrated by numerical examples.