Cloud computing as a new internet-based computing model provides different resources as
a service dynamically. Today, cloud computing is actually one of the main improvements in …

This paper is a contemporary review of QMC ('quasi-Monte Carlo') methods, that is, equal-
weight rules for the approximate evaluation of high-dimensional integrals over the unit cube …

This is the second volume of a three-volume set comprising a comprehensive study of the
tractability of multivariate problems. The second volume deals with algorithms using …

Diversity plays a crucial role in evolutionary computation. While diversity has been mainly
used to prevent the population of an evolutionary algorithm from premature convergence …

We introduce a class of γ-negatively dependent random samples. We prove that this class
includes, apart from Monte Carlo samples, in particular Latin hypercube samples and Latin …

In this book chapter we survey known approaches and algorithms to compute discrepancy
measures of point sets. After providing an introduction which puts the calculation of …

In this chapter we show the deep connections between discrepancy theory on the one hand
and quasi-Monte Carlo integration on the other. Discrepancy theory was established as an …

Building upon the exact methods presented in our earlier work (2022)[5], we introduce a
heuristic approach for the star discrepancy subset selection problem. The heuristic gradually …

The discrepancy function of a point distribution measures the deviation from the uniform
distribution. Different versions of the discrepancy function capture this deviation with respect …

The L∞ star discrepancy is a measure for the regularity of a finite set of points taken from [0,
1) d. Low discrepancy point sets are highly relevant for Quasi-Monte Carlo methods in …