Hot new directions for quasi-Monte Carlo research in step with applications
This is a survey paper that organizes existing QMC theory and applications for researchers, but does not present new results or breakthroughs.
The article reviews quasi-Monte Carlo (QMC) methods with dimension-independent error bounds and efficient parameter generation, and demonstrates their application in three domains with cost-saving strategies. No concrete numerical results are provided.
This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) methods and applications. We summarize three QMC theoretical settings: first order QMC methods in the unit cube $[0,1]^s$ and in $\mathbb{R}^s$, and higher order QMC methods in the unit cube. One important feature is that their error bounds can be independent of the dimension $s$ under appropriate conditions on the function spaces. Another important feature is that good parameters for these QMC methods can be obtained by fast efficient algorithms even when $s$ is large. We outline three different applications and explain how they can tap into the different QMC theory. We also discuss three cost saving strategies that can be combined with QMC in these applications. Many of these recent QMC theory and methods are developed not in isolation, but in close connection with applications.