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Explicit constructions of point sets and sequences with low discrepancy
In this article we survey recent results on the explicit construction of
finite point sets and infinite sequences with optimal order of
discrepancy. In 1954 Roth proved a lower bound for the
discrepancy of finite point sets in the unit cube of arbitrary dimension. Later
various authors extended Roth's result to lower bounds also for the
discrepancy and for infinite sequences. While it was known
already from the early 1980s on that Roth's lower bound is best possible in the
order of magnitude, it was a longstanding open question to find explicit
constructions of point sets and sequences with optimal order of
discrepancy. This problem was solved by Chen and Skriganov in 2002 for finite
point sets and recently by the authors of this article for infinite sequences.
These constructions can also be extended to give optimal order of the
discrepancy of finite point sets for . The
main aim of this article is to give an overview of these constructions and
related results
Recent advances in higher order quasi-Monte Carlo methods
In this article we review some of recent results on higher order quasi-Monte
Carlo (HoQMC) methods. After a seminal work by Dick (2007, 2008) who originally
introduced the concept of HoQMC, there have been significant theoretical
progresses on HoQMC in terms of discrepancy as well as multivariate numerical
integration. Moreover, several successful and promising applications of HoQMC
to partial differential equations with random coefficients and Bayesian
estimation/inversion problems have been reported recently. In this article we
start with standard quasi-Monte Carlo methods based on digital nets and
sequences in the sense of Niederreiter, and then move onto their higher order
version due to Dick. The Walsh analysis of smooth functions plays a crucial
role in developing the theory of HoQMC, and the aim of this article is to
provide a unified picture on how the Walsh analysis enables recent developments
of HoQMC both for discrepancy and numerical integration
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