Entropy, Randomization, Derandomization, and Discrepancy

  • Michael Gnewuch
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 23)


The star discrepancy is a measure of how uniformly distributed a finite point set is in the d-dimensional unit cube. It is related to high-dimensional numerical integration of certain function classes as expressed by the Koksma-Hlawka inequality. A sharp version of this inequality states that the worst-case error of approximating the integral of functions from the unit ball of some Sobolev space by an equal-weight cubature is exactly the star discrepancy of the set of sample points. In many applications, as, e.g., in physics, quantum chemistry or finance, it is essential to approximate high-dimensional integrals. Thus with regard to the Koksma-Hlawka inequality the following three questions are very important:
  1. 1.

    What are good bounds with explicitly given dependence on the dimension d for the smallest possible discrepancy of any n-point set for moderate n?

  2. 2.

    How can we construct point sets efficiently that satisfy such bounds?

  3. 3.

    How can we calculate the discrepancy of given point sets efficiently?


We want to discuss these questions and survey and explain some approaches to tackle them relying on metric entropy, randomization, and derandomization.


Infinite Sequence Star Discrepancy Random Experiment Hammersley Point Small Discrepancy Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author would like to thank two anonymous referees for their helpful suggestions.

Part of the work on this book chapter was done while the author was research fellow at the Department of Computer Science of Columbia University in the City of New York.

He gratefully acknowledges support from the German Science Foundation DFG under grant GN91/3-1 and GN91/4-1.


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Authors and Affiliations

  1. 1.Institut für InformatikChristian-Albrechts-Universität zu KielKielGermany

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