Probabilistic construction of deterministic algorithms: approximating packing integer programs
Journal of Computer and System Sciences - 27th IEEE Conference on Foundations of Computer Science October 27-29, 1986
Approximations of general independent distributions
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Small-bias probability spaces: efficient constructions and applications
SIAM Journal on Computing
Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
Journal of Combinatorial Theory Series A
Algorithmic Chernoff-Hoeffding inequalities in integer programming
Random Structures & Algorithms
Improved algorithms via approximations of probability distributions
Journal of Computer and System Sciences
Some open problems concerning the star-discrepancy
Journal of Complexity
Covering numbers, vapnik-červonenkis classes and bounds for the star-discrepancy
Journal of Complexity
On the tractability of multivariate integration and approximation by neural networks
Journal of Complexity
Note: A note on the existence of sequences with small star discrepancy
Journal of Complexity
Tractability properties of the weighted star discrepancy
Journal of Complexity
Bracketing numbers for axis-parallel boxes and applications to geometric discrepancy
Journal of Complexity
On probabilistic results for the discrepancy of a hybrid-Monte Carlo sequence
Journal of Complexity
Randomly rounding rationals with cardinality constraints and derandomizations
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
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For numerical integration in higher dimensions, bounds for the star-discrepancy with polynomial dependence on the dimension d are desirable. Furthermore, it is still a great challenge to give construction methods for low-discrepancy point sets.In this paper, we give upper bounds for the star-discrepancy and its inverse for subsets of the d-dimensional unit cube. They improve known results. In particular, we determine the usually only implicitly given constants. The bounds are based on the construction of nearly optimal δ-covers of anchored boxes in the d-dimensional unit cube.We give an explicit construction of low-discrepancy points with a derandomized algorithm. The running time of the algorithm, which is exponentially in d, is discussed in detail and comparisons with other methods are given.