Multivariate Algorithms and their Foundations in Number Theory
Linz, October 15-December 21, 2018
Discrepancy theory deals with point distribution in compact spaces, measuring the discrepancy between empirical distribution and target distribution. Most prominent are the star-discrepancy problem, asking for the optimal rate of convergence of the supremum of the discrepancy function, and the inverse of the star-discrepancy problem, asking for explicit constructions of point sets whose discrepancy depends at most polynomially on the dimension. The first problem is related to quasi-Monte Carlo integration, whereas the second problem seems more closely related to pseudo-random number generators. Point distributions on the unit sphere and point distributions with respect to non-uniform target measures are also of great interest.
Group photo: SpecSem_WS3.jpg
A tutorial giving an introduction to some of the main topics of the workshop will be given by Dmitriy Bilyk:
"Uniform Distribution and Discrepancy Theory: At the crossroads of analysis, approximation, discrete geometry, number theory, probability, and more..."
Slides (PDF, 2.3MB)
Date: Friday, Nov. 23,
Location: RICAM, Room SP2 416-2.