Information-based complexity (IBC) studies how many pieces of information are required to solve a (numerical) problem up to a prescribed error tolerance. The problems considered include function approximation and learning, numerical integration, optimization, or the solution of PDEs and SDEs. It is of particular interest how the complexity increases with the dimensionality of the problem (cf. curse of dimensionality versus tractability) and with the desired accuracy (cf. rate of convergence). The IBC workshop interacts naturally with several other workshops, including “Foundations of Data science and Machine Learning”, “Approximation Theory”, or “Stochastic Computation” as we study similar topics but from other perspectives. The two semi-plenary talks that we plan to include shall introduce the most important aspects of the field also to researchers from other communities.
Organizers
Speakers
Semi-plenary speakers
University of Passau
Chemnitz University of Technology
Invited speakers
University of Minnesota
Centre de Recerca Matemàtica
University of Warsaw
University of New South Wales
Johann Radon Institute for Computational and Applied Mathematics
AGH University of Krakow
Brown University
University of New South Wales
University of New South Wales
Osnabrück University
University of Alberta
University of Münster
The University of Tokyo
Friedrich Schiller University Jena
Johannes Kepler University Linz
RPTU University Kaiserslautern-Landau
Johannes Kepler University Linz
University of Graz