Information-based complexity @ FoCM'11
July 4-6, 2011
Budapest, Hungary
Information-based complexity (IBC) is concerned with the complexity and algorithms for continuous problems where information is incomplete, contaminated and priced. Important examples are high dimensional integration, differential and integral equations and numerical optimization. Problems can be studied in the worst case, average case or randomised settings. The focus is on questions such as: What is the least possible cost (e.g., in terms of the number of function evaluations) to obtain approximation with given error? How does the cost change with dimensionality? What algorithms achieve the minimum cost?
Speakers confirmed as of October, 2010:
James M. Calvin, NJIT, Newark Stephan Dahlke, University of Marburg Thomas Daun, University of Kaiserslautern Josef Dick, University of New South Wales, Sydney Michael Gnewuch, University of Kiel Markus Hansen, ETH Zürich Aicke Hinrichs, University of Jena Frances Kuo, University of New South Wales, Sydney Peter Mathé, WIAS, Berlin James Nichols, University of New South Wales, Sydney Erich Novak, University of Jena Dirk Nuyens, University of Leuven Sergei V. Pereverzev, RICAM, Linz Friedrich Pillichshammer, University of Linz Leszek Plaskota, University of Warsaw Paweł Przybyłowicz, AGH, Krakow Daniel Rudolf, University of Jena Winfried Sickel, University of Jena Ian H. Sloan, University of New South Wales, Sydney Jan Vybiral, RICAM, Linz Grzegorz W. Wasilkowski, University of Kentucky, Lexington Henryk Wozniakowski, Columbia University, New York Talk abstracts:
Schedule:
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OrganizersStefan Heinrich,University of Kaiserslautern Klaus Ritter, University of Kaiserslautern contact us 
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