Welcome to the Joint GAMM ANLA-MSIP Workshop on Matrix Computations for Sparse
Recovery 2014, which will be held on April 9-11, 2014 at TU Berlin supported by the Matheon
The 21th century is rightfully coined the century of data, and in fact we can already today witness a rapidly growing importance of research in data analysis within applied mathematics. A particular focus is on high-dimensional data, which appear in various application areas -- let us mention exemplarily biology, material sciences, and imaging in general. Sparsity methodologies, in particular sparse recovery, have recently revolutionized data analysis in various aspects and in fact led to a change of paradigm. Although various theoretical aspects of sparse recovery have already been studied, from a numerical linear algebra standpoint research is just at its beginning stage. Fast (and adapted) algorithms are in great demand due to the high complexity of the data.
Therefore the goal of this workshop is to bring together the members of the GAMM Activity Groups on Applied and Numerical Linear Algebra and Mathematical Signal- and Image Processing , but even more general welcome all mathematicians working in those areas. The topical focus of this workshop is on intersections between those two research areas, in particular on sparse recovery methodologies in data analysis/processing. We aim to discuss recent developments, promote dialogues, and foster new developments and collaborations.
David Gross (U Freiburg)
Reinhold Schneider (TU Berlin)
Thomas Strohmer (UC Davis, USA)
Joel Tropp (CalTech, USA)