Stefania Petra (Universität Heidelberg)
Thursday, March 7, 2019 - 11:00
MPI für Mathematik in den Naturwissenschaften Leipzig
Inselstr. 22, 04103 Leipzig, E1 05 (Leibniz-Saal), 1. Etage
Compressed sensing (CS) is a new sampling theory and has become a major research direction in applied mathematics in the last 10 years. The key idea of CS for addressing the big data problem is to avoid sampling data that can be recovered afterwards. However, mathematical recovery guarantees depend on assumptions that are often too strong in practice. The extension of the mathematical theory as well as the development of new applications in various fields are the subject of many current research activities in the field. The talk will highlight some of the challenges of bridging the gap between theory and practicality of CS.
submitted by Antje Vandenberg (, 0341 9959 50)