Prof. Dr. Holger Rauhut, RWTH Aachen, Vortrag: Sparse and Low Rank Recovery

Donnerstag, 14.07.2016 16:30 im Raum M5
Mathematik und Informatik

Compressive sensing predicts that sparse vectors can be recovered via efficient algorithms from what was previously believed to be incomplete information. Recovery methods include convex optimization approaches (l1-minimization). Provably optimal measurement process are described via Gaussian random matrices. In practice, however, more structure is required. We describe the state of the art on recovery results for several types of structured random measurement matrices, including random partial Fourier matrices and subsampled random convolutions. An extension of compressive sensing considers the recovery of low rank matrices from incomplete measurements. We describe recovery results for types of measurements motivated by quantum physical experiments.

Angelegt am Dienstag, 05.04.2016 11:22 von shupp_01
Geändert am Montag, 04.07.2016 10:33 von shupp_01
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