Prof. Dr. Holger Rauhut, RWTH Aachen, Vortrag: Sparse and Low Rank Recovery
Thursday, 14.07.2016 16:30 im Raum M5
Compressive sensing predicts that sparse vectors can be recovered via
efficient algorithms from what was previously believed to be incomplete
Recovery methods include convex optimization approaches (l1-minimization).
Provably optimal measurement process are described via Gaussian random
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.