Compressed Sensing, Extensions and Applications - Part III of III
For Part I, see MS-Mo-D-05
For Part II, see MS-Mo-E-05

Date: August 11
Time: 13:30--15:30
Room: 215

(Note: Click title to show the abstract.)

Kutyniok, Gitta (Technische Universität Berlin)
Holger, Rauhut (RWTH Aachen Univ.)

Abstract: Compressed sensing has seen an enormous research activity in recent years. The key principle is that (approximately) sparse signals can be recovered efficiently from what was previously believed to be vastly incomplete information. For this reason, compressed sensing and its algorithms (often convex optimization approaches) have a large range of applications such as magnetic resonance imaging, radar, wireless communications, and more. Remarkably, all provably optimal measurement schemes are based on randomness and therefore, compressed sensing connects various mathematical fields such as random matrix theory, optimization, approximation theory, and harmonic analysis. Recent developments have extended the theory and its algorithms to the recovery of low rank matrices from incomplete information, to the phaseless estimation problem, and to low tensor recovery. The minisymposium aims at bringing together experts in the field and to provide an overview of its most recent results.

The state of quantum applications of compressed sensing and low-rank methods
Gross, David (Univ. of Freiburg)

Co-Sparse Tomographic Image Recovery: Performance Estimates and Large-Scale Programming
Petra, Stefania (Univ. of Heidelberg)

Quantitative MRI using model-based compressed Sensing
Davies, Mike (Univ. of Edinburgh)


Code: Type-Date-Time-Room No.
Type : IL=Invited Lecture, SL=Special Lectures, MS=Minisymposia, IM=Industrial Minisymposia, CP=Contributed Papers, PP=Posters
Date: Mo=Monday, Tu=Tuesday, We=Wednesday, Th=Thursday, Fr=Friday
Time : A=8:30-9:30, B=10:00-11:00, C=11:10-12:10, BC=10:00-12:10, D=13:30-15:30, E=16:00-18:00, F=19:00-20:00, G=12:10-13:30, H=15:30-16:00
Room No.: TBA