Key Info

Basic Information

Portrait: Prof. Dr. Holger Rauhut © Copyright: Frank Luerweg
Prof. Dr. Holger Rauhut
Faculty / Institution:
Mathematics, Computer Science and Natural Sciences
Organizational Unit:
Chair for Mathematics of Information Processing
Project duration:
01.01.2011 to 31.12.2015
EU contribution:
1.010.220 euros
  EU flag and ERC logo This project has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme for research, technological development and demonstration (Grant agreement No. 258926)  


Sparse and Low Rank Recovery


Compressive sensing is a novel field in signal processing at the interface of applied mathematics, electrical engineering and computer science, which caught significant interest over the past five years. It provides a fundamentally new approach to signal acquisition and processing that has large potential for many applications. Compressive sensing (sparse recovery) predicts the surprising phenomenon that many sparse signals (i.e. many real-world signals) can be recovered from what was previously believed to be highly incomplete measurements (information) using computationally efficient algorithms. In the past year exciting new developments emerged on the heels of compressive sensing: low rank matrix recovery (matrix completion); as well as a novel approach for the recovery of high-dimensional functions.

We plan to pursue the following research directions:

- Compressive Sensing (sparse recovery): We aim at a rigorous analysis of certain measurement matrices.
- Low rank matrix recovery: First results predict that low rank matrices can be recovered from incomplete linear information using convex optimization.
- Low rank tensor recovery: We plan to extend methods and mathematical results from low rank matrix recovery to tensors. This field is presently completely open.
- Recovery of high-dimensional functions: In order to reduce the huge computational burden usually observed in the computational treatment of high-dimensional functions, a recent novel approach assumes that the function of interest actually depends only on a small number of variables. Preliminary results suggest that compressive sensing
and low rank matrix recovery tools can be applied to the efficient recovery of such functions.

We plan to develop computational methods for all these topics and to derive rigorous mathematical results on their performance. With the experience I gained over the past years, I strongly believe that I have the necessary competence to pursue this project.

Additional information

Prof. Rauhut transferred his grant to RWTH Aachen University from his former Host Institution, the University of Bonn.