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- Holger Rauhut

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
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disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
31
Citations
4,992
84
World Ranking
1939
National Ranking
126

- Mathematical analysis
- Algebra
- Geometry

His primary areas of investigation include Algorithm, Combinatorics, Matching pursuit, Compressed sensing and Mathematical optimization. His study in Algorithm is interdisciplinary in nature, drawing from both Norm, Communication channel and Inverse problem. His Combinatorics research is multidisciplinary, relying on both Discrete mathematics, Random matrix, Type, Euclidean vector and Isometry.

His study looks at the relationship between Matching pursuit and fields such as Sparse approximation, as well as how they intersect with chemical problems. Compressed sensing is the subject of his research, which falls under Artificial intelligence. His work in the fields of Greedy algorithm overlaps with other areas such as Eight-point algorithm.

- A Mathematical Introduction to Compressive Sensing (1295 citations)
- Compressed Sensing and Redundant Dictionaries (500 citations)
- Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation (288 citations)

Holger Rauhut mainly investigates Compressed sensing, Algorithm, Matrix, Gaussian and Restricted isometry property. His biological study deals with issues like Sparse matrix, which deal with fields such as Pattern recognition and Signal processing. His Algorithm study integrates concerns from other disciplines, such as Norm, Coherence and Mathematical optimization.

Holger Rauhut has researched Matrix in several fields, including Quadratic equation and Random matrix. Legendre polynomials and Order is closely connected to Combinatorics in his research, which is encompassed under the umbrella topic of Restricted isometry property. His work investigates the relationship between Basis pursuit and topics such as Trigonometry that intersect with problems in Matching pursuit.

- Compressed sensing (35.95%)
- Algorithm (28.76%)
- Matrix (21.57%)

- Applied mathematics (16.34%)
- Compressed sensing (35.95%)
- Structure (3.92%)

Holger Rauhut spends much of his time researching Applied mathematics, Compressed sensing, Structure, Algorithm and Generalization. His Applied mathematics research incorporates elements of Random matrix and Bounded function, Uniform boundedness. His Compressed sensing research focuses on Discrete mathematics and how it connects with Product.

Holger Rauhut combines subjects such as Network model, Projection and Feed forward with his study of Structure. His studies deal with areas such as Dimension, Unitary matrix, Gabor transform and Gabor frame as well as Algorithm. The various areas that he examines in his Generalization study include Smoothness, Iterative reconstruction and Bounding overwatch.

- Jointly low-rank and bisparse recovery: Questions and partial answers (7 citations)
- Weighted Optimization: better generalization by smoother interpolation. (6 citations)
- Overparameterization and generalization error: weighted trigonometric interpolation (4 citations)

- Mathematical analysis
- Algebra
- Geometry

Holger Rauhut mainly focuses on Applied mathematics, Compressed sensing, Discrete mathematics, Generalization error and Least squares. The Applied mathematics study combines topics in areas such as Deep learning and Artificial intelligence. His Artificial intelligence research includes elements of Projection, Factorization and Matrix.

His research in Compressed sensing intersects with topics in Circulant matrix and Product. His Discrete mathematics research integrates issues from Structure, Type and Restricted isometry property. His Least squares research is multidisciplinary, incorporating perspectives in Smoothness, Smoothness and Generalization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

A Mathematical Introduction to Compressive Sensing

Simon Foucart;Holger Rauhut.

**(2013)**

1347 Citations

Compressed Sensing and Redundant Dictionaries

H. Rauhut;K. Schnass;P. Vandergheynst.

IEEE Transactions on Information Theory **(2008)**

658 Citations

Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation

Y.C. Eldar;H. Rauhut.

IEEE Transactions on Information Theory **(2010)**

329 Citations

Compressive Estimation of Doubly Selective Channels in Multicarrier Systems: Leakage Effects and Sparsity-Enhancing Processing

G. Taubock;F. Hlawatsch;D. Eiwen;H. Rauhut.

IEEE Journal of Selected Topics in Signal Processing **(2010)**

311 Citations

Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints

Massimo Fornasier;Holger Rauhut.

SIAM Journal on Numerical Analysis **(2008)**

256 Citations

Iterative thresholding algorithms

Massimo Fornasier;Holger Rauhut.

Applied and Computational Harmonic Analysis **(2008)**

230 Citations

Restricted isometries for partial random circulant matrices

Holger Rauhut;Justin K. Romberg;Joel A. Tropp.

Applied and Computational Harmonic Analysis **(2012)**

208 Citations

Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization

Massimo Fornasier;Holger Rauhut;Rachel Ward.

Siam Journal on Optimization **(2011)**

207 Citations

Atoms of All Channels, Unite! Average Case Analysis of Multi-Channel Sparse Recovery Using Greedy Algorithms

Rémi Gribonval;Holger Rauhut;Karin Schnass;Pierre Vandergheynst.

Journal of Fourier Analysis and Applications **(2008)**

206 Citations

Suprema of Chaos Processes and the Restricted Isometry Property

Felix Krahmer;Shahar Mendelson;Holger Rauhut.

Communications on Pure and Applied Mathematics **(2014)**

200 Citations

Technical University of Munich

Ludwig-Maximilians-Universität München

École Polytechnique Fédérale de Lausanne

University of California, Davis

Australian National University

ETH Zurich

California Institute of Technology

Technical University of Berlin

TU Wien

École Normale Supérieure de Lyon

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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