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Andreas M. Tillmann
Researcher at RWTH Aachen University
Publications - 33
Citations - 979
Andreas M. Tillmann is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Underdetermined system & Computational complexity theory. The author has an hindex of 10, co-authored 31 publications receiving 857 citations. Previous affiliations of Andreas M. Tillmann include Braunschweig University of Technology & Technische Universität Darmstadt.
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The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing
TL;DR: It is confirmed by showing that for a given matrix A and positive integer k, computing the best constants for which the RIP or NSP hold is, in general, NP-hard.
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The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing
TL;DR: In this article, it was shown that for a given matrix A and positive integer k, computing the best constants for which the restricted isometry property (RIP) or nullspace property (NSP) hold is NP-hard.
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DOLPHIn—Dictionary Learning for Phase Retrieval
TL;DR: This work proposes a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase, and jointly reconstructs the unknown signal and learns a dictionary such that each patch of the estimated image can be sparsely represented.
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On the Computational Intractability of Exact and Approximate Dictionary Learning
TL;DR: In this paper, it was shown that learning a dictionary with which a given set of training signals can be represented as sparsely as possible is indeed a NP-hard problem, and also established hardness of approximating the solution to within large factors of the optimal sparsity level.
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On the Computational Intractability of Exact and Approximate Dictionary Learning
TL;DR: It is shown that learning a dictionary with which a given set of training signals can be represented as sparsely as possible is indeed NP-hard, and hardness of approximating the solution to within large factors of the optimal sparsity level is established.