Integer and combinatorial optimization
Integer and combinatorial optimization
Uncertainty principles and signal recovery
SIAM Journal on Applied Mathematics
Deterministic restrictions in circuit complexity
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
One sketch for all: fast algorithms for compressed sensing
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Deterministic constructions of compressed sensing matrices
Journal of Complexity
Explicit Non-adaptive Combinatorial Group Testing Schemes
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I
Near-Optimal Sparse Recovery in the L1 Norm
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit
Foundations of Computational Mathematics
Unbalanced expanders and randomness extractors from Parvaresh--Vardy codes
Journal of the ACM (JACM)
Combinatorial Sublinear-Time Fourier Algorithms
Foundations of Computational Mathematics
CoSaMP: iterative signal recovery from incomplete and inaccurate samples
Communications of the ACM
Combinatorial algorithms for compressed sensing
SIROCCO'06 Proceedings of the 13th international conference on Structural Information and Communication Complexity
Lower bounds on the maximum cross correlation of signals (Corresp.)
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
A Generic Construction of Complex Codebooks Meeting the Welch Bound
IEEE Transactions on Information Theory
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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We present a general class of compressed sensing matrices which are then demonstrated to have associated sublinear-time sparse approximation algorithms. We then develop methods for constructing specialized matrices from this class which are sparse when multiplied with a discrete Fourier transform matrix. Ultimately, these considerations improve previous sampling requirements for deterministic sparse Fourier transform methods.