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Error Bounds for Random Matrix Approximation Schemes
Alex Gittens, Joel A. Tropp
Randomized matrix sparsification has proven to be a fruitful technique for producing faster algorithms in applications ranging from graph partitioning to semidefinite programming.…
Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
Nathan Halko, Per-Gunnar Martinsson, Joel A. Tropp
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific comp…
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
Joel A. Tropp, Jason N. Laska, Marco F. Duarte +2
Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficie…
Efficient Sampling of Sparse Wideband Analog Signals
Moshe Mishali, Yonina C. Eldar, Joel A. Tropp
Periodic nonuniform sampling is a known method to sample spectrally sparse signals below the Nyquist rate. This strategy relies on the implicit assumption that the individual sampl…