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Faster Linear Algebra Algorithms with Structured Random Matrices
Chris Camaño, Ethan N. Epperly, Raphael A. Meyer +1
To achieve the greatest possible speed, practitioners regularly implement randomized algorithms for low-rank approximation and least-squares regression with structured dimension re…
Binary Component Decomposition Part I: The Positive-Semidefinite Case
Richard Kueng, Joel A. Tropp
This paper studies the problem of decomposing a low-rank positive-semidefinite matrix into symmetric factors with binary entries, either or . This research ans…
Binary component decomposition Part II: The asymmetric case
Richard Kueng, Joel A. Tropp
This paper studies the problem of decomposing a low-rank matrix into a factor with binary entries, either from or from , and an unconstrained factor. The resea…
Algorithmic linear dimension reduction in the l_1 norm for sparse vectors
A. C. Gilbert, M. J. Strauss, J. A. Tropp +1
This paper develops a new method for recovering m-sparse signals that is simultaneously uniform and quick. We present a reconstruction algorithm whose run time, O(m log^2(m) log^2(…