J
Joel A. Tropp
Researcher at California Institute of Technology
Publications - 182
Citations - 53704
Joel A. Tropp is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Matrix (mathematics) & Convex optimization. The author has an hindex of 67, co-authored 173 publications receiving 49525 citations. Previous affiliations of Joel A. Tropp include Rice University & University of Michigan.
Papers
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Journal ArticleDOI
Recovery of short, complex linear combinations via /spl lscr//sub 1/ minimization
TL;DR: In this article, a condition under which lscr1 minimization (also known as basis pursuit) can recover short linear combinations of complex vectors chosen from fixed, overcomplete collection is provided.
Proceedings ArticleDOI
Column subset selection, matrix factorization, and eigenvalue optimization
TL;DR: A randomized, polynomial-time algorithm that produces the submatrix promised by Bourgain and Tzafriri, and an approximation algorithm for the (∞, 1) norm of a matrix, which is generally NP-hard to compute exactly.
Proceedings Article
Algorithmic linear dimension reduction in the l_1 norm for sparse vectors
TL;DR: In this article, the authors present a reconstruction algorithm whose run time, O(m log(m) log(d)), is sublinear in the length d of the signal and reconstruction error is within a logarithmic factor (in m) of the optimal m-term approximation error in the norm of the m-sparse signal.
Patent
Method and apparatus for on-line compressed sensing
Richard G. Baraniuk,Dror Baron,Marco F. Duarte,Mohamed El-Nozahi,Michael B. Wakin,Mark A. Davenport,Jason N. Laska,Joel A. Tropp,Yehia Massoud,S. Kirolos,T. Ragheb +10 more
TL;DR: In this paper, the authors demonstrate and reduce to practice methods to extract information directly from an analog or digital signal based on altering our notion of sampling to replace uniform time samples with more general linear functionals.
Journal ArticleDOI
Solving ptychography with a convex relaxation
TL;DR: A specific algorithm, based on low-rank factorization, whose runtime and memory usage are near-linear in the size of the output image and which offers a 25% lower background variance on average than alternating projections, the ptychographic reconstruction algorithm that is currently in widespread use.