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
Matrix concentration inequalities via the method of exchangeable pairs
TL;DR: In this article, a matrix extension of the scalar concentration theory developed by Sourav Chatterjee using Stein's method of exchangeable pairs is presented. But it is not a generalization of the classical inequalities due to Hoeffding, Bernstein, Khintchine and Rosenthal.
Proceedings Article
Practical Large-Scale Optimization for Max-norm Regularization
TL;DR: This work uses a factorization technique of Burer and Monteiro to devise scalable first-order algorithms for convex programs involving the max-norm and these algorithms are applied to solve huge collaborative filtering, graph cut, and clustering problems.
Journal ArticleDOI
Randomized numerical linear algebra: Foundations and algorithms
TL;DR: This survey describes probabilistic algorithms for linear algebraic computations, such as factorizing matrices and solving linear systems, that have a proven track record for real-world problems and treats both the theoretical foundations of the subject and practical computational issues.
Book ChapterDOI
Convex recovery of a structured signal from independent random linear measurements
TL;DR: This chapter develops a theoretical analysis of the convex programming method for recovering a structured signal from independent random linear measurements that delivers bounds for the sampling complexity that are similar to recent results for standard Gaussian measurements.
Proceedings ArticleDOI
Sparse Approximation Via Iterative Thresholding
TL;DR: A sufficient condition for which general IT exactly recovers a sparse signal is presented, in which the cumulative coherence function naturally arises, and previous results concerning the orthogonal matching pursuit and basis pursuit algorithms to IT algorithms are extended.