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.
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Fast & Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems.
Yuji Nakatsukasa,Joel A. Tropp +1 more
TL;DR: In this paper, the authors apply fast randomized sketching to accelerate subspace projection methods, such as GMRES and Rayleigh--Ritz, for general linear systems and eigenvalue problems.
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Learning to Forecast Dynamical Systems from Streaming Data.
TL;DR: In this article, the authors proposed a streaming algorithm for KAF that only requires a single pass over the training data, which dramatically reduces the costs of training and prediction without sacrificing forecasting skill.
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Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications
Joel A. Tropp,Robert J. Webber +1 more
TL;DR: In this paper , a survey explores modern approaches for computing low-rank approximations of high-dimensional matrices by means of the randomized SVD, randomized subspace iteration, and randomized block Krylov iteration.
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Simplicial faces of the set of correlation matrices
TL;DR: It is stated that almost every set of r vertices generates a simplicial face, provided that r≤cn is an absolute constant, and this bound is qualitatively sharp.
Posted ContentDOI
Sharp phase transitions in Euclidean integral geometry
Martin Lotz,Joel A. Tropp +1 more
TL;DR: In this article , the authors derive finer concentration inequalities for the intrinsic volumes of a convex body and derive new phase transitions in formulas for random projections, rotation means, random slicing, and the kinematic formula.