
Binary Component Decomposition Part I: The PositiveSemidefinite Case
This paper studies the problem of decomposing a lowrank positivesemide...
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Binary component decomposition Part II: The asymmetric case
This paper studies the problem of decomposing a lowrank matrix into a f...
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Streaming LowRank Matrix Approximation with an Application to Scientific Simulation
This paper argues that randomized linear sketching is a natural tool for...
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An OptimalStorage Approach to Semidefinite Programming using Approximate Complementarity
This paper develops a new storageoptimal algorithm that provably solves...
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Concentration of the Intrinsic Volumes of a Convex Body
The intrinsic volumes are measures of the content of a convex body. This...
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Fast state tomography with optimal error bounds
Projected least squares (PLS) is an intuitive and numerically cheap tech...
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FixedRank Approximation of a PositiveSemidefinite Matrix from Streaming Data
Several important applications, such as streaming PCA and semidefinite p...
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Sketchy Decisions: Convex LowRank Matrix Optimization with Optimal Storage
This paper concerns a fundamental class of convex matrix optimization pr...
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Randomized singleview algorithms for lowrank matrix approximation
This paper develops a suite of algorithms for constructing lowrank appr...
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Universality laws for randomized dimension reduction, with applications
Dimension reduction is the process of embedding highdimensional data in...
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An Introduction to Matrix Concentration Inequalities
In recent years, random matrices have come to play a major role in compu...
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Robust computation of linear models by convex relaxation
Consider a dataset of vectorvalued observations that consists of noisy ...
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Joel A. Tropp
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Professor of Applied & Computational Mathematics at California Institute of Technology