Joel A. Tropp | DeepAI

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∙ 06/21/2023

Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

This survey explores modern approaches for computing low-rank approximat...

0 Joel A. Tropp, et al. ∙

∙ 04/24/2023

Robust, randomized preconditioning for kernel ridge regression

This paper introduces two randomized preconditioning techniques for robu...

0 Mateo Díaz, et al. ∙

∙ 01/19/2023

XTrace: Making the most of every sample in stochastic trace estimation

The implicit trace estimation problem asks for an approximation of the t...

0 Ethan N. Epperly, et al. ∙

∙ 07/13/2022

Jackknife Variability Estimation For Randomized Matrix Computations

Randomized algorithms based on sketching have become a workhorse tool in...

2 Ethan N. Epperly, et al. ∙

∙ 10/29/2021

Fast Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems

This paper develops a new class of algorithms for general linear systems...

0 Yuji Nakatsukasa, et al. ∙

∙ 10/06/2021

Randomized Nyström Preconditioning

This paper introduces the Nyström PCG algorithm for solving a symmetric ...

0 Zachary Frangella, et al. ∙

∙ 10/01/2021

Randomized block Krylov methods for approximating extreme eigenvalues

Randomized block Krylov subspace methods form a powerful class of algori...

0 Joel A. Tropp, et al. ∙

∙ 09/20/2021

Learning to Forecast Dynamical Systems from Streaming Data

Kernel analog forecasting (KAF) is a powerful methodology for data-drive...

12 Dimitris Giannakis, et al. ∙

∙ 04/30/2021

Tensor Random Projection for Low Memory Dimension Reduction

Random projections reduce the dimension of a set of vectors while preser...

6 Yiming Sun, et al. ∙

∙ 07/31/2019

Binary Component Decomposition Part I: The Positive-Semidefinite Case

This paper studies the problem of decomposing a low-rank positive-semide...

0 Richard Kueng, et al. ∙

∙ 07/31/2019

Binary component decomposition Part II: The asymmetric case

This paper studies the problem of decomposing a low-rank matrix into a f...

0 Richard Kueng, et al. ∙

∙ 02/22/2019

Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation

This paper argues that randomized linear sketching is a natural tool for...

0 Joel A. Tropp, et al. ∙

∙ 02/09/2019

An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity

This paper develops a new storage-optimal algorithm that provably solves...

0 Lijun Ding, et al. ∙

∙ 10/29/2018

Concentration of the Intrinsic Volumes of a Convex Body

The intrinsic volumes are measures of the content of a convex body. This...

0 Martin Lotz, et al. ∙

∙ 09/28/2018

Fast state tomography with optimal error bounds

Projected least squares (PLS) is an intuitive and numerically cheap tech...

0 Madalin Guta, et al. ∙

∙ 06/18/2017

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

Several important applications, such as streaming PCA and semidefinite p...

0 Joel A. Tropp, et al. ∙

∙ 02/22/2017

Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage

This paper concerns a fundamental class of convex matrix optimization pr...

0 Alp Yurtsever, et al. ∙

∙ 08/31/2016

Randomized single-view algorithms for low-rank matrix approximation

This paper develops a suite of algorithms for constructing low-rank appr...

0 Joel A. Tropp, et al. ∙

∙ 11/30/2015

Universality laws for randomized dimension reduction, with applications

Dimension reduction is the process of embedding high-dimensional data in...

0 Samet Oymak, et al. ∙

∙ 01/07/2015

An Introduction to Matrix Concentration Inequalities

In recent years, random matrices have come to play a major role in compu...

0 Joel A. Tropp, et al. ∙

∙ 02/18/2012

Robust computation of linear models by convex relaxation

Consider a dataset of vector-valued observations that consists of noisy ...

0 Gilad Lerman, et al. ∙

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