148 citations · 790 across the 56 of their papers we have counts for
6 papers · 1 filter
Randomized Nyström Preconditioning
Zachary Frangella, Joel A. Tropp, Madeleine Udell
This paper introduces the Nyström PCG algorithm for solving a symmetric positive-definite linear system. The algorithm applies the randomized Nyström method to form a low-rank appr…
Tensor Random Projection for Low Memory Dimension Reduction
Yiming Sun, Yang Guo, Joel A. Tropp +1
Random projections reduce the dimension of a set of vectors while preserving structural information, such as distances between vectors in the set. This paper proposes a novel use o…
Low-Rank Tucker Approximation of a Tensor From Streaming Data
Yiming Sun, Yang Guo, Charlene Luo +2
This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor. The method applies a randomized linear map to the tensor to obtain a sketch that cap…
Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation
Joel A. Tropp, Alp Yurtsever, Madeleine Udell +1
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collect…
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Joel A. Tropp, Alp Yurtsever, Madeleine Udell +1
Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of line…
Practical sketching algorithms for low-rank matrix approximation
Joel A. Tropp, Alp Yurtsever, Madeleine Udell +1
This paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch. These methods can…