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Low Rank Updates for the Cholesky Decomposition

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Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Stefano Massei

Evaluating the action of a matrix function on a vector, that is

x=f(\mathcal M)v

, is an ubiquitous task in applications. When

\mathcal M

is large, one usually relies on Krylov projection methods. In this paper, we provide effective choices for the pole ...

2019

Low Rank Updates for the Cholesky Decomposition

Graph Chatbot

Chat with Graph Search

Optimal denoising of rotationally invariant rectangular matrices

Fast deterministic and randomized algorithms for low-rank approximation, matrix functions, and trace estimation

Efficient Learning of a Linear Dynamical System with Stability Guarantees

Robust Differentiable SVD

Bootstrapping massive quantum field theories

Recursive blocked algorithms for linear systems with Kronecker product structure

Kinetic Euclidean Distance Matrices

Sub-Matrix Factorization for Real-Time Vote Prediction

Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Decompositions of dependence for high-dimensional extremes

Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Efficient Learning of a Linear Dynamical System with Stability Guarantees

Fast deterministic and randomized algorithms for low-rank approximation, matrix functions, and trace estimation

Recursive blocked algorithms for linear systems with Kronecker product structure

Robust Differentiable SVD

Sub-Matrix Factorization for Real-Time Vote Prediction

Kinetic Euclidean Distance Matrices

Decompositions of dependence for high-dimensional extremes

Optimal denoising of rotationally invariant rectangular matrices

Bootstrapping massive quantum field theories