1.1k citations · 3.9k across the 74 of their papers we have counts for
82 papers
Linear algebra at exponential scale via tensor network dimension reduction
Chris Camaño, Ethan N. Epperly, Raphael A. Meyer +1
Many problems in modern scientific computing are challenging because of a \emph{curse of dimension}, where their mathematical formulation involves objects whose dimension is \emph{…
Applied Random Matrix Theory
Joel A. Tropp
Random matrices now play a role in many parts of computational mathematics. To advance these applications, it is desirable to have tools that are flexible, easy to use, and powerfu…
Universality laws for random matrices via exchangeable counterparts
Joel A. Tropp
Recently, Brailovskaya & van Handel (GAFA, 2024) established a suite of nonasymptotic universality laws which demonstrate that the spectral statistics of an independent sum of rand…
Comparison theorems for the extreme eigenvalues of a random symmetric matrix
Joel A. Tropp
This paper establishes a comparison theorem for the maximum eigenvalue of a sum of independent random symmetric matrices. The theorem states that the maximum eigenvalue of the matr…
Faster Linear Algebra Algorithms with Structured Random Matrices
Chris Camaño, Ethan N. Epperly, Raphael A. Meyer +1
To achieve the greatest possible speed, practitioners regularly implement randomized algorithms for low-rank approximation and least-squares regression with structured dimension re…
Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product
Chris Camaño, Ethan N. Epperly, Joel A. Tropp
Tensor networks like matrix product states (MPSs) and matrix product operators (MPOs) are powerful tools for representing exponentially large states and operators, with application…