activity
20182022
most citedFast and stable randomized low-rank matrix approximation

18 citations · 25 across the 12 of their papers we have counts for

collaborators

22 papers

math.SP2022

Eigenvalue avoidance of structured matrices depending smoothly on a real parameter

Yuji Nakatsukasa, Vanni Noferini

We explore the concept of eigenvalue avoidance, which is well understood for real symmetric and Hermitian matrices, for other classes of structured matrices. We adopt a differentia…

math.NA2022

A Structure-Preserving Divide-and-Conquer Method for Pseudosymmetric Matrices

Peter Benner, Yuji Nakatsukasa, Carolin Penke

We devise a spectral divide-and-conquer scheme for matrices that are self-adjoint with respect to a given indefinite scalar product (i.e. pseudosymmetic matrices). The pseudosymmet…

math.NA2022

Randomized algorithms for Tikhonov regularization in linear least squares

Maike Meier, Yuji Nakatsukasa

We describe two algorithms to efficiently solve regularized linear least squares systems based on sketching. The algorithms compute preconditioners for $\min \|Ax-b\|^2_2 + λ\|x\|^…

cs.DS20223 cited

Stochastic diagonal estimation: probabilistic bounds and an improved algorithm

Robert A. Baston, Yuji Nakatsukasa

We study the problem of estimating the diagonal of an implicitly given matrix . For such a matrix we have access to an oracle that allows us to evaluate the matrix vector produc…

math.NA2021

Least-squares spectral methods for ODE eigenvalue problems

Behnam Hashemi, Yuji Nakatsukasa

We develop spectral methods for ODEs and operator eigenvalue problems that are based on a least-squares formulation of the problem. The key tool is a method for rectangular general…

math.NA2021

Full operator preconditioning and the accuracy of solving linear systems

Stephan Mohr, Yuji Nakatsukasa, Carolina Urzúa-Torres

Unless special conditions apply, the attempt to solve ill-conditioned systems of linear equations with standard numerical methods leads to uncontrollably high numerical error. Ofte…