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20192026
most citedA Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision

3 citations · 5 across the 8 of their papers we have counts for

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11 papers · 1 filter

math.NA2026

A Twin gradient method for unconstrained optimization

Anna De Magistris, Michiel E. Hochstenbach, Gerardo Toraldo

We propose a new strategy for gradient-based unconstrained optimization, involving two parallel sequences of iterates that cooperate to determine their stepsizes via a \textit{Twin…

math.NA2026

Transpose-free linear algebra

Diana Halikias, Michiel E. Hochstenbach, Alex Townsend

We study the limitations of matrix-free algorithms that access a matrix only through forward matrix-vector products (matvecs) , without access to the transpose $A…

math.NA2026

Deterministic and randomized Kaczmarz methods for with applications to color image restoration

Wenli Wang, Duo Liu, Gangrong Qu +1

We study Kaczmarz type methods to solve consistent linear matrix equations. We first present a block Kaczmarz (BK) method that employs a deterministic cyclic row selection strategy…

math.NA2026

On spectral properties and fast initial convergence of the Kaczmarz method

Per Christian Hansen, Michiel E. Hochstenbach

The Kaczmarz method is successfully used for solving discretizations of linear inverse problems, especially in computed tomography where it is known as ART. Practitioners often obs…

math.NA2024

Solving singular generalized eigenvalue problems. Part III: structure preservation

Michiel E. Hochstenbach, Christian Mehl, Bor Plestenjak

In Parts I and II of this series of papers, three new methods for the computation of eigenvalues of singular pencils were developed: rank-completing perturbations, rank-projections…

math.NA2024

Numerical methods for eigenvalues of singular polynomial eigenvalue problems

Michiel E. Hochstenbach, Christian Mehl, Bor Plestenjak

Recently, three numerical methods for the computation of eigenvalues of singular matrix pencils, based on a rank-completing perturbation, a rank-projection, or an augmentation were…