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Volker Grimm

Publications and source records attributed to Volker Grimm.

5 recordsLinked to original sources
arXiv2025

An extended Krylov subspace method for decoding edge-based compressed images by homogeneous diffusion

The heat equation is often used in order to inpaint dropped data in inpainting-based lossy compression schemes. We propose an alternative way to numerically solve the heat equation by an extended Krylov subspace method. The method is very efficient with respect to the direct computation of the solution of the heat equation at large times. And this is exactly what is needed for decoding edge-compressed pictures by homogeneous diffusion.

cs.IT↗
arXiv2022

Discrete gradients in short-range molecular dynamics simulations

Discrete gradients (DG) or more exactly discrete gradient methods are time integration schemes that are custom-built to preserve first integrals or Lyapunov functions of a given ordinary differential equation (ODE). In conservative molecular dynamics (MD) simulations, the energy of the system is constant and therefore a first integral of motion. Hence, discrete gradient methods seem to be a natural choice as an integration scheme in conservative molecular dynamics simulations.

math.NA↗
arXiv2019

A conjugate-gradient-type rational Krylov subspace method for ill-posed problems

Conjugated gradients on the normal equation (CGNE) is a popular method to regularise linear inverse problems. The idea of the method can be summarised as minimising the residuum over a suitable Krylov subspace. It is shown that using the same idea for the shift-and-invert rational Krylov subspace yields an order-optimal regularisation scheme.

math.NA↗
arXiv2017

Automatic smoothness detection of the resolvent Krylov subspace method for the approximation of $C_0$-semigroups

The resolvent Krylov subspace method builds approximations to operator functions $f(A)$ times a vector $v$. For the semigroup and related operator functions, this method is proved to possess the favorable property that the convergence is automatically faster when the vector $v$ is smoother. The user of the method does not need to know the presented theory and alterations of the method are not necessary in order to adapt to the (possibly unknown) smoothness of $v$. The findings are illustrated by numerical experiments.

math.NA↗