Skip to content
Preprint

Block preconditioning for all-at-once variable-coefficient fractional evolution equations via the GLT analysis

Aug 2026 · 0 citations · 52 references
Mathematics Computer Science

Abstract

We study a class of nonlocal evolutionary partial differential equations with weakly singular temporal kernel and spatially variable diffusion coefficient. The model is posed on $\Omega \subset \mathbb{R}$, and involves a left-sided Riemann--Liouville fractional derivative in space multiplied by a variable coefficient $a(x)$. The temporal derivative is approximated by an $L1$ type scheme, while the spatial operator is discretized by finite difference techniques, resulting in large scale all at once linear systems with a twolevel Toeplitz like structure. We develop and analyze a block lower triangular strategy that mimics the structure of the coefficient matrix while simplifying its components for computational efficiency. The analysis is carried out at the level of matrix sequences by means of generalized locally Toeplitz (GLT) theory. Within this framework, we characterize the asymptotic spectral distribution of the discretized operators and use the associated GLT symbol to guide the construction of the structured approximation. Numerical experiments using the GMRES solver demonstrate that the proposed preconditioning strategy significantly improves convergence rates, robustness, and scalability for large-scale problems. Open problems and possible extensions are briefly discussed at the end of the present work.

View source

Similar papers

Open access Aug 2026

Fast Sine-Transform Preconditioning for Global-in-Time Fractional Diffusion

Time-fractional diffusion equations describe subdiffusive transport in heterogeneous media, but their numerical treatment is complicated by the nonlocal Caputo derivative and by the weak singularity that the solution develops at the initial time. We study a global-in-time discretization that combines spectral collocati...

P. De Luca · 0 citations
Preprint Sep 2026

Space-time fractional stochastic Burgers-type equation

We establish the existence and uniqueness of solutions to a $(1+1)$-dimensional fractional stochastic Burgers-type equation featuring a Caputo fractional time derivative, a fractional Laplacian, and space-time white noise forcing modified by a Riemann-Liouville fractional integral. From a physical perspective, equation...

Ming-Wei Kuo, K. Matetski · 0 citations
Preprint Aug 2026

Analysis of Block Jacobi/Gauss-Seidel and additive/multiplicative Schwarz preconditioning through the theory of GLT sequences, with applications to domain decomposition discretizations

It is proved that, if the BJ, BGS, and MS preconditioners are GLT sequences with symbol $\kappa$, then the sequences of the BJ, BGS, and MS preconditioners are GLT sequences with symbol $\kappa$.

C. Garoni, Abdessadek Rifqui, S. Serra-Capizzano · 0 citations

An Efficient Spectral Technique Based on Operational Matrices to Solve Variable Coefficients Time-Space Fractional Advection-Diffusion Equations

This investigation presents a robust spectral technique for accurately solving timespace fractional advection-diffusion equations (FADEs) characterized by Caputo-type derivatives with coefficients that vary in both time and space. Such equations inherently model nonlocality and memory effects prevalent in various compl...

S. Kheybari, F. Alizadeh, Mohammad Taghi Darvishi et al. · 0 citations
Open access Aug 2026

Spatiotemporal variable-order fractional modeling of long wave interactions: a spectral tau method with Mittag–Leffler-type memory

The long-wave interactions described by the generalized Hirota–Satsuma (GHS) coupled KdV system are studied here through a memory dependent model in which the time fractional order varies in both space and time. The fractional derivative is taken in the Atangana Baleanu Caputo (ABC) sense, whose Mittag Leffler kernel i...

W. A. Hashem, Mofida Zaki · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.