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20232025
most citedA Stochastic Method for Solving Time-Fractional Differential Equations

5 citations · 12 across the 6 of their papers we have counts for

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math.NA2025

Accelerating a restarted Krylov method for matrix functions with randomization

Nicolas L. Guidotti, Per-Gunnar Martinsson, Juan A. Acebrón +1

Many scientific applications require the evaluation of the action of a matrix function on a vector, and Krylov subspace methods are the most common ones for this task. Since the or…

math.NA2024

Sensitivity analysis of fractional linear systems based on random walks with negligible memory usage

Andrés Centeno, Juan A. Acebrón, José Monteiro

A random walk-based method is proposed to efficiently compute the solution of a large class of fractional in time linear systems of differential equations (linear F-ODE systems), a…

math.NA2024

Survey of a Class of Iterative Row-Action Methods: The Kaczmarz Method

Inês A. Ferreira, Juan A. Acebrón, José Monteiro

The Kaczmarz algorithm is an iterative method that solves linear systems of equations. It stands out among iterative algorithms when dealing with large systems for two reasons. Fir…

math.NA2023★ 5 cited

A Stochastic Method for Solving Time-Fractional Differential Equations

Nicolas L. Guidotti, Juan Acebrón, José Monteiro

We present a stochastic method for efficiently computing the solution of time-fractional partial differential equations (fPDEs) that model anomalous diffusion problems of the subdi…

math.NA2023★ 3 cited

A hybrid probabilistic domain decomposition algorithm suited for very large-scale elliptic PDEs

Francisco Bernal, Jorge Morón-Vidal, Juan A. Acebrón

State of the art domain decomposition algorithms for large-scale boundary value problems (with degrees of freedom) suffer from bounded strong scalability because they invo…