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20172026
most citedA fast convolution method for the fractional Laplacian in

1 citations · 1 across the 7 of their papers we have counts for

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math.NA2026

A matrix-based spectral method for the numerical approximation of the fractional Laplacian and the fractional -Laplacian of functions defined on

Loïc Constantin, Carlota M. Cuesta, Francisco de la Hoz

Given a function defined on , its fractional -Laplacian is given by $$(-Δ)_p^su(\vec x)=C_1(n,s,p)\int_{\mathbb R^n}\frac{|u(\vec x)-u(\vec y)|^{p-2}(u(\vec x)-…

math.NA2024

A Non-Recursive, Dimension-Independent Schur-Decomposition Algorithm for -Dimensional Sylvester Tensor Equations

Carlota M. Cuesta, Francisco de la Hoz

In this paper we present a non-recursive direct solver, based on the Bartels-Stewart algorithm, for -dimensional Sylvester tensor equations. The method relies only on Schur deco…

math.NA2024

Numerical Approximation of Riesz-Feller Operators on

Carlota M. Cuesta, Francisco de la Hoz, Ivan Girona

In this paper, we develop an accurate pseudospectral method to approximate numerically the Riesz-Feller operator on , where , and $|γ|\le\min\{α, 2 -…

math.NA2023

Numerical computation of the half Laplacian by means of a fast convolution algorithm

Carlota M. Cuesta, Francisco de la Hoz, Ivan Girona

In this paper, we develop a fast and accurate pseudospectral method to approximate numerically the half Laplacian of a function on , which is equivalent to…

math.NA2022★ 1 cited

A fast convolution method for the fractional Laplacian in

Jorge Cayama, Carlota M. Cuesta, Francisco de la Hoz +1

In this article, we develop a new method to approximate numerically the fractional Laplacian of functions defined on , as well as some more general singular integrals. A…

math.NA2020

Vortex Filament Equation for a regular polygon in the hyperbolic plane

Francisco de la Hoz, Sandeep Kumar, Luis Vega

The aim of this article is twofold. First, we show the evolution of the vortex filament equation (VFE) for a regular planar polygon in the hyperbolic space. Unlike in the Euclidean…