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We prove maximum and comparison principles for fractional discrete derivatives in the integers. Regularity results when the space is a mesh of length $h$, and approximation theorems to the continuous fractional derivatives are shown. When…

Analysis of PDEs · Mathematics 2016-05-24 Luciano Abadías , Marta de León-Contreras , José L. Torrea

We implement an efficient method of computation of two dimensional Fourier-type integrals based on approximation of the integrand by Gaussian radial basis functions, which constitute a standard tool in approximation theory. As a result, we…

Numerical Analysis · Mathematics 2022-02-07 A. Martinez-Finkelshtein , D. Ramos-Lopez , D. R. Iskander

Solutions to fractional models inherently exhibit non-smooth behavior, which significantly deteriorates the accuracy and therefore efficiency of existing numerical methods. We develop a two-stage data-infused computational framework for…

Numerical Analysis · Mathematics 2018-10-30 Jorge L. Suzuki , Mohsen Zayernouri

Let $B=(B_1(t),..,B_d(t))$ be a $d$-dimensional fractional Brownian motion with Hurst index $\alpha\le 1/4$, or more generally a Gaussian process whose paths have the same local regularity. Defining properly iterated integrals of $B$ is a…

Probability · Mathematics 2015-05-20 Jacques Magnen , Jérémie Unterberger

Shape calculus concerns the calculation of directional derivatives of some quantity of interest, typically expressed as an integral. This article introduces a type of shape calculus based on localized dilation of boundary faces through…

Numerical Analysis · Mathematics 2023-05-29 Martin Berggren

In this paper we propose two proximal gradient algorithms for fractional programming problems in real Hilbert spaces, where the numerator is a proper, convex and lower semicontinuous function and the denominator is a smooth function, either…

Optimization and Control · Mathematics 2016-02-01 Radu Ioan Bot , Ernö Robert Csetnek

Using kicked differential equations of motion with derivatives of noninteger orders, we obtain generalizations of the dissipative standard map. The main property of these generalized maps, which are called fractional maps, is long-term…

Chaotic Dynamics · Physics 2014-03-03 Vasily E. Tarasov , Mark Edelman

Computational costs of numerically solving multidimensional partial differential equations (PDEs) increase significantly when the spatial dimensions of the PDEs are high, due to large number of spatial grid points. For multidimensional…

Numerical Analysis · Mathematics 2019-05-01 Yuan Liu , Yingda Cheng , Shanqin Chen , Yong-Tao Zhang

Conical functions appear in a large number of applications in physics and engineering. In this paper we describe an extension of our module CONICAL for the computation of conical functions. Specifically, the module includes now a routine…

Mathematical Software · Computer Science 2017-06-07 T. M. Dunster , A. Gil , J. Segura , N. M. Temme

A new calculus based on fractal subsets of the real line is formulated. In this calculus, an integral of order $\alpha, 0 < \alpha \leq 1$, called $F^\alpha$-integral, is defined, which is suitable to integrate functions with fractal…

Mathematical Physics · Physics 2007-05-23 Abhay Parvate , A. D. Gangal

In this work, we propose a numerical approach for simulations of large deformations of interfaces in a level set framework. To obtain a fast and viable numerical solution in both time and space, temporal discretization is based on the…

General Mathematics · Mathematics 2023-05-30 Aymen Laadhari , Ahmad Deeb

Fractional partial differential equations (FDEs) are used to describe phenomena that involve a "non-local" or "long-range" interaction of some kind. Accurate and practical numerical approximation of their solutions is challenging due to the…

Numerical Analysis · Mathematics 2019-07-18 Justin Crum , Joshua A. Levine , Andrew Gillette

While the definition of a fractional integral may be codified by Riemann and Liouville, an agreed-upon fractional derivative has eluded discovery for many years. This is likely a result of integral definitions including numerous constants…

Classical Analysis and ODEs · Mathematics 2018-10-10 Evan Camrud

When applying the finite-differences method to numerically solve the one-dimensional diffusion equation, one must choose discretization steps $\Delta x$, $\Delta t$ in space and time, respectively. By applying large-deviation theory on the…

Statistical Mechanics · Physics 2024-04-09 Naftali R. Smith

Spectral discretizations of fractional derivative operators are examined, where the approximation basis is related to the set of Jacobi polynomials. The pseudo-spectral method is implemented by assuming that the grid, used to represent the…

Numerical Analysis · Mathematics 2018-03-29 Lorella Fatone , Daniele Funaro

The solutions to many problems in the mathematical, computational, and physical sciences often involve multidimensional integrals. A direct numerical evaluation of the integral incurs a computational cost that is exponential in the number…

Statistical Mechanics · Physics 2026-04-15 Ryan T. Grimm , Alexander J. Staat , Joel D. Eaves

We present a super-high-efficiency approximate computing scheme for series sum and discrete Fourier transform. The summation of a series sum or a discrete Fourier transform is approximated by summing over part of the terms multiplied by…

Numerical Analysis · Mathematics 2013-12-09 Xin-Zhong Yan

In the present article, a new method for the evaluation of fractional derivatives of arbitrary real order is proposed. Numerous but inequivalent formulations have been given in the past. Some of them exhibit unsatisfactory properties such…

Functional Analysis · Mathematics 2021-05-04 Cyril Belardinelli

We propose an efficient algorithm to compute the real roots of a sparse polynomial $f\in\mathbb{R}[x]$ having $k$ non-zero real-valued coefficients. It is assumed that arbitrarily good approximations of the non-zero coefficients are given…

Symbolic Computation · Computer Science 2017-04-25 Gorav Jindal , Michael Sagraloff

We propose a numerical accountant for evaluating the tight $(\varepsilon,\delta)$-privacy loss for algorithms with discrete one dimensional output. The method is based on the privacy loss distribution formalism and it uses the recently…

Machine Learning · Statistics 2021-06-24 Antti Koskela , Joonas Jälkö , Lukas Prediger , Antti Honkela
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