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This paper investigates, a new class of fractional order Runge-Kutta (FORK) methods for numerical approximation to the solution of fractional differential equations (FDEs). By using the Caputo generalizedTaylor formula and the total…

Numerical Analysis · Mathematics 2023-03-06 F. Ghoreishi , R. Ghaffari

Local fractional derivative and integrals are revealed as one of useful tools to deal with everywhere continuous but nowhere differentiable functions in fractal areas ranging from fundamental science to engineering. In this paper, a…

Mathematical Physics · Physics 2012-06-25 Yang Xiao-Jun

Minimizing loss functions is central to machine-learning training. Although first-order methods dominate practical applications, higher-order techniques such as Newton's method can deliver greater accuracy and faster convergence, yet are…

Machine Learning · Computer Science 2025-11-25 Giuseppe Carrino , Elena Loli Piccolomini , Elisa Riccietti , Theo Mary

In this paper we consider the Riemann--Liouville fractional integral $\mathcal{N}^{\alpha,\nu}(t)= \frac{1}{\Gamma(\alpha)} \int_0^t (t-s)^{\alpha-1}N^\nu(s) \, \mathrm ds $, where $N^\nu(t)$, $t \ge 0$, is a fractional Poisson process of…

Probability · Mathematics 2014-03-06 Enzo Orsingher , Federico Polito

We develop a finite difference approximation of order $\alpha$ for the $\alpha$-fractional derivative. The weights of the approximation scheme have the same rate-matrix type properties as the popular Gr\"unwald scheme. In particular,…

Numerical Analysis · Mathematics 2021-12-21 Boris Baeumer , Mihály Kovács , Matthew Parry

Two approximations, derived from continuous expansions of Riemann-Liouville fractional derivatives into series involving integer order derivatives, are studied. Using those series, one can formally transform any problem that contains…

Optimization and Control · Mathematics 2013-05-10 Shakoor Pooseh , Ricardo Almeida , Delfim F. M. Torres

A zero-finding technique for solving nonlinear equations more efficiently than they usually are with traditional iterative methods in which the order of convergence is improved is presented. The key idea in deriving this procedure is to…

Numerical Analysis · Mathematics 2011-06-07 Miquel Grau-Sánchez , José Luis Díaz-Barrero

It is well known that using high-order numerical algorithms to solve fractional differential equations leads to almost the same computational cost with low-order ones but the accuracy (or convergence order) is greatly improved, due to the…

Numerical Analysis · Mathematics 2017-05-25 Hengfei Ding , Changpin Li

In this paper, based on Newton interpolation we have proposed a numerical scheme of predictor-corrector type in order to solve fractional differential equations with the fractional derivative involving the Mittag-Leffler function. We have…

Numerical Analysis · Mathematics 2025-11-05 Sami Aljhani

We investigate the problem of sequential linear data prediction for real life big data applications. The second order algorithms, i.e., Newton-Raphson Methods, asymptotically achieve the performance of the "best" possible linear data…

Data Structures and Algorithms · Computer Science 2017-01-20 Burak C. Civek , Suleyman S. Kozat

It is demonstrated is this letter that linear multistep methods for integrating ordinary differential equations can be used to develop a family of fast forward scattering algorithms with higher orders of convergence. Excluding the cost of…

Computational Physics · Physics 2018-03-28 Vishal Vaibhav

In this paper we propose an algorithm for the numerical solution of arbitrary differential equations of fractional order. The algorithm is obtained by using the following decomposition of the differential equation into a system of…

Numerical Analysis · Mathematics 2025-10-20 Leszczynski Jacek , Ciesielski Mariusz

In fractional calculus there are two approaches to obtain fractional derivatives. The first approach is by iterating the integral and then defining a fractional order by using Cauchy formula to obtain Riemann fractional integrals and…

Dynamical Systems · Mathematics 2012-10-02 Thabet Abdeljawad , Dumitru Baleanu , Fahd Jarad , Ravi Agarwal

This paper presents a new approach of constructing $\alpha$-fractal interpolation functions (FIFs) using neural network operators, integrating concepts from approximation theory. Initially, we construct $\alpha$-fractals utilizing neural…

Machine Learning · Computer Science 2025-05-13 Aaqib Ayoub Bhat , Asif Khan , M. Mursaleen

In this paper, we present a new numerical method to solve fractional differential equations. Given a fractional derivative of arbitrary real order, we present an approximation formula for the fractional operator that involves integer-order…

Numerical Analysis · Mathematics 2015-12-16 Ricardo Almeida , Nuno R. O. Bastos

Recently, Mboup, Join and Fliess [27], [28] introduced non-asymptotic integer order differentiators by using an algebraic parametric estimation method [7], [8]. In this paper, in order to obtain non-asymptotic fractional order…

Numerical Analysis · Mathematics 2012-07-03 Dayan Liu , Olivier Gibaru , Wilfrid Perruquetti

Considering the large number of fractional operators that exist, and since it does not seem that their number will stop increasing soon at the time of writing this paper, it is presented for the first time, as far as the authors know, a…

Numerical Analysis · Mathematics 2024-03-27 A. Torres-Hernandez , F. Brambila-Paz

This paper presents a numerical method to solve a time-fractional Burgers equation, achieving order of convergence $(2-\alpha)$ in time, here $\alpha$ represents the order of the time derivative. The fractional derivative is modeled by…

Numerical Analysis · Mathematics 2025-08-29 Deeksha Singh , Swati Yadav , Rajesh K. Pandey

We propose and analyze a class of second-order dynamical systems for continuous-time optimization that incorporate fractional-order gradient terms. The system is given by \begin{equation} \ddot{x}(t) + \frac{\alpha}{t}\dot{x}(t) +…

Optimization and Control · Mathematics 2025-09-16 Tumelo Ranoto

We consider stochastic second-order methods for minimizing smooth and strongly-convex functions under an interpolation condition satisfied by over-parameterized models. Under this condition, we show that the regularized subsampled Newton…

Machine Learning · Computer Science 2020-03-24 Si Yi Meng , Sharan Vaswani , Issam Laradji , Mark Schmidt , Simon Lacoste-Julien