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The Euclidean Algorithm is the often forgotten key to rational approximation techniques, including Taylor, Lagrange, Hermite, osculating, cubic spline, Chebyshev, Pade and other interpolation schemes. A unified view of these various…

Numerical Analysis · Mathematics 2007-05-23 Garret Sobczyk

It is well known that rational approximation theory involves degenerate hypergeometric functions and, in particular, the Pad\'e approximation of the exponential function is closely related to Kummer hypergeometric functions. Recently, in…

Optimization and Control · Mathematics 2022-12-06 Islam Boussaada , Guilherme Mazanti , Silviu-Iulian Niculescu

This study introduces the reader to the theory of approximating the solution(s) of a non-linear, second order, ordinary differential equation (ODE) with piecewise polynomial functions by using the collocation method. It then focuses on the…

Numerical Analysis · Mathematics 2018-05-09 J Hamish M Darbyshire

We propose and investigate two new methods to approximate $f({\bf A}){\bf b}$ for large, sparse, Hermitian matrices ${\bf A}$. The main idea behind both methods is to first estimate the spectral density of ${\bf A}$, and then find…

Numerical Analysis · Computer Science 2018-08-30 Li Fan , David I Shuman , Shashanka Ubaru , Yousef Saad

Solving an acoustic wave equation using a parabolic approximation is a popular approach for many existing ocean acoustic models. Commonly used parabolic equation (PE) model programs, such as the range-dependent acoustic model (RAM), are…

Computational Engineering, Finance, and Science · Computer Science 2021-06-02 Houwang Tu , Yongxian Wang , Xian Ma , Xunjiang Zhu

When an Approximation Theorist looks at well-posed PDE problems or operator equations, and standard solution algorithms like Finite Elements, Rayleigh-Ritz or Trefftz techniques, methods of fundamental or particular solutions and their…

Numerical Analysis · Mathematics 2018-06-20 Robert Schaback

While Spectral Methods have long been used for Principal Component Analysis, this survey focusses on work over the last 15 years with three salient features: (i) Spectral methods are useful not only for numerical problems, but also discrete…

Data Structures and Algorithms · Computer Science 2010-04-09 Ravindran Kannan

We propose an approximation of nonlinear renewal equations by means of ordinary differential equations. We consider the integrated state, which is absolutely continuous and satisfies a delay differential equation. By applying the…

Numerical Analysis · Mathematics 2021-03-23 Francesca Scarabel , Odo Diekmann , Rossana Vermiglio

The main purpose of this paper is to compare the convergence properties of Pad\'e approximants and rational Hermite-Pad\'e approximants for some model class of multivalued analytic functions based of the inverse Zhoukovsky transform. We…

Complex Variables · Mathematics 2026-05-15 Nikolay R. Ikonomov , Sergey P. Suetin

Using the Thue-Siegel method, we obtain effective improvements on Liouville's irrationality measure for certain one-parameter families of algebraic numbers, defined by equations of the type $(t-a)Q(t)+P(t)=0$. We apply these to some…

Number Theory · Mathematics 2018-07-17 Gabriel Andreas Dill

In some applications, one is interested in reconstructing a function $f$ from its Fourier series coefficients. The problem is that the Fourier series is slowly convergent if the function is non-periodic, or is non-smooth. In this paper, we…

Numerical Analysis · Mathematics 2020-04-14 David Levin

Power series representations for special functions are computationally satisfactory only in the vicinity of the expansion point. Thus, it is an obvious idea to use instead Pad\'{e} approximants or other rational functions constructed from…

Numerical Analysis · Mathematics 2025-10-20 Ernst Joachim Weniger

A spectral decomposition method is used to obtain solutions to a class of nonlinear differential equations. We extend this approach to the analysis of the fractional form of these equations and demonstrate the method by applying it to the…

Mathematical Physics · Physics 2015-08-14 Malgorzata Turalska , Bruce J. West

We consider large linear and nonlinear fixed point problems, and solution with proximal algorithms. We show that there is a close connection between two seemingly different types of methods from distinct fields: 1) Proximal iterations for…

Numerical Analysis · Computer Science 2019-09-05 Dimitri P. Bertsekas

In this paper, we propose a numerical method to approximate the solution of partial differential equations in irregular domains with no-flux boundary conditions by means of spectral methods. The main features of this method are its…

Numerical Analysis · Mathematics 2007-05-23 Alfonso Bueno-Orovio , Victor M. Perez-Garcia , Flavio H. Fenton

This paper focuses on numerical approximation for fractional powers of elliptic operators on $2$-d manifolds. Firstly, parametric finite element method is employed to discretize the original problem. We then approximate fractional powers of…

Numerical Analysis · Mathematics 2022-07-01 Beiping Duan

The solutions of fractional differential equations (FDEs) have a natural singularity at the initial point. The accuracy of their numerical solutions is lower than the accuracy of the numerical solutions of FDEs whose solutions are…

Numerical Analysis · Mathematics 2018-06-11 Yuri Dimitrov , Ivan Dimov , Venelin Todorov

Partial differential equations (PDEs) on surfaces appear in many applications throughout the natural and applied sciences. The classical closest point method (Ruuth and Merriman, J. Comput. Phys. 227(3):1943-1961, [2008]) is an embedding…

Numerical Analysis · Mathematics 2018-05-17 Argyrios Petras , Leevan Ling , Steven J. Ruuth

We describe an extension of the Taylor method for the numerical solution of ODEs that uses Pad\'e approximants to obtain extremely precise numerical results. The accuracy of the results is essentially limited only by the computer time and…

Computational Physics · Physics 2022-05-04 Paolo Amore

We consider the proximal-gradient method for minimizing an objective function that is the sum of a smooth function and a non-smooth convex function. A feature that distinguishes our work from most in the literature is that we assume that…

Optimization and Control · Mathematics 2022-11-07 Yutong Dai , Daniel P. Robinson