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Univariate polynomial root-finding is both classical and important for modern computing. Frequently one seeks just the real roots of a polynomial with real coefficients. They can be approximated at a low computational cost if the polynomial…

Numerical Analysis · Mathematics 2014-07-01 Victor Y. Pan

The usual methods for root finding of polynomials are based on the iteration of a numerical formula for improvement of successive estimations. The unpredictable nature of the iterations prevents to search roots inside a pre-specified region…

Numerical Analysis · Mathematics 2013-08-21 Juan Luis García Zapata , Juan Carlos Díaz Martín

Iteration methods based on barycentric rational interpolation are derived that exhibit accelerating orders of convergence. For univariate root search, the derivative-free methods approach quadratic convergence and the first-derivative…

Numerical Analysis · Mathematics 2020-11-11 Sebastian Cassel

We construct a family of iterations for computing the principal square root of a square matrix $A$ using Zolotarev's rational minimax approximants of the square root function. We show that these rational functions obey a recursion, allowing…

Numerical Analysis · Mathematics 2018-05-01 Evan S. Gawlik

The reciprocal square root is an important computation for which many sophisticated algorithms exist (see for example \cite{Moroz,863046,863031} and the references therein). A common theme is the use of Newton's method to refine the…

Numerical Analysis · Mathematics 2021-12-30 Carlos F. Borges

We present convergence theory for corrected quadrature rules on uniform Cartesian grids for functions with a point singularity. We begin by deriving an error estimate for the punctured trapezoidal rule, and then derive error expansions. We…

Numerical Analysis · Mathematics 2022-08-30 Federico Izzo , Olof Runborg , Richard Tsai

Nonlinear inverse problems have complicated landscapes. Hence the calculation with naive iterative schemes (e.g., Gauss-Newton or conjugate gradients) is trapped in local minima. The (first) Born approximation can avoid this trapping but…

Numerical Analysis · Mathematics 2025-12-02 Akari Ishida , Manabu Machida

The present author recently proposed and proved a relationship theorem between nonlinear polynomial equations and the corresponding Jacobian matrix. By using this theorem, this paper derives a Newton iterative formula without requiring the…

Computational Engineering, Finance, and Science · Computer Science 2024-09-21 W. Chen

We study the problem of computing the largest root of a real rooted polynomial $p(x)$ to within error $\varepsilon $ given only black box access to it, i.e., for any $x \in {\mathbb R}$, the algorithm can query an oracle for the value of…

Data Structures and Algorithms · Computer Science 2016-01-05 Anand Louis , Santosh S. Vempala

In this paper, we consider the problem of solving a constrained system of nonlinear equations. We propose an algorithm based on a combination of the Newton and conditional gradient methods, and establish its local convergence analysis. Our…

Optimization and Control · Mathematics 2016-08-25 Max L. N. Goncalves , Jefferson G. Melo

We present a modification of Newton's method to restore quadratic convergence for isolated singular solutions of polynomial systems. Our method is symbolic-numeric: we produce a new polynomial system which has the original multiple solution…

Numerical Analysis · Mathematics 2007-05-23 Anton Leykin , Jan Verschelde , Ailing Zhao

The numerical integration of an analytical function $f(x)$ using a finite set of equidistant points can be performed by quadrature formulas like the Newton-Cotes. Unlike Gaussian quadrature formulas however, higher-order Newton-Cotes…

Numerical Analysis · Mathematics 2021-08-24 Irfan Muhammad

Quasi-Newton methods form an important class of methods for solving nonlinear optimization problems. In such methods, first order information is used to approximate the second derivative. The aim is to mimic the fast convergence that can be…

Optimization and Control · Mathematics 2025-02-20 Aban Ansari-Önnestam , Anders Forsgren

The secant method is a very effective numerical procedure used for solving nonlinear equations of the form $f(x)=0$. In a recent work [A. Sidi, Generalization of the secant method for nonlinear equations. {\em Appl. Math. E-Notes},…

Numerical Analysis · Mathematics 2021-05-31 Avram Sidi

A method of deriving quadrature rules has been developed which gives nodes and weights for a Gaussian-type rule which integrates functions of the form: f(x,y,t) = a(x,y,t)/((x-t)^2+y^2) + b(x,y,t)/([(x-t)^2+y^2]^{1/2}) +…

Numerical Analysis · Mathematics 2010-09-21 Michael Carley

This paper proposes and develops new Newton-type methods to solve structured nonconvex and nonsmooth optimization problems with justifying their fast local and global convergence by means of advanced tools of variational analysis and…

Optimization and Control · Mathematics 2026-03-03 Pham Duy Khanh , Boris S. Mordukhovich , Vo Thanh Phat

We investigate two well known dynamical systems that are designed to find roots of univariate polynomials by iteration: the methods known by Newton and by Ehrlich-Aberth. Both are known to have found all roots of high degree polynomials…

Numerical Analysis · Mathematics 2020-04-08 Sergey Shemyakov , Roman Chernov , Dzmitry Rumiantsau , Dierk Schleicher , Simon Schmitt , Anton Shemyakov

Four new variants of the Computational Order of Convergence (COC) of a one-point iterative method with memory for solving nonlinear equations are presented. Furthermore, the way to approximate the new variants to the local order of…

Numerical Analysis · Mathematics 2012-02-21 Miquel Grau-Sánchez , Miquel Noguera , Àngela Grau , José R. Herrero

Iterative methods with certified convergence for the computation of Gauss--Jacobi quadratures are described. The methods do not require a priori estimations of the nodes to guarantee its fourth-order convergence. They are shown to be…

Numerical Analysis · Mathematics 2020-08-24 A. Gil , J. Segura , N. M. Temme

Advanced optimization algorithms such as Newton method and AdaGrad benefit from second order derivative or second order statistics to achieve better descent directions and faster convergence rates. At their heart, such algorithms need to…

Machine Learning · Computer Science 2022-08-31 Yao Lu , Mehrtash Harandi , Richard Hartley , Razvan Pascanu