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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

We report an ongoing work on clustering algorithms for complex roots of a univariate polynomial $p$ of degree $d$ with real or complex coefficients. As in their previous best subdivision algorithms our root-finders are robust even for…

Symbolic Computation · Computer Science 2019-11-18 Rémi Imbach , Victor Y. Pan

In this paper methods for simultaneous finding all roots of generalized polynomials are developed. These methods are related to the case when the roots are multiple. They possess cubic rate of convergence and they are as labour-consuming as…

Numerical Analysis · Mathematics 2025-10-20 A. I. Iliev

A general class of Newton algorithms on Gra{\ss}mann and Lagrange-Gra{\ss}mann manifolds is introduced, that depends on an arbitrary pair of local coordinates. Local quadratic convergence of the algorithm is shown under a suitable condition…

Optimization and Control · Mathematics 2011-11-10 Uwe Helmke , Knut Hüper , Jochen Trumpf

Until recently, the only known method of finding the roots of polynomials over prime power rings, other than fields, was brute force. One reason for this is the lack of a division algorithm, obstructing the use of greatest common divisors.…

Number Theory · Mathematics 2018-11-26 Trajan Hammonds , Jeremy Johnson , Angela Patini , Robert M. Walker

We present a new algorithm to solve polynomial equations, and publish its code, which is 1.6-3 times faster than the ZROOTS subroutine that is commercially available from Numerical Recipes, depending on application. The largest improvement,…

Earth and Planetary Astrophysics · Physics 2012-03-07 J. Skowron , A. Gould

Dynamical systems with quadratic or polynomial drift exhibit complex dynamics, yet compared to nonlinear systems in general form, are often easier to analyze, simulate, control, and learn. Results going back over a century have shown that…

Symbolic Computation · Computer Science 2025-02-17 Boris Kramer , Gleb Pogudin

This paper proposes and develops a new Newton-type algorithm to solve subdifferential inclusions defined by subgradients of extended-real-valued prox-regular functions. The proposed algorithm is formulated in terms of the second-order…

Optimization and Control · Mathematics 2022-09-16 Pham Duy Khanh , Boris Mordukhovich , Vo Thanh Phat

We present generalizations of Newton's method that incorporate derivatives of an arbitrary order $d$ but maintain a polynomial dependence on dimension in their cost per iteration. At each step, our $d^{\text{th}}$-order method uses…

Optimization and Control · Mathematics 2024-06-13 Amir Ali Ahmadi , Abraar Chaudhry , Jeffrey Zhang

For prime $p\equiv-1\bmod d$ and $q$ a power of $p$, we obtain the slopes of the $q$-adic Newton polygons of $L$-functions of $x^d+ax^{d-1}\in \mathbb{F}_q[x]$ with respect to finite characters $\chi$ when $p$ is larger than an explicit…

Number Theory · Mathematics 2015-11-03 Yi Ouyang , Shenxing Zhang

We combine the known methods for univariate polynomial root-finding and for computations in the Frobenius matrix algebra with our novel techniques to advance numerical solution of a univariate polynomial equation, and in particular…

Numerical Analysis · Mathematics 2013-11-26 Victor Y. Pan , Ai-Long Zheng

Consider a root system of type $BC_1$ on the real line $\mathbb R$ with general positive multiplicities. The Cherednik-Opdam transform defines a unitary operator from an $L^2$-space on $\mathbb R$ to a $L^2$-space of $\mathbb C^2$-valued…

Classical Analysis and ODEs · Mathematics 2016-09-07 Lizhong Peng , Genkai Zhang

It has recently been shown that many of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost functions. Importantly, this understanding allows us to safely start…

Machine Learning · Statistics 2017-04-06 Adrian G. Wills , Thomas B. Schön

We present algorithms revealing new families of polynomials allowing sub-exponential detection of p-adic rational roots, relative to the sparse encoding. For instance, we show that the case of honest n-variate (n+1)-nomials is doable in NP…

Number Theory · Mathematics 2010-11-09 Martín Avendaño , Ashraf Ibrahim , J. Maurice Rojas , Korben Rusek

We study the $p$-adic absolute value of the roots of the $L$-functions associated to certain twisted character sums, and additive character sums associated to polynomials $P(x^d)$, when $P$ varies among the space of polynomial of fixed…

Number Theory · Mathematics 2007-06-18 Regis Blache , Eric Ferard

A new version of the Graeffe algorithm for finding all the roots of univariate complex polynomials is proposed. It is obtained from the classical algorithm by a process analogous to renormalization of dynamical systems. This iteration is…

Numerical Analysis · Mathematics 2025-10-20 Gregorio Malajovich , Jorge P. Zubelli

In this paper we develop a new method which is a generalization of the Obreshkoff -Ehrlich method for the cases of algebraic, trigonometric and exponential polynomials. This method has a cubic rate of convergence. It is efficient from the…

Numerical Analysis · Mathematics 2025-10-20 A. I. Iliev

We present a new algorithm for refining a real interval containing a single real root: the new method combines characteristics of the classical Bisection algorithm and Newton's Iteration. Our method exhibits quadratic convergence when…

Numerical Analysis · Mathematics 2014-07-01 John Abbott

We present a new method to calculate analytically the roots of the general complex polynomial of degree three. Thismethod is based on the approach of appropriated changes of variable involving an arbitrary parameter. The advantageof this…

General Mathematics · Mathematics 2018-01-22 Ibrahim Baydoun

We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably…

Machine Learning · Computer Science 2014-05-15 Ohad Shamir , Nathan Srebro , Tong Zhang