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Most root-finding algorithms can find some real roots, but cannot certify having found all the roots. Methods for finding all complex roots, such as Aberth method can provide the real roots. However, because of the numerical instability of polynomials (see Wilkinson's polynomial ), they may need arbitrary-precision arithmetic for deciding which ...
Finding roots −1/2, −1/ √ 2, and 1/ √ 2 of the cubic 4x 3 + 2x 2 − 2x − 1, showing how negative coefficients and extended segments are handled. Each number shown on a colored line is the negative of its slope and hence a real root of the polynomial. To employ the method, a diagram is drawn starting at the origin.
In numerical analysis, the Weierstrass method or Durand–Kerner method, discovered by Karl Weierstrass in 1891 and rediscovered independently by Durand in 1960 and Kerner in 1966, is a root-finding algorithm for solving polynomial equations. [1] In other words, the method can be used to solve numerically the equation f(x) = 0,
If x is a simple root of the polynomial , then Laguerre's method converges cubically whenever the initial guess, , is close enough to the root . On the other hand, when x 1 {\displaystyle \ x_{1}\ } is a multiple root convergence is merely linear, with the penalty of calculating values for the polynomial and its first and second derivatives at ...
In numerical analysis, a root-finding algorithm is an algorithm for finding zeros, also called "roots", of continuous functions. A zero of a function f is a number x such that f ( x ) = 0 . As, generally, the zeros of a function cannot be computed exactly nor expressed in closed form , root-finding algorithms provide approximations to zeros.
Wilkinson's polynomial arose in the study of algorithms for finding the roots of a polynomial = =. It is a natural question in numerical analysis to ask whether the problem of finding the roots of p from the coefficients c i is well-conditioned. That is, we hope that a small change in the coefficients will lead to a small change in the roots.
The main advantage of Steffensen's method is that it has quadratic convergence [1] like Newton's method – that is, both methods find roots to an equation just as 'quickly'. In this case quickly means that for both methods, the number of correct digits in the answer doubles with each step.
Formally, if one expands () (), the terms are precisely (), where is either 0 or 1, accordingly as whether is included in the product or not, and k is the number of that are included, so the total number of factors in the product is n (counting with multiplicity k) – as there are n binary choices (include or x), there are terms ...