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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 ...
Function minimization at minima.hpp with an example locating function minima. Root finding implements the newer TOMS748, a more modern and efficient algorithm than Brent's original, at TOMS748, and Boost.Math rooting finding that uses TOMS748 internally with examples. The Optim.jl package implements the algorithm in Julia (programming language)
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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.
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In numerical analysis, the secant method is a root-finding algorithm that uses a succession of roots of secant lines to better approximate a root of a function f. The secant method can be thought of as a finite-difference approximation of Newton's method , so it is considered a quasi-Newton method .
A root-finding algorithm is a numerical method or algorithm for finding a value x such that f(x) = 0, for a given function f. Here, x is a single real number. Root-finding algorithms are studied in numerical analysis.
The asymptotic behaviour is very good: generally, the iterates x n converge fast to the root once they get close. However, performance is often quite poor if the initial values are not close to the actual root. For instance, if by any chance two of the function values f n−2, f n−1 and f n coincide, the algorithm fails completely. Thus ...