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In calculus, Newton's method (also called Newton–Raphson) is an iterative method for finding the roots of a differentiable function, which are solutions to the equation =. However, to optimize a twice-differentiable f {\displaystyle f} , our goal is to find the roots of f ′ {\displaystyle f'} .
An illustration of Newton's method. In numerical analysis, the Newton–Raphson method, also known simply as Newton's method, named after Isaac Newton and Joseph Raphson, is a root-finding algorithm which produces successively better approximations to the roots (or zeroes) of a real-valued function.
In the mathematical field of numerical analysis, a Newton polynomial, named after its inventor Isaac Newton, [1] is an interpolation polynomial for a given set of data points. The Newton polynomial is sometimes called Newton's divided differences interpolation polynomial because the coefficients of the polynomial are calculated using Newton's ...
Order of accuracy — rate at which numerical solution of differential equation converges to exact solution; Series acceleration — methods to accelerate the speed of convergence of a series Aitken's delta-squared process — most useful for linearly converging sequences; Minimum polynomial extrapolation — for vector sequences; Richardson ...
Solutions to polynomial systems computed using numerical algebraic geometric methods can be certified, meaning that the approximate solution is "correct".This can be achieved in several ways, either a priori using a certified tracker, [7] [8] or a posteriori by showing that the point is, say, in the basin of convergence for Newton's method.
Finding the root of a linear polynomial (degree one) is easy and needs only one division: the general equation + = has solution = /. For quadratic polynomials (degree two), the quadratic formula produces a solution, but its numerical evaluation may require some care for ensuring numerical stability.
The method may be iterated to generate additional terms of an asymptotic expansion to provide a more accurate solution. [11] Iterative methods such as the Newton-Raphson method may generate a more accurate solution. [4] A perturbation series, using the approximate solution as the first term, may also generate a more accurate solution. [5]
The first problem solved by Newton with the Newton-Raphson-Simpson method was the polynomial equation =. He observed that there should be a solution close to 2. Replacing y = x + 2 transforms the equation into = = + + +.