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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.
Newton's method uses curvature information (i.e. the second derivative) to take a more direct route. In calculus , Newton's method (also called Newton–Raphson ) is an iterative method for finding the roots of a differentiable function f {\displaystyle f} , which are solutions to the equation f ( x ) = 0 {\displaystyle f(x)=0} .
If instead one performed Newton-Raphson iterations beginning with an estimate of 10, it would take two iterations to get to 3.66, matching the hyperbolic estimate. For a more typical case like 75, the hyperbolic estimate of 8.00 is only 7.6% low, and 5 Newton-Raphson iterations starting at 75 would be required to obtain a more accurate result.
Newton–Raphson uses Newton's method to find the reciprocal of and multiply that reciprocal by to find the final quotient . The steps of Newton–Raphson division are: Calculate an estimate X 0 {\displaystyle X_{0}} for the reciprocal 1 / D {\displaystyle 1/D} of the divisor D {\displaystyle D} .
It contains a method, now known as the Newton–Raphson method, for approximating the roots of an equation. Isaac Newton had developed a very similar formula in his Method of Fluxions, written in 1671, but this work would not be published until 1736, nearly 50 years after Raphson's Analysis. However, Raphson's version of the method is simpler ...
The backward Euler method is an implicit method, meaning that we have to solve an equation to find y n+1. One often uses fixed-point iteration or (some modification of) the Newton–Raphson method to achieve this.
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Fast-decoupled-load-flow method is a variation on Newton–Raphson that exploits the approximate decoupling of active and reactive flows in well-behaved power networks, and additionally fixes the value of the Jacobian during the iteration in order to avoid costly matrix decompositions. Also referred to as "fixed-slope, decoupled NR".