Analyse numérique avec Python - normale sup
www.normalesup.org › ~glafon › eiffel13Un programme Python très simple appliquant la méthode de Newton dans ce cas est le suivant (on donne comme argument la valeur initiale et le nombre d’itérations souhaité) : > def Newton(x,n) : > a=x > for i in range(n) : > a=a/2+1/a > return a Terminons avec un petit tableau récapitulatif des performances de nos deux algorithmes. À gauche,
Implementation of the Newton-Raphson algorithm in Python and ...
eskatrem.github.io › Newton-RaphsonHere is some pseudo code to implement Newton-Raphson in python with the function to solve as an input: def approx_nr(func,target,candidate=0,tol=0.001,n_max=100): error = abs(func(candidate)-target) n = 1 derivative_func = derivative(func) #this is the hard part while error > tol and n < n_max: candidate = (target-func(candidate))/derivative_func(candidate) + candidate candidate_value = func(candidate) n += 1 error = abs(candidate_value-target) return candidate,n.
Implementation of the Newton-Raphson algorithm in Python ...
eskatrem.github.io/Newton-RaphsonThe code requires 17 iterations to calculate an approximation of $\sqrt{2}$ with a precision of 0.00001. Can we do better? When searching for a better candidate that a and b, the bisection algorithm takes the value $\displaystyle\frac{a+b}{2}$.Taking the average is a reasonable choice but it can seem a bit arbitrary, and that is where lies any improvement of that algorithm.