scipy.optimize.newton¶

scipy.optimize.
newton
(func, x0, fprime=None, args=(), tol=1.48e08, maxiter=50, fprime2=None)[source]¶ Find a zero using the NewtonRaphson or secant method.
Find a zero of the function func given a nearby starting point x0. The NewtonRaphson method is used if the derivative fprime of func is provided, otherwise the secant method is used. If the second order derivate fprime2 of func is provided, parabolic Halley’s method is used.
Parameters: func : function
The function whose zero is wanted. It must be a function of a single variable of the form f(x,a,b,c...), where a,b,c... are extra arguments that can be passed in the
args
parameter.x0 : float
An initial estimate of the zero that should be somewhere near the actual zero.
fprime : function, optional
The derivative of the function when available and convenient. If it is None (default), then the secant method is used.
args : tuple, optional
Extra arguments to be used in the function call.
tol : float, optional
The allowable error of the zero value.
maxiter : int, optional
Maximum number of iterations.
fprime2 : function, optional
The second order derivative of the function when available and convenient. If it is None (default), then the normal NewtonRaphson or the secant method is used. If it is given, parabolic Halley’s method is used.
Returns: zero : float
Estimated location where function is zero.
Notes
The convergence rate of the NewtonRaphson method is quadratic, the Halley method is cubic, and the secant method is subquadratic. This means that if the function is well behaved the actual error in the estimated zero is approximately the square (cube for Halley) of the requested tolerance up to roundoff error. However, the stopping criterion used here is the step size and there is no guarantee that a zero has been found. Consequently the result should be verified. Safer algorithms are brentq, brenth, ridder, and bisect, but they all require that the root first be bracketed in an interval where the function changes sign. The brentq algorithm is recommended for general use in one dimensional problems when such an interval has been found.
Examples
>>> def f(x): ... return (x**3  1) # only one real root at x = 1
>>> from scipy import optimize
fprime
andfprime2
not provided, use secant method>>> root = optimize.newton(f, 1.5) >>> root 1.0000000000000016
Only
fprime
provided, use Newton Raphson method>>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2) >>> root 1.0
fprime2
provided,fprime
provided/not provided use parabolic Halley’s method>>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x) >>> root 1.0000000000000016 >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2, ... fprime2=lambda x: 6 * x) >>> root 1.0