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# scipy.optimize.newton¶

scipy.optimize.newton(func, x0, fprime=None, args=(), tol=1.48e-08, maxiter=50, fprime2=None)[source]

Find a zero using the Newton-Raphson or secant method.

Find a zero of the function func given a nearby starting point x0. The Newton-Raphson 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 Newton-Raphson or the secant method is used. If it is given, parabolic Halley’s method is used. zero : float Estimated location where function is zero.

fsolve