scipy.optimize._zeros_py:brentq

Parameters

f : function

Python function returning a number. The function $f$ must be continuous, and $f(a)$ and $f(b)$ must have opposite signs.

a : scalar

One end of the bracketing interval $[a,b]$.

b : scalar

The other end of the bracketing interval $[a,b]$.

xtol : number, optional

The computed root x0 will satisfy np.isclose(x, x0, atol=xtol, rtol=rtol), where x is the exact root. The parameter must be positive. For nice functions, Brent's method will often satisfy the above condition with xtol/2 and rtol/2. [Brent1973]

rtol : number, optional

The computed root x0 will satisfy np.isclose(x, x0, atol=xtol, rtol=rtol), where x is the exact root. The parameter cannot be smaller than its default value of 4*np.finfo(float).eps. For nice functions, Brent's method will often satisfy the above condition with xtol/2 and rtol/2. [Brent1973]

maxiter : int, optional

If convergence is not achieved in maxiter iterations, an error is raised. Must be >= 0.

args : tuple, optional

Containing extra arguments for the function f. f is called by apply(f, (x)+args).

full_output : bool, optional

If full_output is False, the root is returned. If full_output is True, the return value is (x, r), where x is the root, and r is a RootResults object.

disp : bool, optional

If True, raise RuntimeError if the algorithm didn't converge. Otherwise, the convergence status is recorded in any RootResults return object.