- scipy.optimize.brenth(f, a, b, args=(), xtol=1e-12, rtol=4.4408920985006262e-16, maxiter=100, full_output=False, disp=True)¶
Find root of f in [a,b].
A variation on the classic Brent routine to find a zero of the function f between the arguments a and b that uses hyperbolic extrapolation instead of inverse quadratic extrapolation. There was a paper back in the 1980’s ... f(a) and f(b) cannot have the same signs. Generally on a par with the brent routine, but not as heavily tested. It is a safe version of the secant method that uses hyperbolic extrapolation. The version here is by Chuck Harris.
f : function
Python function returning a number. f must be continuous, and f(a) and f(b) must have opposite signs.
a : number
One end of the bracketing interval [a,b].
b : number
The other end of the bracketing interval [a,b].
xtol : number, optional
The routine converges when a root is known to lie within xtol of the value return. Should be >= 0. The routine modifies this to take into account the relative precision of doubles.
rtol : number, optional
The routine converges when a root is known to lie within rtol times the value returned of the value returned. Should be >= 0. Defaults to np.finfo(float).eps * 2.
maxiter : number, 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.
x0 : float
Zero of f between a and b.
r : RootResults (present if full_output = True)
Object containing information about the convergence. In particular, r.converged is True if the routine converged.
- nonlinear least squares minimizer
- n-dimensional root-finding
- scalar fixed-point finder