# scipy.optimize.brenth¶

scipy.optimize.brenth(f, a, b, args=(), xtol=2e-12, rtol=8.881784197001252e-16, maxiter=100, full_output=False, disp=True)[source]

Find a root of a function in a bracketing interval using Brent’s method with hyperbolic extrapolation.

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.

Parameters: f : function Python function returning a number. 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.allclose(x, x0, atol=xtol, rtol=rtol), where x is the exact root. The parameter must be nonnegative. As with brentq, for nice functions the method will often satisfy the above condition with xtol/2 and rtol/2. rtol : number, optional The computed root x0 will satisfy np.allclose(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. As with brentq, for nice functions the method will often satisfy the above condition with xtol/2 and rtol/2. 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. 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.

leastsq
nonlinear least squares minimizer
fsolve
n-dimensional root-finding
fixed_point
scalar fixed-point finder

Examples

>>> def f(x):
...     return (x**2 - 1)

>>> from scipy import optimize

>>> root = optimize.brenth(f, -2, 0)
>>> root
-1.0

>>> root = optimize.brenth(f, 0, 2)
>>> root
1.0


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