scipy.optimize.brenth¶

scipy.optimize.
brenth
(f, a, b, args=(), xtol=2e12, rtol=8.881784197001252e16, maxiter=100, full_output=False, disp=True)[source]¶ 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.
Parameters:  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 computed root
x0
will satisfynp.allclose(x, x0, atol=xtol, rtol=rtol)
, wherex
is the exact root. The parameter must be nonnegative. As withbrentq
, for nice functions the method will often satisfy the above condition withxtol/2
andrtol/2
. rtol : number, optional
The computed root
x0
will satisfynp.allclose(x, x0, atol=xtol, rtol=rtol)
, wherex
is the exact root. The parameter cannot be smaller than its default value of4*np.finfo(float).eps
. As withbrentq
, for nice functions the method will often satisfy the above condition withxtol/2
andrtol/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.
Returns:  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.
See also
leastsq
 nonlinear least squares minimizer
fmin_l_bfgs_b
,fmin_tnc
,fmin_cobyla
,basinhopping
,differential_evolution
,brute
,fminbound
,brent
,golden
,bracket
fsolve
 ndimensional rootfinding
brentq
,brenth
,ridder
,bisect
,newton
fixed_point
 scalar fixedpoint 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