scipy.optimize.toms748¶

scipy.optimize.
toms748
(f, a, b, args=(), k=1, xtol=2e12, rtol=8.881784197001252e16, maxiter=100, full_output=False, disp=True)[source]¶ Find a zero using TOMS Algorithm 748 method.
Implements the Algorithm 748 method of Alefeld, Potro and Shi to find a zero of the function f on the interval [a , b], where f(a) and f(b) must have opposite signs.
It uses a mixture of inverse cubic interpolation and “Newtonquadratic” steps. [APS1995].
 Parameters
 ffunction
Python function returning a scalar. The function \(f\) must be continuous, and \(f(a)\) and \(f(b)\) have opposite signs.
 ascalar,
lower boundary of the search interval
 bscalar,
upper boundary of the search interval
 argstuple, optional
containing extra arguments for the function f. f is called by
f(x, *args)
. kint, optional
The number of Newton quadratic steps to perform each iteration.
k>=1
. xtolscalar, optional
The computed root
x0
will satisfynp.allclose(x, x0, atol=xtol, rtol=rtol)
, wherex
is the exact root. The parameter must be nonnegative. rtolscalar, optional
The computed root
x0
will satisfynp.allclose(x, x0, atol=xtol, rtol=rtol)
, wherex
is the exact root. maxiterint, optional
If convergence is not achieved in maxiter iterations, an error is raised. Must be >= 0.
 full_outputbool, 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 aRootResults
object. dispbool, optional
If True, raise RuntimeError if the algorithm didn’t converge. Otherwise, the convergence status is recorded in the
RootResults
return object.
 Returns
 x0float
Approximate Zero of f
 r
RootResults
(present iffull_output = True
) Object containing information about the convergence. In particular,
r.converged
is True if the routine converged.
Notes
f must be continuous. Algorithm 748 with
k=2
is asymptotically the most efficient algorithm known for finding roots of a four times continuously differentiable function. In contrast with Brent’s algorithm, which may only decrease the length of the enclosing bracket on the last step, Algorithm 748 decreases it each iteration with the same asymptotic efficiency as it finds the root.For easy statement of efficiency indices, assume that f has 4 continuouous deriviatives. For
k=1
, the convergence order is at least 2.7, and with about asymptotically 2 function evaluations per iteration, the efficiency index is approximately 1.65. Fork=2
, the order is about 4.6 with asymptotically 3 function evaluations per iteration, and the efficiency index 1.66. For higher values of k, the efficiency index approaches the kth root of(3k2)
, hencek=1
ork=2
are usually appropriate.References
 APS1995
Alefeld, G. E. and Potra, F. A. and Shi, Yixun, Algorithm 748: Enclosing Zeros of Continuous Functions, ACM Trans. Math. Softw. Volume 221(1995) doi = {10.1145/210089.210111}
Examples
>>> def f(x): ... return (x**3  1) # only one real root at x = 1
>>> from scipy import optimize >>> root, results = optimize.toms748(f, 0, 2, full_output=True) >>> root 1.0 >>> results converged: True flag: 'converged' function_calls: 11 iterations: 5 root: 1.0