613 lines
21 KiB
Python
613 lines
21 KiB
Python
|
from __future__ import division, print_function, absolute_import
|
||
|
|
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
from . import _zeros
|
||
|
|
from numpy import finfo, sign, sqrt
|
||
|
|
|
||
|
|
_iter = 100
|
||
|
|
_xtol = 2e-12
|
||
|
|
_rtol = 4*finfo(float).eps
|
||
|
|
|
||
|
|
__all__ = ['newton', 'bisect', 'ridder', 'brentq', 'brenth']
|
||
|
|
|
||
|
|
CONVERGED = 'converged'
|
||
|
|
SIGNERR = 'sign error'
|
||
|
|
CONVERR = 'convergence error'
|
||
|
|
flag_map = {0: CONVERGED, -1: SIGNERR, -2: CONVERR}
|
||
|
|
|
||
|
|
|
||
|
|
class RootResults(object):
|
||
|
|
""" Represents the root finding result.
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
root : float
|
||
|
|
Estimated root location.
|
||
|
|
iterations : int
|
||
|
|
Number of iterations needed to find the root.
|
||
|
|
function_calls : int
|
||
|
|
Number of times the function was called.
|
||
|
|
converged : bool
|
||
|
|
True if the routine converged.
|
||
|
|
flag : str
|
||
|
|
Description of the cause of termination.
|
||
|
|
"""
|
||
|
|
def __init__(self, root, iterations, function_calls, flag):
|
||
|
|
self.root = root
|
||
|
|
self.iterations = iterations
|
||
|
|
self.function_calls = function_calls
|
||
|
|
self.converged = flag == 0
|
||
|
|
try:
|
||
|
|
self.flag = flag_map[flag]
|
||
|
|
except KeyError:
|
||
|
|
self.flag = 'unknown error %d' % (flag,)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
attrs = ['converged', 'flag', 'function_calls',
|
||
|
|
'iterations', 'root']
|
||
|
|
m = max(map(len, attrs)) + 1
|
||
|
|
return '\n'.join([a.rjust(m) + ': ' + repr(getattr(self, a))
|
||
|
|
for a in attrs])
|
||
|
|
|
||
|
|
|
||
|
|
def results_c(full_output, r):
|
||
|
|
if full_output:
|
||
|
|
x, funcalls, iterations, flag = r
|
||
|
|
results = RootResults(root=x,
|
||
|
|
iterations=iterations,
|
||
|
|
function_calls=funcalls,
|
||
|
|
flag=flag)
|
||
|
|
return x, results
|
||
|
|
else:
|
||
|
|
return r
|
||
|
|
|
||
|
|
|
||
|
|
# Newton-Raphson method
|
||
|
|
def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50,
|
||
|
|
fprime2=None):
|
||
|
|
"""
|
||
|
|
Find a zero using the Newton-Raphson or secant method.
|
||
|
|
|
||
|
|
Find a zero of the function `func` given a nearby starting point `x0`.
|
||
|
|
The Newton-Raphson method is used if the derivative `fprime` of `func`
|
||
|
|
is provided, otherwise the secant method is used. If the second order
|
||
|
|
derivative `fprime2` of `func` is provided, then Halley's method is used.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
func : function
|
||
|
|
The function whose zero is wanted. It must be a function of a
|
||
|
|
single variable of the form f(x,a,b,c...), where a,b,c... are extra
|
||
|
|
arguments that can be passed in the `args` parameter.
|
||
|
|
x0 : float
|
||
|
|
An initial estimate of the zero that should be somewhere near the
|
||
|
|
actual zero.
|
||
|
|
fprime : function, optional
|
||
|
|
The derivative of the function when available and convenient. If it
|
||
|
|
is None (default), then the secant method is used.
|
||
|
|
args : tuple, optional
|
||
|
|
Extra arguments to be used in the function call.
|
||
|
|
tol : float, optional
|
||
|
|
The allowable error of the zero value.
|
||
|
|
maxiter : int, optional
|
||
|
|
Maximum number of iterations.
|
||
|
|
fprime2 : function, optional
|
||
|
|
The second order derivative of the function when available and
|
||
|
|
convenient. If it is None (default), then the normal Newton-Raphson
|
||
|
|
or the secant method is used. If it is not None, then Halley's method
|
||
|
|
is used.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
zero : float
|
||
|
|
Estimated location where function is zero.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
brentq, brenth, ridder, bisect
|
||
|
|
fsolve : find zeroes in n dimensions.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The convergence rate of the Newton-Raphson method is quadratic,
|
||
|
|
the Halley method is cubic, and the secant method is
|
||
|
|
sub-quadratic. This means that if the function is well behaved
|
||
|
|
the actual error in the estimated zero is approximately the square
|
||
|
|
(cube for Halley) of the requested tolerance up to roundoff
|
||
|
|
error. However, the stopping criterion used here is the step size
|
||
|
|
and there is no guarantee that a zero has been found. Consequently
|
||
|
|
the result should be verified. Safer algorithms are brentq,
|
||
|
|
brenth, ridder, and bisect, but they all require that the root
|
||
|
|
first be bracketed in an interval where the function changes
|
||
|
|
sign. The brentq algorithm is recommended for general use in one
|
||
|
|
dimensional problems when such an interval has been found.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> def f(x):
|
||
|
|
... return (x**3 - 1) # only one real root at x = 1
|
||
|
|
|
||
|
|
>>> from scipy import optimize
|
||
|
|
|
||
|
|
``fprime`` not provided, use secant method
|
||
|
|
|
||
|
|
>>> root = optimize.newton(f, 1.5)
|
||
|
|
>>> root
|
||
|
|
1.0000000000000016
|
||
|
|
>>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x)
|
||
|
|
>>> root
|
||
|
|
1.0000000000000016
|
||
|
|
|
||
|
|
Only ``fprime`` provided, use Newton Raphson method
|
||
|
|
|
||
|
|
>>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2)
|
||
|
|
>>> root
|
||
|
|
1.0
|
||
|
|
|
||
|
|
Both ``fprime2`` and ``fprime`` provided, use Halley's method
|
||
|
|
|
||
|
|
>>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2,
|
||
|
|
... fprime2=lambda x: 6 * x)
|
||
|
|
>>> root
|
||
|
|
1.0
|
||
|
|
|
||
|
|
"""
|
||
|
|
if tol <= 0:
|
||
|
|
raise ValueError("tol too small (%g <= 0)" % tol)
|
||
|
|
if maxiter < 1:
|
||
|
|
raise ValueError("maxiter must be greater than 0")
|
||
|
|
# Multiply by 1.0 to convert to floating point. We don't use float(x0)
|
||
|
|
# so it still works if x0 is complex.
|
||
|
|
p0 = 1.0 * x0
|
||
|
|
if fprime is not None:
|
||
|
|
# Newton-Rapheson method
|
||
|
|
for iter in range(maxiter):
|
||
|
|
fder = fprime(p0, *args)
|
||
|
|
if fder == 0:
|
||
|
|
msg = "derivative was zero."
|
||
|
|
warnings.warn(msg, RuntimeWarning)
|
||
|
|
return p0
|
||
|
|
fval = func(p0, *args)
|
||
|
|
newton_step = fval / fder
|
||
|
|
if fprime2 is None:
|
||
|
|
# Newton step
|
||
|
|
p = p0 - newton_step
|
||
|
|
else:
|
||
|
|
fder2 = fprime2(p0, *args)
|
||
|
|
# Halley's method
|
||
|
|
p = p0 - newton_step / (1.0 - 0.5 * newton_step * fder2 / fder)
|
||
|
|
if abs(p - p0) < tol:
|
||
|
|
return p
|
||
|
|
p0 = p
|
||
|
|
else:
|
||
|
|
# Secant method
|
||
|
|
if x0 >= 0:
|
||
|
|
p1 = x0*(1 + 1e-4) + 1e-4
|
||
|
|
else:
|
||
|
|
p1 = x0*(1 + 1e-4) - 1e-4
|
||
|
|
q0 = func(p0, *args)
|
||
|
|
q1 = func(p1, *args)
|
||
|
|
for iter in range(maxiter):
|
||
|
|
if q1 == q0:
|
||
|
|
if p1 != p0:
|
||
|
|
msg = "Tolerance of %s reached" % (p1 - p0)
|
||
|
|
warnings.warn(msg, RuntimeWarning)
|
||
|
|
return (p1 + p0)/2.0
|
||
|
|
else:
|
||
|
|
p = p1 - q1*(p1 - p0)/(q1 - q0)
|
||
|
|
if abs(p - p1) < tol:
|
||
|
|
return p
|
||
|
|
p0 = p1
|
||
|
|
q0 = q1
|
||
|
|
p1 = p
|
||
|
|
q1 = func(p1, *args)
|
||
|
|
msg = "Failed to converge after %d iterations, value is %s" % (maxiter, p)
|
||
|
|
raise RuntimeError(msg)
|
||
|
|
|
||
|
|
|
||
|
|
def bisect(f, a, b, args=(),
|
||
|
|
xtol=_xtol, rtol=_rtol, maxiter=_iter,
|
||
|
|
full_output=False, disp=True):
|
||
|
|
"""
|
||
|
|
Find root of a function within an interval.
|
||
|
|
|
||
|
|
Basic bisection routine to find a zero of the function `f` between the
|
||
|
|
arguments `a` and `b`. `f(a)` and `f(b)` cannot have the same signs.
|
||
|
|
Slow but sure.
|
||
|
|
|
||
|
|
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 satisfy ``np.allclose(x, x0,
|
||
|
|
atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The
|
||
|
|
parameter must be nonnegative.
|
||
|
|
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``.
|
||
|
|
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.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> def f(x):
|
||
|
|
... return (x**2 - 1)
|
||
|
|
|
||
|
|
>>> from scipy import optimize
|
||
|
|
|
||
|
|
>>> root = optimize.bisect(f, 0, 2)
|
||
|
|
>>> root
|
||
|
|
1.0
|
||
|
|
|
||
|
|
>>> root = optimize.bisect(f, -2, 0)
|
||
|
|
>>> root
|
||
|
|
-1.0
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
brentq, brenth, bisect, newton
|
||
|
|
fixed_point : scalar fixed-point finder
|
||
|
|
fsolve : n-dimensional root-finding
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not isinstance(args, tuple):
|
||
|
|
args = (args,)
|
||
|
|
if xtol <= 0:
|
||
|
|
raise ValueError("xtol too small (%g <= 0)" % xtol)
|
||
|
|
if rtol < _rtol:
|
||
|
|
raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol))
|
||
|
|
r = _zeros._bisect(f,a,b,xtol,rtol,maxiter,args,full_output,disp)
|
||
|
|
return results_c(full_output, r)
|
||
|
|
|
||
|
|
|
||
|
|
def ridder(f, a, b, args=(),
|
||
|
|
xtol=_xtol, rtol=_rtol, maxiter=_iter,
|
||
|
|
full_output=False, disp=True):
|
||
|
|
"""
|
||
|
|
Find a root of a function in an interval.
|
||
|
|
|
||
|
|
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 satisfy ``np.allclose(x, x0,
|
||
|
|
atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The
|
||
|
|
parameter must be nonnegative.
|
||
|
|
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``.
|
||
|
|
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
|
||
|
|
--------
|
||
|
|
brentq, brenth, bisect, newton : one-dimensional root-finding
|
||
|
|
fixed_point : scalar fixed-point finder
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Uses [Ridders1979]_ method to find a zero of the function `f` between the
|
||
|
|
arguments `a` and `b`. Ridders' method is faster than bisection, but not
|
||
|
|
generally as fast as the Brent routines. [Ridders1979]_ provides the
|
||
|
|
classic description and source of the algorithm. A description can also be
|
||
|
|
found in any recent edition of Numerical Recipes.
|
||
|
|
|
||
|
|
The routine used here diverges slightly from standard presentations in
|
||
|
|
order to be a bit more careful of tolerance.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> def f(x):
|
||
|
|
... return (x**2 - 1)
|
||
|
|
|
||
|
|
>>> from scipy import optimize
|
||
|
|
|
||
|
|
>>> root = optimize.ridder(f, 0, 2)
|
||
|
|
>>> root
|
||
|
|
1.0
|
||
|
|
|
||
|
|
>>> root = optimize.ridder(f, -2, 0)
|
||
|
|
>>> root
|
||
|
|
-1.0
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [Ridders1979]
|
||
|
|
Ridders, C. F. J. "A New Algorithm for Computing a
|
||
|
|
Single Root of a Real Continuous Function."
|
||
|
|
IEEE Trans. Circuits Systems 26, 979-980, 1979.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not isinstance(args, tuple):
|
||
|
|
args = (args,)
|
||
|
|
if xtol <= 0:
|
||
|
|
raise ValueError("xtol too small (%g <= 0)" % xtol)
|
||
|
|
if rtol < _rtol:
|
||
|
|
raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol))
|
||
|
|
r = _zeros._ridder(f,a,b,xtol,rtol,maxiter,args,full_output,disp)
|
||
|
|
return results_c(full_output, r)
|
||
|
|
|
||
|
|
|
||
|
|
def brentq(f, a, b, args=(),
|
||
|
|
xtol=_xtol, rtol=_rtol, maxiter=_iter,
|
||
|
|
full_output=False, disp=True):
|
||
|
|
"""
|
||
|
|
Find a root of a function in a bracketing interval using Brent's method.
|
||
|
|
|
||
|
|
Uses the classic Brent's method to find a zero of the function `f` on
|
||
|
|
the sign changing interval [a , b]. Generally considered the best of the
|
||
|
|
rootfinding routines here. It is a safe version of the secant method that
|
||
|
|
uses inverse quadratic extrapolation. Brent's method combines root
|
||
|
|
bracketing, interval bisection, and inverse quadratic interpolation. It is
|
||
|
|
sometimes known as the van Wijngaarden-Dekker-Brent method. Brent (1973)
|
||
|
|
claims convergence is guaranteed for functions computable within [a,b].
|
||
|
|
|
||
|
|
[Brent1973]_ provides the classic description of the algorithm. Another
|
||
|
|
description can be found in a recent edition of Numerical Recipes, including
|
||
|
|
[PressEtal1992]_. Another description is at
|
||
|
|
http://mathworld.wolfram.com/BrentsMethod.html. It should be easy to
|
||
|
|
understand the algorithm just by reading our code. Our code diverges a bit
|
||
|
|
from standard presentations: we choose a different formula for the
|
||
|
|
extrapolation step.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
f : function
|
||
|
|
Python function returning a number. The function :math:`f`
|
||
|
|
must be continuous, and :math:`f(a)` and :math:`f(b)` must
|
||
|
|
have opposite signs.
|
||
|
|
a : number
|
||
|
|
One end of the bracketing interval :math:`[a, b]`.
|
||
|
|
b : number
|
||
|
|
The other end of the bracketing interval :math:`[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. 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.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``. For nice functions, Brent's
|
||
|
|
method will often satisfy the above condition with ``xtol/2``
|
||
|
|
and ``rtol/2``. [Brent1973]_
|
||
|
|
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
|
||
|
|
--------
|
||
|
|
multivariate local optimizers
|
||
|
|
`fmin`, `fmin_powell`, `fmin_cg`, `fmin_bfgs`, `fmin_ncg`
|
||
|
|
nonlinear least squares minimizer
|
||
|
|
`leastsq`
|
||
|
|
constrained multivariate optimizers
|
||
|
|
`fmin_l_bfgs_b`, `fmin_tnc`, `fmin_cobyla`
|
||
|
|
global optimizers
|
||
|
|
`basinhopping`, `brute`, `differential_evolution`
|
||
|
|
local scalar minimizers
|
||
|
|
`fminbound`, `brent`, `golden`, `bracket`
|
||
|
|
n-dimensional root-finding
|
||
|
|
`fsolve`
|
||
|
|
one-dimensional root-finding
|
||
|
|
`brenth`, `ridder`, `bisect`, `newton`
|
||
|
|
scalar fixed-point finder
|
||
|
|
`fixed_point`
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
`f` must be continuous. f(a) and f(b) must have opposite signs.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> def f(x):
|
||
|
|
... return (x**2 - 1)
|
||
|
|
|
||
|
|
>>> from scipy import optimize
|
||
|
|
|
||
|
|
>>> root = optimize.brentq(f, -2, 0)
|
||
|
|
>>> root
|
||
|
|
-1.0
|
||
|
|
|
||
|
|
>>> root = optimize.brentq(f, 0, 2)
|
||
|
|
>>> root
|
||
|
|
1.0
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [Brent1973]
|
||
|
|
Brent, R. P.,
|
||
|
|
*Algorithms for Minimization Without Derivatives*.
|
||
|
|
Englewood Cliffs, NJ: Prentice-Hall, 1973. Ch. 3-4.
|
||
|
|
|
||
|
|
.. [PressEtal1992]
|
||
|
|
Press, W. H.; Flannery, B. P.; Teukolsky, S. A.; and Vetterling, W. T.
|
||
|
|
*Numerical Recipes in FORTRAN: The Art of Scientific Computing*, 2nd ed.
|
||
|
|
Cambridge, England: Cambridge University Press, pp. 352-355, 1992.
|
||
|
|
Section 9.3: "Van Wijngaarden-Dekker-Brent Method."
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not isinstance(args, tuple):
|
||
|
|
args = (args,)
|
||
|
|
if xtol <= 0:
|
||
|
|
raise ValueError("xtol too small (%g <= 0)" % xtol)
|
||
|
|
if rtol < _rtol:
|
||
|
|
raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol))
|
||
|
|
r = _zeros._brentq(f,a,b,xtol,rtol,maxiter,args,full_output,disp)
|
||
|
|
return results_c(full_output, r)
|
||
|
|
|
||
|
|
|
||
|
|
def brenth(f, a, b, args=(),
|
||
|
|
xtol=_xtol, rtol=_rtol, maxiter=_iter,
|
||
|
|
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.
|
||
|
|
|
||
|
|
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 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 : 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.
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
fmin, fmin_powell, fmin_cg,
|
||
|
|
fmin_bfgs, fmin_ncg : multivariate local optimizers
|
||
|
|
|
||
|
|
leastsq : nonlinear least squares minimizer
|
||
|
|
|
||
|
|
fmin_l_bfgs_b, fmin_tnc, fmin_cobyla : constrained multivariate optimizers
|
||
|
|
|
||
|
|
basinhopping, differential_evolution, brute : global optimizers
|
||
|
|
|
||
|
|
fminbound, brent, golden, bracket : local scalar minimizers
|
||
|
|
|
||
|
|
fsolve : n-dimensional root-finding
|
||
|
|
|
||
|
|
brentq, brenth, ridder, bisect, newton : one-dimensional root-finding
|
||
|
|
|
||
|
|
fixed_point : scalar fixed-point finder
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not isinstance(args, tuple):
|
||
|
|
args = (args,)
|
||
|
|
if xtol <= 0:
|
||
|
|
raise ValueError("xtol too small (%g <= 0)" % xtol)
|
||
|
|
if rtol < _rtol:
|
||
|
|
raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol))
|
||
|
|
r = _zeros._brenth(f,a, b, xtol, rtol, maxiter, args, full_output, disp)
|
||
|
|
return results_c(full_output, r)
|