1372 lines
49 KiB
Python
1372 lines
49 KiB
Python
import warnings
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from collections import namedtuple
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import operator
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from . import _zeros
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import numpy as np
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_iter = 100
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_xtol = 2e-12
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_rtol = 4 * np.finfo(float).eps
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__all__ = ['newton', 'bisect', 'ridder', 'brentq', 'brenth', 'toms748',
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'RootResults']
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# Must agree with CONVERGED, SIGNERR, CONVERR, ... in zeros.h
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_ECONVERGED = 0
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_ESIGNERR = -1
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_ECONVERR = -2
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_EVALUEERR = -3
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_EINPROGRESS = 1
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CONVERGED = 'converged'
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SIGNERR = 'sign error'
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CONVERR = 'convergence error'
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VALUEERR = 'value error'
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INPROGRESS = 'No error'
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flag_map = {_ECONVERGED: CONVERGED, _ESIGNERR: SIGNERR, _ECONVERR: CONVERR,
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_EVALUEERR: VALUEERR, _EINPROGRESS: INPROGRESS}
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class RootResults(object):
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"""Represents the root finding result.
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Attributes
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----------
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root : float
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Estimated root location.
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iterations : int
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Number of iterations needed to find the root.
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function_calls : int
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Number of times the function was called.
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converged : bool
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True if the routine converged.
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flag : str
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Description of the cause of termination.
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"""
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def __init__(self, root, iterations, function_calls, flag):
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self.root = root
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self.iterations = iterations
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self.function_calls = function_calls
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self.converged = flag == _ECONVERGED
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self.flag = None
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try:
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self.flag = flag_map[flag]
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except KeyError:
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self.flag = 'unknown error %d' % (flag,)
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def __repr__(self):
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attrs = ['converged', 'flag', 'function_calls',
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'iterations', 'root']
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m = max(map(len, attrs)) + 1
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return '\n'.join([a.rjust(m) + ': ' + repr(getattr(self, a))
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for a in attrs])
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def results_c(full_output, r):
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if full_output:
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x, funcalls, iterations, flag = r
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results = RootResults(root=x,
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iterations=iterations,
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function_calls=funcalls,
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flag=flag)
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return x, results
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else:
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return r
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def _results_select(full_output, r):
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"""Select from a tuple of (root, funccalls, iterations, flag)"""
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x, funcalls, iterations, flag = r
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if full_output:
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results = RootResults(root=x,
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iterations=iterations,
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function_calls=funcalls,
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flag=flag)
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return x, results
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return x
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def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50,
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fprime2=None, x1=None, rtol=0.0,
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full_output=False, disp=True):
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"""
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Find a zero of a real or complex function using the Newton-Raphson
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(or secant or Halley's) method.
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Find a zero of the function `func` given a nearby starting point `x0`.
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The Newton-Raphson method is used if the derivative `fprime` of `func`
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is provided, otherwise the secant method is used. If the second order
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derivative `fprime2` of `func` is also provided, then Halley's method is
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used.
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If `x0` is a sequence with more than one item, then `newton` returns an
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array, and `func` must be vectorized and return a sequence or array of the
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same shape as its first argument. If `fprime` or `fprime2` is given, then
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its return must also have the same shape.
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Parameters
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----------
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func : callable
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The function whose zero is wanted. It must be a function of a
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single variable of the form ``f(x,a,b,c...)``, where ``a,b,c...``
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are extra arguments that can be passed in the `args` parameter.
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x0 : float, sequence, or ndarray
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An initial estimate of the zero that should be somewhere near the
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actual zero. If not scalar, then `func` must be vectorized and return
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a sequence or array of the same shape as its first argument.
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fprime : callable, optional
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The derivative of the function when available and convenient. If it
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is None (default), then the secant method is used.
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args : tuple, optional
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Extra arguments to be used in the function call.
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tol : float, optional
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The allowable error of the zero value. If `func` is complex-valued,
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a larger `tol` is recommended as both the real and imaginary parts
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of `x` contribute to ``|x - x0|``.
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maxiter : int, optional
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Maximum number of iterations.
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fprime2 : callable, optional
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The second order derivative of the function when available and
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convenient. If it is None (default), then the normal Newton-Raphson
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or the secant method is used. If it is not None, then Halley's method
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is used.
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x1 : float, optional
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Another estimate of the zero that should be somewhere near the
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actual zero. Used if `fprime` is not provided.
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rtol : float, optional
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Tolerance (relative) for termination.
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full_output : bool, optional
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If `full_output` is False (default), the root is returned.
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If True and `x0` is scalar, the return value is ``(x, r)``, where ``x``
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is the root and ``r`` is a `RootResults` object.
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If True and `x0` is non-scalar, the return value is ``(x, converged,
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zero_der)`` (see Returns section for details).
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disp : bool, optional
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If True, raise a RuntimeError if the algorithm didn't converge, with
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the error message containing the number of iterations and current
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function value. Otherwise, the convergence status is recorded in a
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`RootResults` return object.
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Ignored if `x0` is not scalar.
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*Note: this has little to do with displaying, however,
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the `disp` keyword cannot be renamed for backwards compatibility.*
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Returns
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-------
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root : float, sequence, or ndarray
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Estimated location where function is zero.
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r : `RootResults`, optional
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Present if ``full_output=True`` and `x0` is scalar.
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Object containing information about the convergence. In particular,
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``r.converged`` is True if the routine converged.
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converged : ndarray of bool, optional
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Present if ``full_output=True`` and `x0` is non-scalar.
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For vector functions, indicates which elements converged successfully.
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zero_der : ndarray of bool, optional
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Present if ``full_output=True`` and `x0` is non-scalar.
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For vector functions, indicates which elements had a zero derivative.
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See Also
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--------
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brentq, brenth, ridder, bisect
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fsolve : find zeros in N dimensions.
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Notes
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-----
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The convergence rate of the Newton-Raphson method is quadratic,
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the Halley method is cubic, and the secant method is
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sub-quadratic. This means that if the function is well-behaved
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the actual error in the estimated zero after the nth iteration
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is approximately the square (cube for Halley) of the error
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after the (n-1)th step. However, the stopping criterion used
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here is the step size and there is no guarantee that a zero
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has been found. Consequently, the result should be verified.
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Safer algorithms are brentq, brenth, ridder, and bisect,
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but they all require that the root first be bracketed in an
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interval where the function changes sign. The brentq algorithm
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is recommended for general use in one dimensional problems
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when such an interval has been found.
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When `newton` is used with arrays, it is best suited for the following
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types of problems:
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* The initial guesses, `x0`, are all relatively the same distance from
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the roots.
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* Some or all of the extra arguments, `args`, are also arrays so that a
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class of similar problems can be solved together.
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* The size of the initial guesses, `x0`, is larger than O(100) elements.
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Otherwise, a naive loop may perform as well or better than a vector.
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Examples
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--------
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>>> from scipy import optimize
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>>> import matplotlib.pyplot as plt
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>>> def f(x):
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... return (x**3 - 1) # only one real root at x = 1
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``fprime`` is not provided, use the secant method:
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>>> root = optimize.newton(f, 1.5)
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>>> root
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1.0000000000000016
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>>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x)
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>>> root
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1.0000000000000016
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Only ``fprime`` is provided, use the Newton-Raphson method:
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>>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2)
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>>> root
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1.0
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Both ``fprime2`` and ``fprime`` are provided, use Halley's method:
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>>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2,
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... fprime2=lambda x: 6 * x)
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>>> root
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1.0
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When we want to find zeros for a set of related starting values and/or
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function parameters, we can provide both of those as an array of inputs:
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>>> f = lambda x, a: x**3 - a
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>>> fder = lambda x, a: 3 * x**2
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>>> np.random.seed(4321)
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>>> x = np.random.randn(100)
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>>> a = np.arange(-50, 50)
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>>> vec_res = optimize.newton(f, x, fprime=fder, args=(a, ))
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The above is the equivalent of solving for each value in ``(x, a)``
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separately in a for-loop, just faster:
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>>> loop_res = [optimize.newton(f, x0, fprime=fder, args=(a0,))
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... for x0, a0 in zip(x, a)]
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>>> np.allclose(vec_res, loop_res)
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True
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Plot the results found for all values of ``a``:
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>>> analytical_result = np.sign(a) * np.abs(a)**(1/3)
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>>> fig = plt.figure()
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>>> ax = fig.add_subplot(111)
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>>> ax.plot(a, analytical_result, 'o')
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>>> ax.plot(a, vec_res, '.')
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>>> ax.set_xlabel('$a$')
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>>> ax.set_ylabel('$x$ where $f(x, a)=0$')
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>>> plt.show()
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"""
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if tol <= 0:
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raise ValueError("tol too small (%g <= 0)" % tol)
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maxiter = operator.index(maxiter)
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if maxiter < 1:
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raise ValueError("maxiter must be greater than 0")
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if np.size(x0) > 1:
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return _array_newton(func, x0, fprime, args, tol, maxiter, fprime2,
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full_output)
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# Convert to float (don't use float(x0); this works also for complex x0)
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p0 = 1.0 * x0
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funcalls = 0
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if fprime is not None:
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# Newton-Raphson method
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for itr in range(maxiter):
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# first evaluate fval
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fval = func(p0, *args)
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funcalls += 1
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# If fval is 0, a root has been found, then terminate
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if fval == 0:
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return _results_select(
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full_output, (p0, funcalls, itr, _ECONVERGED))
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fder = fprime(p0, *args)
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funcalls += 1
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if fder == 0:
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msg = "Derivative was zero."
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if disp:
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msg += (
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" Failed to converge after %d iterations, value is %s."
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% (itr + 1, p0))
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raise RuntimeError(msg)
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warnings.warn(msg, RuntimeWarning)
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return _results_select(
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full_output, (p0, funcalls, itr + 1, _ECONVERR))
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newton_step = fval / fder
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if fprime2:
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fder2 = fprime2(p0, *args)
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funcalls += 1
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# Halley's method:
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# newton_step /= (1.0 - 0.5 * newton_step * fder2 / fder)
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# Only do it if denominator stays close enough to 1
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# Rationale: If 1-adj < 0, then Halley sends x in the
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# opposite direction to Newton. Doesn't happen if x is close
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# enough to root.
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adj = newton_step * fder2 / fder / 2
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if np.abs(adj) < 1:
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newton_step /= 1.0 - adj
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p = p0 - newton_step
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if np.isclose(p, p0, rtol=rtol, atol=tol):
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return _results_select(
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full_output, (p, funcalls, itr + 1, _ECONVERGED))
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p0 = p
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else:
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# Secant method
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if x1 is not None:
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if x1 == x0:
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raise ValueError("x1 and x0 must be different")
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p1 = x1
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else:
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eps = 1e-4
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p1 = x0 * (1 + eps)
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p1 += (eps if p1 >= 0 else -eps)
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q0 = func(p0, *args)
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funcalls += 1
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q1 = func(p1, *args)
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funcalls += 1
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if abs(q1) < abs(q0):
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p0, p1, q0, q1 = p1, p0, q1, q0
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for itr in range(maxiter):
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if q1 == q0:
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if p1 != p0:
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msg = "Tolerance of %s reached." % (p1 - p0)
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if disp:
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msg += (
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" Failed to converge after %d iterations, value is %s."
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% (itr + 1, p1))
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raise RuntimeError(msg)
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warnings.warn(msg, RuntimeWarning)
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p = (p1 + p0) / 2.0
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return _results_select(
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full_output, (p, funcalls, itr + 1, _ECONVERGED))
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else:
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if abs(q1) > abs(q0):
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p = (-q0 / q1 * p1 + p0) / (1 - q0 / q1)
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else:
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p = (-q1 / q0 * p0 + p1) / (1 - q1 / q0)
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if np.isclose(p, p1, rtol=rtol, atol=tol):
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return _results_select(
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full_output, (p, funcalls, itr + 1, _ECONVERGED))
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p0, q0 = p1, q1
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p1 = p
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q1 = func(p1, *args)
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funcalls += 1
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if disp:
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msg = ("Failed to converge after %d iterations, value is %s."
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% (itr + 1, p))
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raise RuntimeError(msg)
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return _results_select(full_output, (p, funcalls, itr + 1, _ECONVERR))
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def _array_newton(func, x0, fprime, args, tol, maxiter, fprime2, full_output):
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"""
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A vectorized version of Newton, Halley, and secant methods for arrays.
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Do not use this method directly. This method is called from `newton`
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when ``np.size(x0) > 1`` is ``True``. For docstring, see `newton`.
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"""
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# Explicitly copy `x0` as `p` will be modified inplace, but the
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# user's array should not be altered.
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p = np.array(x0, copy=True)
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failures = np.ones_like(p, dtype=bool)
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nz_der = np.ones_like(failures)
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if fprime is not None:
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# Newton-Raphson method
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for iteration in range(maxiter):
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# first evaluate fval
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fval = np.asarray(func(p, *args))
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# If all fval are 0, all roots have been found, then terminate
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if not fval.any():
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failures = fval.astype(bool)
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break
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fder = np.asarray(fprime(p, *args))
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nz_der = (fder != 0)
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# stop iterating if all derivatives are zero
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if not nz_der.any():
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break
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# Newton step
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dp = fval[nz_der] / fder[nz_der]
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if fprime2 is not None:
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fder2 = np.asarray(fprime2(p, *args))
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dp = dp / (1.0 - 0.5 * dp * fder2[nz_der] / fder[nz_der])
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# only update nonzero derivatives
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p = np.asarray(p, dtype=np.result_type(p, dp, np.float64))
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p[nz_der] -= dp
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failures[nz_der] = np.abs(dp) >= tol # items not yet converged
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# stop iterating if there aren't any failures, not incl zero der
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if not failures[nz_der].any():
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break
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else:
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# Secant method
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dx = np.finfo(float).eps**0.33
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p1 = p * (1 + dx) + np.where(p >= 0, dx, -dx)
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q0 = np.asarray(func(p, *args))
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q1 = np.asarray(func(p1, *args))
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active = np.ones_like(p, dtype=bool)
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for iteration in range(maxiter):
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nz_der = (q1 != q0)
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# stop iterating if all derivatives are zero
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if not nz_der.any():
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p = (p1 + p) / 2.0
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break
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# Secant Step
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dp = (q1 * (p1 - p))[nz_der] / (q1 - q0)[nz_der]
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# only update nonzero derivatives
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p = np.asarray(p, dtype=np.result_type(p, p1, dp, np.float64))
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p[nz_der] = p1[nz_der] - dp
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active_zero_der = ~nz_der & active
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p[active_zero_der] = (p1 + p)[active_zero_der] / 2.0
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active &= nz_der # don't assign zero derivatives again
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failures[nz_der] = np.abs(dp) >= tol # not yet converged
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# stop iterating if there aren't any failures, not incl zero der
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if not failures[nz_der].any():
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break
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p1, p = p, p1
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q0 = q1
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q1 = np.asarray(func(p1, *args))
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zero_der = ~nz_der & failures # don't include converged with zero-ders
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if zero_der.any():
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# Secant warnings
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if fprime is None:
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nonzero_dp = (p1 != p)
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# non-zero dp, but infinite newton step
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zero_der_nz_dp = (zero_der & nonzero_dp)
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if zero_der_nz_dp.any():
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rms = np.sqrt(
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sum((p1[zero_der_nz_dp] - p[zero_der_nz_dp]) ** 2)
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)
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warnings.warn(
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'RMS of {:g} reached'.format(rms), RuntimeWarning)
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# Newton or Halley warnings
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else:
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all_or_some = 'all' if zero_der.all() else 'some'
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msg = '{:s} derivatives were zero'.format(all_or_some)
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warnings.warn(msg, RuntimeWarning)
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elif failures.any():
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all_or_some = 'all' if failures.all() else 'some'
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msg = '{0:s} failed to converge after {1:d} iterations'.format(
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all_or_some, maxiter
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)
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if failures.all():
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raise RuntimeError(msg)
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warnings.warn(msg, RuntimeWarning)
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if full_output:
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result = namedtuple('result', ('root', 'converged', 'zero_der'))
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p = result(p, ~failures, zero_der)
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return p
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|
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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 using bisection.
|
|
|
|
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 : 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.
|
|
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 : 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 a `RootResults`
|
|
return object.
|
|
|
|
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,)
|
|
maxiter = operator.index(maxiter)
|
|
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 using Ridder's method.
|
|
|
|
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.
|
|
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 : 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.
|
|
|
|
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 : 1-D 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.
|
|
|
|
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.
|
|
|
|
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
|
|
"""
|
|
if not isinstance(args, tuple):
|
|
args = (args,)
|
|
maxiter = operator.index(maxiter)
|
|
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]_. A third 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 : scalar
|
|
One end of the bracketing interval :math:`[a, b]`.
|
|
b : scalar
|
|
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 : 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.
|
|
|
|
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.
|
|
|
|
Notes
|
|
-----
|
|
`f` must be continuous. f(a) and f(b) must have opposite signs.
|
|
|
|
Related functions fall into several classes:
|
|
|
|
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-D root-finding
|
|
`fsolve`
|
|
1-D root-finding
|
|
`brenth`, `ridder`, `bisect`, `newton`
|
|
scalar fixed-point finder
|
|
`fixed_point`
|
|
|
|
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."
|
|
|
|
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
|
|
"""
|
|
if not isinstance(args, tuple):
|
|
args = (args,)
|
|
maxiter = operator.index(maxiter)
|
|
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 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.
|
|
|
|
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-D root-finding
|
|
|
|
brentq, brenth, ridder, bisect, newton : 1-D root-finding
|
|
|
|
fixed_point : scalar fixed-point finder
|
|
|
|
"""
|
|
if not isinstance(args, tuple):
|
|
args = (args,)
|
|
maxiter = operator.index(maxiter)
|
|
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)
|
|
|
|
|
|
################################
|
|
# TOMS "Algorithm 748: Enclosing Zeros of Continuous Functions", by
|
|
# Alefeld, G. E. and Potra, F. A. and Shi, Yixun,
|
|
# See [1]
|
|
|
|
|
|
def _within_tolerance(x, y, rtol, atol):
|
|
diff = np.abs(x - y)
|
|
z = np.abs(y)
|
|
result = (diff <= (atol + rtol * z))
|
|
return result
|
|
|
|
|
|
def _notclose(fs, rtol=_rtol, atol=_xtol):
|
|
# Ensure not None, not 0, all finite, and not very close to each other
|
|
notclosefvals = (
|
|
all(fs) and all(np.isfinite(fs)) and
|
|
not any(any(np.isclose(_f, fs[i + 1:], rtol=rtol, atol=atol))
|
|
for i, _f in enumerate(fs[:-1])))
|
|
return notclosefvals
|
|
|
|
|
|
def _secant(xvals, fvals):
|
|
"""Perform a secant step, taking a little care"""
|
|
# Secant has many "mathematically" equivalent formulations
|
|
# x2 = x0 - (x1 - x0)/(f1 - f0) * f0
|
|
# = x1 - (x1 - x0)/(f1 - f0) * f1
|
|
# = (-x1 * f0 + x0 * f1) / (f1 - f0)
|
|
# = (-f0 / f1 * x1 + x0) / (1 - f0 / f1)
|
|
# = (-f1 / f0 * x0 + x1) / (1 - f1 / f0)
|
|
x0, x1 = xvals[:2]
|
|
f0, f1 = fvals[:2]
|
|
if f0 == f1:
|
|
return np.nan
|
|
if np.abs(f1) > np.abs(f0):
|
|
x2 = (-f0 / f1 * x1 + x0) / (1 - f0 / f1)
|
|
else:
|
|
x2 = (-f1 / f0 * x0 + x1) / (1 - f1 / f0)
|
|
return x2
|
|
|
|
|
|
def _update_bracket(ab, fab, c, fc):
|
|
"""Update a bracket given (c, fc), return the discarded endpoints."""
|
|
fa, fb = fab
|
|
idx = (0 if np.sign(fa) * np.sign(fc) > 0 else 1)
|
|
rx, rfx = ab[idx], fab[idx]
|
|
fab[idx] = fc
|
|
ab[idx] = c
|
|
return rx, rfx
|
|
|
|
|
|
def _compute_divided_differences(xvals, fvals, N=None, full=True,
|
|
forward=True):
|
|
"""Return a matrix of divided differences for the xvals, fvals pairs
|
|
|
|
DD[i, j] = f[x_{i-j}, ..., x_i] for 0 <= j <= i
|
|
|
|
If full is False, just return the main diagonal(or last row):
|
|
f[a], f[a, b] and f[a, b, c].
|
|
If forward is False, return f[c], f[b, c], f[a, b, c]."""
|
|
if full:
|
|
if forward:
|
|
xvals = np.asarray(xvals)
|
|
else:
|
|
xvals = np.array(xvals)[::-1]
|
|
M = len(xvals)
|
|
N = M if N is None else min(N, M)
|
|
DD = np.zeros([M, N])
|
|
DD[:, 0] = fvals[:]
|
|
for i in range(1, N):
|
|
DD[i:, i] = (np.diff(DD[i - 1:, i - 1]) /
|
|
(xvals[i:] - xvals[:M - i]))
|
|
return DD
|
|
|
|
xvals = np.asarray(xvals)
|
|
dd = np.array(fvals)
|
|
row = np.array(fvals)
|
|
idx2Use = (0 if forward else -1)
|
|
dd[0] = fvals[idx2Use]
|
|
for i in range(1, len(xvals)):
|
|
denom = xvals[i:i + len(row) - 1] - xvals[:len(row) - 1]
|
|
row = np.diff(row)[:] / denom
|
|
dd[i] = row[idx2Use]
|
|
return dd
|
|
|
|
|
|
def _interpolated_poly(xvals, fvals, x):
|
|
"""Compute p(x) for the polynomial passing through the specified locations.
|
|
|
|
Use Neville's algorithm to compute p(x) where p is the minimal degree
|
|
polynomial passing through the points xvals, fvals"""
|
|
xvals = np.asarray(xvals)
|
|
N = len(xvals)
|
|
Q = np.zeros([N, N])
|
|
D = np.zeros([N, N])
|
|
Q[:, 0] = fvals[:]
|
|
D[:, 0] = fvals[:]
|
|
for k in range(1, N):
|
|
alpha = D[k:, k - 1] - Q[k - 1:N - 1, k - 1]
|
|
diffik = xvals[0:N - k] - xvals[k:N]
|
|
Q[k:, k] = (xvals[k:] - x) / diffik * alpha
|
|
D[k:, k] = (xvals[:N - k] - x) / diffik * alpha
|
|
# Expect Q[-1, 1:] to be small relative to Q[-1, 0] as x approaches a root
|
|
return np.sum(Q[-1, 1:]) + Q[-1, 0]
|
|
|
|
|
|
def _inverse_poly_zero(a, b, c, d, fa, fb, fc, fd):
|
|
"""Inverse cubic interpolation f-values -> x-values
|
|
|
|
Given four points (fa, a), (fb, b), (fc, c), (fd, d) with
|
|
fa, fb, fc, fd all distinct, find poly IP(y) through the 4 points
|
|
and compute x=IP(0).
|
|
"""
|
|
return _interpolated_poly([fa, fb, fc, fd], [a, b, c, d], 0)
|
|
|
|
|
|
def _newton_quadratic(ab, fab, d, fd, k):
|
|
"""Apply Newton-Raphson like steps, using divided differences to approximate f'
|
|
|
|
ab is a real interval [a, b] containing a root,
|
|
fab holds the real values of f(a), f(b)
|
|
d is a real number outside [ab, b]
|
|
k is the number of steps to apply
|
|
"""
|
|
a, b = ab
|
|
fa, fb = fab
|
|
_, B, A = _compute_divided_differences([a, b, d], [fa, fb, fd],
|
|
forward=True, full=False)
|
|
|
|
# _P is the quadratic polynomial through the 3 points
|
|
def _P(x):
|
|
# Horner evaluation of fa + B * (x - a) + A * (x - a) * (x - b)
|
|
return (A * (x - b) + B) * (x - a) + fa
|
|
|
|
if A == 0:
|
|
r = a - fa / B
|
|
else:
|
|
r = (a if np.sign(A) * np.sign(fa) > 0 else b)
|
|
# Apply k Newton-Raphson steps to _P(x), starting from x=r
|
|
for i in range(k):
|
|
r1 = r - _P(r) / (B + A * (2 * r - a - b))
|
|
if not (ab[0] < r1 < ab[1]):
|
|
if (ab[0] < r < ab[1]):
|
|
return r
|
|
r = sum(ab) / 2.0
|
|
break
|
|
r = r1
|
|
|
|
return r
|
|
|
|
|
|
class TOMS748Solver(object):
|
|
"""Solve f(x, *args) == 0 using Algorithm748 of Alefeld, Potro & Shi.
|
|
"""
|
|
_MU = 0.5
|
|
_K_MIN = 1
|
|
_K_MAX = 100 # A very high value for real usage. Expect 1, 2, maybe 3.
|
|
|
|
def __init__(self):
|
|
self.f = None
|
|
self.args = None
|
|
self.function_calls = 0
|
|
self.iterations = 0
|
|
self.k = 2
|
|
# ab=[a,b] is a global interval containing a root
|
|
self.ab = [np.nan, np.nan]
|
|
# fab is function values at a, b
|
|
self.fab = [np.nan, np.nan]
|
|
self.d = None
|
|
self.fd = None
|
|
self.e = None
|
|
self.fe = None
|
|
self.disp = False
|
|
self.xtol = _xtol
|
|
self.rtol = _rtol
|
|
self.maxiter = _iter
|
|
|
|
def configure(self, xtol, rtol, maxiter, disp, k):
|
|
self.disp = disp
|
|
self.xtol = xtol
|
|
self.rtol = rtol
|
|
self.maxiter = maxiter
|
|
# Silently replace a low value of k with 1
|
|
self.k = max(k, self._K_MIN)
|
|
# Noisily replace a high value of k with self._K_MAX
|
|
if self.k > self._K_MAX:
|
|
msg = "toms748: Overriding k: ->%d" % self._K_MAX
|
|
warnings.warn(msg, RuntimeWarning)
|
|
self.k = self._K_MAX
|
|
|
|
def _callf(self, x, error=True):
|
|
"""Call the user-supplied function, update book-keeping"""
|
|
fx = self.f(x, *self.args)
|
|
self.function_calls += 1
|
|
if not np.isfinite(fx) and error:
|
|
raise ValueError("Invalid function value: f(%f) -> %s " % (x, fx))
|
|
return fx
|
|
|
|
def get_result(self, x, flag=_ECONVERGED):
|
|
r"""Package the result and statistics into a tuple."""
|
|
return (x, self.function_calls, self.iterations, flag)
|
|
|
|
def _update_bracket(self, c, fc):
|
|
return _update_bracket(self.ab, self.fab, c, fc)
|
|
|
|
def start(self, f, a, b, args=()):
|
|
r"""Prepare for the iterations."""
|
|
self.function_calls = 0
|
|
self.iterations = 0
|
|
|
|
self.f = f
|
|
self.args = args
|
|
self.ab[:] = [a, b]
|
|
if not np.isfinite(a) or np.imag(a) != 0:
|
|
raise ValueError("Invalid x value: %s " % (a))
|
|
if not np.isfinite(b) or np.imag(b) != 0:
|
|
raise ValueError("Invalid x value: %s " % (b))
|
|
|
|
fa = self._callf(a)
|
|
if not np.isfinite(fa) or np.imag(fa) != 0:
|
|
raise ValueError("Invalid function value: f(%f) -> %s " % (a, fa))
|
|
if fa == 0:
|
|
return _ECONVERGED, a
|
|
fb = self._callf(b)
|
|
if not np.isfinite(fb) or np.imag(fb) != 0:
|
|
raise ValueError("Invalid function value: f(%f) -> %s " % (b, fb))
|
|
if fb == 0:
|
|
return _ECONVERGED, b
|
|
|
|
if np.sign(fb) * np.sign(fa) > 0:
|
|
raise ValueError("a, b must bracket a root f(%e)=%e, f(%e)=%e " %
|
|
(a, fa, b, fb))
|
|
self.fab[:] = [fa, fb]
|
|
|
|
return _EINPROGRESS, sum(self.ab) / 2.0
|
|
|
|
def get_status(self):
|
|
"""Determine the current status."""
|
|
a, b = self.ab[:2]
|
|
if _within_tolerance(a, b, self.rtol, self.xtol):
|
|
return _ECONVERGED, sum(self.ab) / 2.0
|
|
if self.iterations >= self.maxiter:
|
|
return _ECONVERR, sum(self.ab) / 2.0
|
|
return _EINPROGRESS, sum(self.ab) / 2.0
|
|
|
|
def iterate(self):
|
|
"""Perform one step in the algorithm.
|
|
|
|
Implements Algorithm 4.1(k=1) or 4.2(k=2) in [APS1995]
|
|
"""
|
|
self.iterations += 1
|
|
eps = np.finfo(float).eps
|
|
d, fd, e, fe = self.d, self.fd, self.e, self.fe
|
|
ab_width = self.ab[1] - self.ab[0] # Need the start width below
|
|
c = None
|
|
|
|
for nsteps in range(2, self.k+2):
|
|
# If the f-values are sufficiently separated, perform an inverse
|
|
# polynomial interpolation step. Otherwise, nsteps repeats of
|
|
# an approximate Newton-Raphson step.
|
|
if _notclose(self.fab + [fd, fe], rtol=0, atol=32*eps):
|
|
c0 = _inverse_poly_zero(self.ab[0], self.ab[1], d, e,
|
|
self.fab[0], self.fab[1], fd, fe)
|
|
if self.ab[0] < c0 < self.ab[1]:
|
|
c = c0
|
|
if c is None:
|
|
c = _newton_quadratic(self.ab, self.fab, d, fd, nsteps)
|
|
|
|
fc = self._callf(c)
|
|
if fc == 0:
|
|
return _ECONVERGED, c
|
|
|
|
# re-bracket
|
|
e, fe = d, fd
|
|
d, fd = self._update_bracket(c, fc)
|
|
|
|
# u is the endpoint with the smallest f-value
|
|
uix = (0 if np.abs(self.fab[0]) < np.abs(self.fab[1]) else 1)
|
|
u, fu = self.ab[uix], self.fab[uix]
|
|
|
|
_, A = _compute_divided_differences(self.ab, self.fab,
|
|
forward=(uix == 0), full=False)
|
|
c = u - 2 * fu / A
|
|
if np.abs(c - u) > 0.5 * (self.ab[1] - self.ab[0]):
|
|
c = sum(self.ab) / 2.0
|
|
else:
|
|
if np.isclose(c, u, rtol=eps, atol=0):
|
|
# c didn't change (much).
|
|
# Either because the f-values at the endpoints have vastly
|
|
# differing magnitudes, or because the root is very close to
|
|
# that endpoint
|
|
frs = np.frexp(self.fab)[1]
|
|
if frs[uix] < frs[1 - uix] - 50: # Differ by more than 2**50
|
|
c = (31 * self.ab[uix] + self.ab[1 - uix]) / 32
|
|
else:
|
|
# Make a bigger adjustment, about the
|
|
# size of the requested tolerance.
|
|
mm = (1 if uix == 0 else -1)
|
|
adj = mm * np.abs(c) * self.rtol + mm * self.xtol
|
|
c = u + adj
|
|
if not self.ab[0] < c < self.ab[1]:
|
|
c = sum(self.ab) / 2.0
|
|
|
|
fc = self._callf(c)
|
|
if fc == 0:
|
|
return _ECONVERGED, c
|
|
|
|
e, fe = d, fd
|
|
d, fd = self._update_bracket(c, fc)
|
|
|
|
# If the width of the new interval did not decrease enough, bisect
|
|
if self.ab[1] - self.ab[0] > self._MU * ab_width:
|
|
e, fe = d, fd
|
|
z = sum(self.ab) / 2.0
|
|
fz = self._callf(z)
|
|
if fz == 0:
|
|
return _ECONVERGED, z
|
|
d, fd = self._update_bracket(z, fz)
|
|
|
|
# Record d and e for next iteration
|
|
self.d, self.fd = d, fd
|
|
self.e, self.fe = e, fe
|
|
|
|
status, xn = self.get_status()
|
|
return status, xn
|
|
|
|
def solve(self, f, a, b, args=(),
|
|
xtol=_xtol, rtol=_rtol, k=2, maxiter=_iter, disp=True):
|
|
r"""Solve f(x) = 0 given an interval containing a zero."""
|
|
self.configure(xtol=xtol, rtol=rtol, maxiter=maxiter, disp=disp, k=k)
|
|
status, xn = self.start(f, a, b, args)
|
|
if status == _ECONVERGED:
|
|
return self.get_result(xn)
|
|
|
|
# The first step only has two x-values.
|
|
c = _secant(self.ab, self.fab)
|
|
if not self.ab[0] < c < self.ab[1]:
|
|
c = sum(self.ab) / 2.0
|
|
fc = self._callf(c)
|
|
if fc == 0:
|
|
return self.get_result(c)
|
|
|
|
self.d, self.fd = self._update_bracket(c, fc)
|
|
self.e, self.fe = None, None
|
|
self.iterations += 1
|
|
|
|
while True:
|
|
status, xn = self.iterate()
|
|
if status == _ECONVERGED:
|
|
return self.get_result(xn)
|
|
if status == _ECONVERR:
|
|
fmt = "Failed to converge after %d iterations, bracket is %s"
|
|
if disp:
|
|
msg = fmt % (self.iterations + 1, self.ab)
|
|
raise RuntimeError(msg)
|
|
return self.get_result(xn, _ECONVERR)
|
|
|
|
|
|
def toms748(f, a, b, args=(), k=1,
|
|
xtol=_xtol, rtol=_rtol, maxiter=_iter,
|
|
full_output=False, disp=True):
|
|
"""
|
|
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
|
|
"Newton-quadratic" steps. [APS1995].
|
|
|
|
Parameters
|
|
----------
|
|
f : function
|
|
Python function returning a scalar. The function :math:`f`
|
|
must be continuous, and :math:`f(a)` and :math:`f(b)`
|
|
have opposite signs.
|
|
a : scalar,
|
|
lower boundary of the search interval
|
|
b : scalar,
|
|
upper boundary of the search interval
|
|
args : tuple, optional
|
|
containing extra arguments for the function `f`.
|
|
`f` is called by ``f(x, *args)``.
|
|
k : int, optional
|
|
The number of Newton quadratic steps to perform each
|
|
iteration. ``k>=1``.
|
|
xtol : scalar, 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 : scalar, optional
|
|
The computed root ``x0`` will satisfy ``np.allclose(x, x0,
|
|
atol=xtol, rtol=rtol)``, where ``x`` is the exact root.
|
|
maxiter : int, optional
|
|
If convergence is not achieved in `maxiter` iterations, an error is
|
|
raised. Must be >= 0.
|
|
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 the `RootResults`
|
|
return object.
|
|
|
|
Returns
|
|
-------
|
|
x0 : float
|
|
Approximate Zero of `f`
|
|
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, ridder, bisect, newton
|
|
fsolve : find zeroes in N dimensions.
|
|
|
|
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.
|
|
For ``k=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 ``(3k-2)``, hence ``k=1`` or ``k=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
|
|
"""
|
|
if xtol <= 0:
|
|
raise ValueError("xtol too small (%g <= 0)" % xtol)
|
|
if rtol < _rtol / 4:
|
|
raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol))
|
|
maxiter = operator.index(maxiter)
|
|
if maxiter < 1:
|
|
raise ValueError("maxiter must be greater than 0")
|
|
if not np.isfinite(a):
|
|
raise ValueError("a is not finite %s" % a)
|
|
if not np.isfinite(b):
|
|
raise ValueError("b is not finite %s" % b)
|
|
if a >= b:
|
|
raise ValueError("a and b are not an interval [{}, {}]".format(a, b))
|
|
if not k >= 1:
|
|
raise ValueError("k too small (%s < 1)" % k)
|
|
|
|
if not isinstance(args, tuple):
|
|
args = (args,)
|
|
solver = TOMS748Solver()
|
|
result = solver.solve(f, a, b, args=args, k=k, xtol=xtol, rtol=rtol,
|
|
maxiter=maxiter, disp=disp)
|
|
x, function_calls, iterations, flag = result
|
|
return _results_select(full_output, (x, function_calls, iterations, flag))
|