462 lines
15 KiB
Python
462 lines
15 KiB
Python
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"""
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Unified interfaces to root finding algorithms for real or complex
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scalar functions.
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Functions
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---------
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- root : find a root of a scalar function.
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"""
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import numpy as np
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from . import zeros as optzeros
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__all__ = ['root_scalar']
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ROOT_SCALAR_METHODS = ['bisect', 'brentq', 'brenth', 'ridder', 'toms748',
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'newton', 'secant', 'halley']
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class MemoizeDer(object):
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"""Decorator that caches the value and derivative(s) of function each
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time it is called.
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This is a simplistic memoizer that calls and caches a single value
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of `f(x, *args)`.
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It assumes that `args` does not change between invocations.
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It supports the use case of a root-finder where `args` is fixed,
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`x` changes, and only rarely, if at all, does x assume the same value
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more than once."""
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def __init__(self, fun):
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self.fun = fun
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self.vals = None
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self.x = None
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self.n_calls = 0
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def __call__(self, x, *args):
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r"""Calculate f or use cached value if available"""
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# Derivative may be requested before the function itself, always check
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if self.vals is None or x != self.x:
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fg = self.fun(x, *args)
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self.x = x
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self.n_calls += 1
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self.vals = fg[:]
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return self.vals[0]
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def fprime(self, x, *args):
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r"""Calculate f' or use a cached value if available"""
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if self.vals is None or x != self.x:
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self(x, *args)
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return self.vals[1]
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def fprime2(self, x, *args):
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r"""Calculate f'' or use a cached value if available"""
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if self.vals is None or x != self.x:
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self(x, *args)
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return self.vals[2]
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def ncalls(self):
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return self.n_calls
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def root_scalar(f, args=(), method=None, bracket=None,
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fprime=None, fprime2=None,
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x0=None, x1=None,
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xtol=None, rtol=None, maxiter=None,
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options=None):
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"""
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Find a root of a scalar function.
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Parameters
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----------
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f : callable
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A function to find a root of.
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args : tuple, optional
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Extra arguments passed to the objective function and its derivative(s).
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method : str, optional
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Type of solver. Should be one of
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- 'bisect' :ref:`(see here) <optimize.root_scalar-bisect>`
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- 'brentq' :ref:`(see here) <optimize.root_scalar-brentq>`
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- 'brenth' :ref:`(see here) <optimize.root_scalar-brenth>`
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- 'ridder' :ref:`(see here) <optimize.root_scalar-ridder>`
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- 'toms748' :ref:`(see here) <optimize.root_scalar-toms748>`
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- 'newton' :ref:`(see here) <optimize.root_scalar-newton>`
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- 'secant' :ref:`(see here) <optimize.root_scalar-secant>`
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- 'halley' :ref:`(see here) <optimize.root_scalar-halley>`
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bracket: A sequence of 2 floats, optional
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An interval bracketing a root. `f(x, *args)` must have different
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signs at the two endpoints.
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x0 : float, optional
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Initial guess.
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x1 : float, optional
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A second guess.
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fprime : bool or callable, optional
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If `fprime` is a boolean and is True, `f` is assumed to return the
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value of the objective function and of the derivative.
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`fprime` can also be a callable returning the derivative of `f`. In
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this case, it must accept the same arguments as `f`.
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fprime2 : bool or callable, optional
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If `fprime2` is a boolean and is True, `f` is assumed to return the
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value of the objective function and of the
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first and second derivatives.
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`fprime2` can also be a callable returning the second derivative of `f`.
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In this case, it must accept the same arguments as `f`.
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xtol : float, optional
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Tolerance (absolute) for termination.
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rtol : float, optional
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Tolerance (relative) for termination.
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maxiter : int, optional
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Maximum number of iterations.
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options : dict, optional
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A dictionary of solver options. E.g., ``k``, see
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:obj:`show_options()` for details.
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Returns
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-------
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sol : RootResults
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The solution represented as a ``RootResults`` object.
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Important attributes are: ``root`` the solution , ``converged`` a
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boolean flag indicating if the algorithm exited successfully and
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``flag`` which describes the cause of the termination. See
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`RootResults` for a description of other attributes.
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See also
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--------
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show_options : Additional options accepted by the solvers
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root : Find a root of a vector function.
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Notes
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-----
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This section describes the available solvers that can be selected by the
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'method' parameter.
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The default is to use the best method available for the situation
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presented.
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If a bracket is provided, it may use one of the bracketing methods.
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If a derivative and an initial value are specified, it may
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select one of the derivative-based methods.
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If no method is judged applicable, it will raise an Exception.
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Examples
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--------
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Find the root of a simple cubic
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>>> from scipy import optimize
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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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>>> def fprime(x):
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... return 3*x**2
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The `brentq` method takes as input a bracket
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>>> sol = optimize.root_scalar(f, bracket=[0, 3], method='brentq')
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>>> sol.root, sol.iterations, sol.function_calls
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(1.0, 10, 11)
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The `newton` method takes as input a single point and uses the derivative(s)
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>>> sol = optimize.root_scalar(f, x0=0.2, fprime=fprime, method='newton')
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>>> sol.root, sol.iterations, sol.function_calls
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(1.0, 11, 22)
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The function can provide the value and derivative(s) in a single call.
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>>> def f_p_pp(x):
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... return (x**3 - 1), 3*x**2, 6*x
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>>> sol = optimize.root_scalar(f_p_pp, x0=0.2, fprime=True, method='newton')
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>>> sol.root, sol.iterations, sol.function_calls
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(1.0, 11, 11)
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>>> sol = optimize.root_scalar(f_p_pp, x0=0.2, fprime=True, fprime2=True, method='halley')
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>>> sol.root, sol.iterations, sol.function_calls
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(1.0, 7, 8)
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"""
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if not isinstance(args, tuple):
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args = (args,)
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if options is None:
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options = {}
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# fun also returns the derivative(s)
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is_memoized = False
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if fprime2 is not None and not callable(fprime2):
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if bool(fprime2):
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f = MemoizeDer(f)
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is_memoized = True
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fprime2 = f.fprime2
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fprime = f.fprime
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else:
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fprime2 = None
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if fprime is not None and not callable(fprime):
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if bool(fprime):
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f = MemoizeDer(f)
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is_memoized = True
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fprime = f.fprime
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else:
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fprime = None
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# respect solver-specific default tolerances - only pass in if actually set
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kwargs = {}
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for k in ['xtol', 'rtol', 'maxiter']:
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v = locals().get(k)
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if v is not None:
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kwargs[k] = v
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# Set any solver-specific options
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if options:
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kwargs.update(options)
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# Always request full_output from the underlying method as _root_scalar
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# always returns a RootResults object
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kwargs.update(full_output=True, disp=False)
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# Pick a method if not specified.
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# Use the "best" method available for the situation.
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if not method:
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if bracket:
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method = 'brentq'
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elif x0 is not None:
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if fprime:
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if fprime2:
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method = 'halley'
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else:
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method = 'newton'
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else:
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method = 'secant'
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if not method:
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raise ValueError('Unable to select a solver as neither bracket '
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'nor starting point provided.')
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meth = method.lower()
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map2underlying = {'halley': 'newton', 'secant': 'newton'}
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try:
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methodc = getattr(optzeros, map2underlying.get(meth, meth))
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except AttributeError as e:
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raise ValueError('Unknown solver %s' % meth) from e
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if meth in ['bisect', 'ridder', 'brentq', 'brenth', 'toms748']:
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if not isinstance(bracket, (list, tuple, np.ndarray)):
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raise ValueError('Bracket needed for %s' % method)
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a, b = bracket[:2]
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r, sol = methodc(f, a, b, args=args, **kwargs)
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elif meth in ['secant']:
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if x0 is None:
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raise ValueError('x0 must not be None for %s' % method)
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if x1 is None:
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raise ValueError('x1 must not be None for %s' % method)
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if 'xtol' in kwargs:
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kwargs['tol'] = kwargs.pop('xtol')
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r, sol = methodc(f, x0, args=args, fprime=None, fprime2=None,
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x1=x1, **kwargs)
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elif meth in ['newton']:
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if x0 is None:
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raise ValueError('x0 must not be None for %s' % method)
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if not fprime:
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raise ValueError('fprime must be specified for %s' % method)
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if 'xtol' in kwargs:
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kwargs['tol'] = kwargs.pop('xtol')
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r, sol = methodc(f, x0, args=args, fprime=fprime, fprime2=None,
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**kwargs)
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elif meth in ['halley']:
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if x0 is None:
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raise ValueError('x0 must not be None for %s' % method)
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if not fprime:
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raise ValueError('fprime must be specified for %s' % method)
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if not fprime2:
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raise ValueError('fprime2 must be specified for %s' % method)
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if 'xtol' in kwargs:
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kwargs['tol'] = kwargs.pop('xtol')
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r, sol = methodc(f, x0, args=args, fprime=fprime, fprime2=fprime2, **kwargs)
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else:
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raise ValueError('Unknown solver %s' % method)
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if is_memoized:
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# Replace the function_calls count with the memoized count.
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# Avoids double and triple-counting.
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n_calls = f.n_calls
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sol.function_calls = n_calls
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return sol
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def _root_scalar_brentq_doc():
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r"""
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Options
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-------
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args : tuple, optional
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Extra arguments passed to the objective function.
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xtol : float, optional
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Tolerance (absolute) for termination.
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rtol : float, optional
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Tolerance (relative) for termination.
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maxiter : int, optional
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Maximum number of iterations.
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options: dict, optional
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Specifies any method-specific options not covered above
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"""
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pass
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def _root_scalar_brenth_doc():
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r"""
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Options
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-------
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args : tuple, optional
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Extra arguments passed to the objective function.
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xtol : float, optional
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Tolerance (absolute) for termination.
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rtol : float, optional
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Tolerance (relative) for termination.
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maxiter : int, optional
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Maximum number of iterations.
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options: dict, optional
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Specifies any method-specific options not covered above.
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"""
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pass
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def _root_scalar_toms748_doc():
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r"""
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Options
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-------
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args : tuple, optional
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Extra arguments passed to the objective function.
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xtol : float, optional
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Tolerance (absolute) for termination.
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rtol : float, optional
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Tolerance (relative) for termination.
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maxiter : int, optional
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Maximum number of iterations.
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options: dict, optional
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Specifies any method-specific options not covered above.
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"""
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pass
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def _root_scalar_secant_doc():
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r"""
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Options
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-------
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args : tuple, optional
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Extra arguments passed to the objective function.
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xtol : float, optional
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Tolerance (absolute) for termination.
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rtol : float, optional
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Tolerance (relative) for termination.
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maxiter : int, optional
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Maximum number of iterations.
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x0 : float, required
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Initial guess.
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x1 : float, required
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A second guess.
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options: dict, optional
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Specifies any method-specific options not covered above.
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"""
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pass
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def _root_scalar_newton_doc():
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r"""
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Options
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-------
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args : tuple, optional
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Extra arguments passed to the objective function and its derivative.
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xtol : float, optional
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Tolerance (absolute) for termination.
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rtol : float, optional
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Tolerance (relative) for termination.
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maxiter : int, optional
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Maximum number of iterations.
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x0 : float, required
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Initial guess.
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fprime : bool or callable, optional
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|
|
If `fprime` is a boolean and is True, `f` is assumed to return the
|
||
|
|
value of derivative along with the objective function.
|
||
|
|
`fprime` can also be a callable returning the derivative of `f`. In
|
||
|
|
this case, it must accept the same arguments as `f`.
|
||
|
|
options: dict, optional
|
||
|
|
Specifies any method-specific options not covered above.
|
||
|
|
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def _root_scalar_halley_doc():
|
||
|
|
r"""
|
||
|
|
Options
|
||
|
|
-------
|
||
|
|
args : tuple, optional
|
||
|
|
Extra arguments passed to the objective function and its derivatives.
|
||
|
|
xtol : float, optional
|
||
|
|
Tolerance (absolute) for termination.
|
||
|
|
rtol : float, optional
|
||
|
|
Tolerance (relative) for termination.
|
||
|
|
maxiter : int, optional
|
||
|
|
Maximum number of iterations.
|
||
|
|
x0 : float, required
|
||
|
|
Initial guess.
|
||
|
|
fprime : bool or callable, required
|
||
|
|
If `fprime` is a boolean and is True, `f` is assumed to return the
|
||
|
|
value of derivative along with the objective function.
|
||
|
|
`fprime` can also be a callable returning the derivative of `f`. In
|
||
|
|
this case, it must accept the same arguments as `f`.
|
||
|
|
fprime2 : bool or callable, required
|
||
|
|
If `fprime2` is a boolean and is True, `f` is assumed to return the
|
||
|
|
value of 1st and 2nd derivatives along with the objective function.
|
||
|
|
`fprime2` can also be a callable returning the 2nd derivative of `f`.
|
||
|
|
In this case, it must accept the same arguments as `f`.
|
||
|
|
options: dict, optional
|
||
|
|
Specifies any method-specific options not covered above.
|
||
|
|
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def _root_scalar_ridder_doc():
|
||
|
|
r"""
|
||
|
|
Options
|
||
|
|
-------
|
||
|
|
args : tuple, optional
|
||
|
|
Extra arguments passed to the objective function.
|
||
|
|
xtol : float, optional
|
||
|
|
Tolerance (absolute) for termination.
|
||
|
|
rtol : float, optional
|
||
|
|
Tolerance (relative) for termination.
|
||
|
|
maxiter : int, optional
|
||
|
|
Maximum number of iterations.
|
||
|
|
options: dict, optional
|
||
|
|
Specifies any method-specific options not covered above.
|
||
|
|
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def _root_scalar_bisect_doc():
|
||
|
|
r"""
|
||
|
|
Options
|
||
|
|
-------
|
||
|
|
args : tuple, optional
|
||
|
|
Extra arguments passed to the objective function.
|
||
|
|
xtol : float, optional
|
||
|
|
Tolerance (absolute) for termination.
|
||
|
|
rtol : float, optional
|
||
|
|
Tolerance (relative) for termination.
|
||
|
|
maxiter : int, optional
|
||
|
|
Maximum number of iterations.
|
||
|
|
options: dict, optional
|
||
|
|
Specifies any method-specific options not covered above.
|
||
|
|
|
||
|
|
"""
|
||
|
|
pass
|