Module « scipy.optimize »

Fonction root_scalar - module scipy.optimize

Signature de la fonction root_scalar

def root_scalar(f, args=(), method=None, bracket=None, fprime=None, fprime2=None, x0=None, x1=None, xtol=None, rtol=None, maxiter=None, options=None) 

Description

root_scalar.__doc__

    Find a root of a scalar function.

    Parameters
    ----------
    f : callable
        A function to find a root of.
    args : tuple, optional
        Extra arguments passed to the objective function and its derivative(s).
    method : str, optional
        Type of solver.  Should be one of

            - 'bisect'    :ref:`(see here) <optimize.root_scalar-bisect>`
            - 'brentq'    :ref:`(see here) <optimize.root_scalar-brentq>`
            - 'brenth'    :ref:`(see here) <optimize.root_scalar-brenth>`
            - 'ridder'    :ref:`(see here) <optimize.root_scalar-ridder>`
            - 'toms748'    :ref:`(see here) <optimize.root_scalar-toms748>`
            - 'newton'    :ref:`(see here) <optimize.root_scalar-newton>`
            - 'secant'    :ref:`(see here) <optimize.root_scalar-secant>`
            - 'halley'    :ref:`(see here) <optimize.root_scalar-halley>`

    bracket: A sequence of 2 floats, optional
        An interval bracketing a root.  `f(x, *args)` must have different
        signs at the two endpoints.
    x0 : float, optional
        Initial guess.
    x1 : float, optional
        A second guess.
    fprime : bool or callable, optional
        If `fprime` is a boolean and is True, `f` is assumed to return the
        value of the objective function and of the derivative.
        `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, optional
        If `fprime2` is a boolean and is True, `f` is assumed to return the
        value of the objective function and of the
        first and second derivatives.
        `fprime2` can also be a callable returning the second derivative of `f`.
        In this case, it must accept the same arguments as `f`.
    xtol : float, optional
        Tolerance (absolute) for termination.
    rtol : float, optional
        Tolerance (relative) for termination.
    maxiter : int, optional
        Maximum number of iterations.
    options : dict, optional
        A dictionary of solver options. E.g., ``k``, see
        :obj:`show_options()` for details.

    Returns
    -------
    sol : RootResults
        The solution represented as a ``RootResults`` object.
        Important attributes are: ``root`` the solution , ``converged`` a
        boolean flag indicating if the algorithm exited successfully and
        ``flag`` which describes the cause of the termination. See
        `RootResults` for a description of other attributes.

    See also
    --------
    show_options : Additional options accepted by the solvers
    root : Find a root of a vector function.

    Notes
    -----
    This section describes the available solvers that can be selected by the
    'method' parameter.

    The default is to use the best method available for the situation
    presented.
    If a bracket is provided, it may use one of the bracketing methods.
    If a derivative and an initial value are specified, it may
    select one of the derivative-based methods.
    If no method is judged applicable, it will raise an Exception.


    Examples
    --------

    Find the root of a simple cubic

    >>> from scipy import optimize
    >>> def f(x):
    ...     return (x**3 - 1)  # only one real root at x = 1

    >>> def fprime(x):
    ...     return 3*x**2

    The `brentq` method takes as input a bracket

    >>> sol = optimize.root_scalar(f, bracket=[0, 3], method='brentq')
    >>> sol.root, sol.iterations, sol.function_calls
    (1.0, 10, 11)

    The `newton` method takes as input a single point and uses the derivative(s)

    >>> sol = optimize.root_scalar(f, x0=0.2, fprime=fprime, method='newton')
    >>> sol.root, sol.iterations, sol.function_calls
    (1.0, 11, 22)

    The function can provide the value and derivative(s) in a single call.

    >>> def f_p_pp(x):
    ...     return (x**3 - 1), 3*x**2, 6*x

    >>> sol = optimize.root_scalar(f_p_pp, x0=0.2, fprime=True, method='newton')
    >>> sol.root, sol.iterations, sol.function_calls
    (1.0, 11, 11)

    >>> sol = optimize.root_scalar(f_p_pp, x0=0.2, fprime=True, fprime2=True, method='halley')
    >>> sol.root, sol.iterations, sol.function_calls
    (1.0, 7, 8)