def conjugate_gradient_nonlinear(f, x, line_search=1.0, maxiter=1000, nreset=0,
                                 tol=1e-16, beta_method='FR',
                                 callback=None):
    r"""Conjugate gradient for nonlinear problems.

    Parameters
    ----------
    f : `Functional`
        Functional with ``f.gradient``.
    x : ``op.domain`` element
        Vector to which the result is written. Its initial value is
        used as starting point of the iteration, and its values are
        updated in each iteration step.
    line_search : float or `LineSearch`, optional
        Strategy to choose the step length. If a float is given, it is used as
        a fixed step length.
    maxiter : int, optional
        Maximum number of iterations to perform.
    nreset : int, optional
        Number of times the solver should be reset. Default: no reset.
    tol : float, optional
        Tolerance that should be used to terminating the iteration.
    beta_method : {'FR', 'PR', 'HS', 'DY'}, optional
        Method to calculate ``beta`` in the iterates.

        - ``'FR'`` : Fletcher-Reeves
        - ``'PR'`` : Polak-Ribiere
        - ``'HS'`` : Hestenes-Stiefel
        - ``'DY'`` : Dai-Yuan

    callback : callable, optional
        Object executing code per iteration, e.g. plotting each iterate.

    Notes
    -----
    This is a general and optimized implementation of the nonlinear conjguate
    gradient method for solving a general unconstrained optimization problem

    .. math::
        \min f(x)

    for a differentiable functional
    :math:`f: \mathcal{X}\to \mathbb{R}` on a Hilbert space
    :math:`\mathcal{X}`. It does so by finding a zero of the gradient

    .. math::
        \nabla f: \mathcal{X} \to \mathcal{X}.

    The method is described in a
    `Wikipedia article
    <https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method>`_.

    See Also
    --------
    odl.solvers.smooth.newton.bfgs_method :
        Quasi-Newton solver for the same problem
    odl.solvers.iterative.iterative.conjugate_gradient :
        Optimized solver for least-squares problem with linear and symmetric
        operator
    odl.solvers.iterative.iterative.conjugate_gradient_normal :
        Equivalent solver but for least-squares problem with linear operator
    """
    if x not in f.domain:
        raise TypeError('`x` {!r} is not in the domain of `f` {!r}'
                        ''.format(x, f.domain))

    if not callable(line_search):
        line_search = ConstantLineSearch(line_search)

    if beta_method not in ['FR', 'PR', 'HS', 'DY']:
        raise ValueError('unknown ``beta_method``')

    for _ in range(nreset + 1):
        # First iteration is done without beta
        dx = -f.gradient(x)
        dir_derivative = -dx.inner(dx)
        if abs(dir_derivative) < tol:
            return
        a = line_search(x, dx, dir_derivative)
        x.lincomb(1, x, a, dx)  # x = x + a * dx

        s = dx  # for 'HS' and 'DY' beta methods

        for _ in range(maxiter // (nreset + 1)):
            # Compute dx as -grad f
            dx, dx_old = -f.gradient(x), dx

            # Calculate "beta"
            if beta_method == 'FR':
                beta = dx.inner(dx) / dx_old.inner(dx_old)
            elif beta_method == 'PR':
                beta = dx.inner(dx - dx_old) / dx_old.inner(dx_old)
            elif beta_method == 'HS':
                beta = - dx.inner(dx - dx_old) / s.inner(dx - dx_old)
            elif beta_method == 'DY':
                beta = - dx.inner(dx) / s.inner(dx - dx_old)
            else:
                raise RuntimeError('unknown ``beta_method``')

            # Reset beta if negative.
            beta = max(0, beta)

            # Update search direction
            s.lincomb(1, dx, beta, s)  # s = dx + beta * s

            # Find optimal step along s
            dir_derivative = -dx.inner(s)

            if abs(dir_derivative) <= tol:
                return
            a = line_search(x, s, dir_derivative)

            # Update position
            x.lincomb(1, x, a, s)  # x = x + a * s

            if callback is not None:
                callback(x)
r"""Conjugate gradient for nonlinear problems.

    Parameters
    ----------
    f : `Functional`
        Functional with ``f.gradient``.
    x : ``op.domain`` element
        Vector to which the result is written. Its initial value is
        used as starting point of the iteration, and its values are
        updated in each iteration step.
    line_search : float or `LineSearch`, optional
        Strategy to choose the step length. If a float is given, it is used as
        a fixed step length.
    maxiter : int, optional
        Maximum number of iterations to perform.
    nreset : int, optional
        Number of times the solver should be reset. Default: no reset.
    tol : float, optional
        Tolerance that should be used to terminating the iteration.
    beta_method : {'FR', 'PR', 'HS', 'DY'}, optional
        Method to calculate ``beta`` in the iterates.

        - ``'FR'`` : Fletcher-Reeves
        - ``'PR'`` : Polak-Ribiere
        - ``'HS'`` : Hestenes-Stiefel
        - ``'DY'`` : Dai-Yuan

    callback : callable, optional
        Object executing code per iteration, e.g. plotting each iterate.

    Notes
    -----
    This is a general and optimized implementation of the nonlinear conjguate
    gradient method for solving a general unconstrained optimization problem

    .. math::
        \min f(x)

    for a differentiable functional
    :math:`f: \mathcal{X}\to \mathbb{R}` on a Hilbert space
    :math:`\mathcal{X}`. It does so by finding a zero of the gradient

    .. math::
        \nabla f: \mathcal{X} \to \mathcal{X}.

    The method is described in a
    `Wikipedia article
    <https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method>`_.

    See Also
    --------
    odl.solvers.smooth.newton.bfgs_method :
        Quasi-Newton solver for the same problem
    odl.solvers.iterative.iterative.conjugate_gradient :
        Optimized solver for least-squares problem with linear and symmetric
        operator
    odl.solvers.iterative.iterative.conjugate_gradient_normal :
        Equivalent solver but for least-squares problem with linear operator