(e.g.
    ``-myfprime(xk)``) to find a step length that satisfies the strong Wolfe
    conditions. If the search direction is not a descent direction (e.g.
    ``myfprime(xk)``), then `alpha`, `new_fval`, and `new_slope` will be None.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.optimize import line_search

    A objective function and its gradient are defined.

    >>> def obj_func(x):
    ...     return (x[0])**2+(x[1])**2
    >>> def obj_grad(x):
    ...     return [2*x[0], 2*x[1]]

    We can find alpha that satisfies strong Wolfe conditions.

    >>> start_point = np.array([1.8, 1.7])
    >>> search_gradient = np.array([-1.0, -1.0])
    >>> line_search(obj_func, obj_grad, start_point, search_gradient)
    (1.0, 2, 1, 1.1300000000000001, 6.13, [1.6, 1.4])

    r