gdprox 0.3

gdprox，近端梯度下降算法

实现了复合目标函数的近端梯度下降算法，即形式为 f(x) + g(x) 的函数，其中 f 是一个光滑函数，而 g 是一个可能非光滑的函数，对于该函数的近端算子是已知的。

此包中的主要函数是 gdprox.fmin_cgprox。此函数与 scipy.optimize 中的函数具有相似的接口。此函数的定义如下：

def fmin_cgprox(f, fprime, g_prox, x0, rtol=1e-6,
                maxiter=1000, verbose=0, default_step_size=1.):
    """
    proximal gradient-descent solver for optimization problems of the form

                       minimize_x f(x) + g(x)

    where f is a smooth function and g is a (possibly non-smooth)
    function for which the proximal operator is known.

    Parameters
    ----------
    f : callable
        f(x) returns the value of f at x.

    f_prime : callable
        f_prime(x) returns the gradient of f.

    g_prox : callable of the form g_prox(x, alpha)
        g_prox(x, alpha) returns the proximal operator of g at x
        with parameter alpha.

    x0 : array-like
        Initial guess

    maxiter : int
        Maximum number of iterations.

    verbose : int
        Verbosity level, from 0 (no output) to 2 (output on each iteration)

    default_step_size : float
        Starting value for the line-search procedure.

    Returns
    -------
    res : OptimizeResult
        The optimization result represented as a
        ``scipy.optimize.OptimizeResult`` object. Important attributes are:
        ``x`` the solution array, ``success`` a Boolean flag indicating if
        the optimizer exited successfully and ``message`` which describes
        the cause of the termination. See `scipy.optimize.OptimizeResult`
        for a description of other attributes.
    """