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scipy.optimize.

# rosen_der

scipy.optimize.rosen_der(x)

  The derivative (i.e. gradient) of the Rosenbrock function.

  Parameters:
    x array_like

      1-D array of points at which the derivative is to be computed.

  Returns:
    rosen_der(N,) ndarray

      The gradient of the Rosenbrock function at x.

  See also

  `rosen`, `rosen_hess`, `rosen_hess_prod`

  Examples

      >>> import numpy as np
      >>> from scipy.optimize import rosen_der
      >>> X = 0.1 * np.arange(9)
      >>> rosen_der(X)
      array([ -2. ,  10.6,  15.6,  13.4,   6.4,  -3. , -12.4, -19.4,  62. ])