parameters: (x)
return_statement: return der
docstring: 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
docstring_summary: The derivative (i.e. gradient) of the Rosenbrock function.
docstring_tokens: The derivative ( i . e . gradient ) of the Rosenbrock function .
function: def 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
  
      """
      x = asarray(x)
      xm = x[1:-1]
      xm_m1 = x[:-2]
      xm_p1 = x[2:]
      der = numpy.zeros_like(x)
      der[1:-1] = (200 * (xm - xm_m1**2) -
                   400 * (xm_p1 - xm**2) * xm - 2 * (1 - xm))
      der[0] = -400 * x[0] * (x[1] - x[0]**2) - 2 * (1 - x[0])
      der[-1] = 200 * (x[-1] - x[-2]**2)
      return der
function_tokens: def rosen_der ( x ) : x = asarray ( x ) xm = x [ 1 : - 1 ] xm_m1 = x [ : - 2 ] xm_p1 = x [ 2 : ] der = numpy . zeros_like ( x ) der [ 1 : - 1 ] = ( 200 * ( xm - xm_m1 ** 2 ) - 400 * ( xm_p1 - xm ** 2 ) * xm - 2 * ( 1 - xm ) ) der [ 0 ] = - 400 * x [ 0 ] * ( x [ 1 ] - x [ 0 ] ** 2 ) - 2 * ( 1 - x [ 0 ] ) der [ - 1 ] = 200 * ( x [ - 1 ] - x [ - 2 ] ** 2 ) return der