scipy.optimize.rosen_der#

scipy.optimize.rosen_der(x)[source]#

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

Parameters:

xarray_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. ])