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

Notes

rosen_der has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✓	n/a
CuPy	n/a	✓
PyTorch	✓	✓
JAX	✗	✗
Dask	✓	✓

See Support for the array API standard for more information.

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. ])