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 - Notes - rosen_derhas 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=1and 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 - ✅ - n/a - 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. ])