scipy.optimize.

rosen#

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

The Rosenbrock function.

The function computed is:

sum(100.0*(x[1:]-x[:-1]**2.0)**2.0+(1-x[:-1])**2.0)
Parameters:
xarray_like

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

Returns:
ffloat

The value of the Rosenbrock function.

Notes

Array API Standard Support

rosen has experimental support for Python Array API Standard compatiblebackends in addition to NumPy. Please consider testing these featuresby setting an environment variableSCIPY_ARRAY_API=1 and providingCuPy, PyTorch, JAX, or Dask arrays as array arguments. The followingcombinations of backend and device (or other capability) are supported.

Library

CPU

GPU

NumPy

✅

n/a

CuPy

n/a

✅

PyTorch

✅

✅

JAX

✅

✅

Dask

✅

n/a

SeeSupport for the array API standard for more information.

Examples

>>>importnumpyasnp>>>fromscipy.optimizeimportrosen>>>X=0.1*np.arange(10)>>>rosen(X)76.56

For higher-dimensional inputrosen broadcasts.In the following example, we use this to plot a 2D landscape.Note thatrosen_hess does not broadcast in this manner.

>>>importmatplotlib.pyplotasplt>>>frommpl_toolkits.mplot3dimportAxes3D>>>x=np.linspace(-1,1,50)>>>X,Y=np.meshgrid(x,x)>>>ax=plt.subplot(111,projection='3d')>>>ax.plot_surface(X,Y,rosen([X,Y]))>>>plt.show()
../../_images/scipy-optimize-rosen-1.png
On this page