rosen(x)
The function computed is:
sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)
1-D array of points at which the Rosenbrock function is to be computed.
The value of the Rosenbrock function.
The Rosenbrock function.
>>> from scipy.optimize import rosen
... X = 0.1 * np.arange(10)
... rosen(X) 76.56
For higher-dimensional input rosen
broadcasts. In the following example, we use this to plot a 2D landscape. Note that rosen_hess
does not broadcast in this manner.
>>> import matplotlib.pyplot as plt
... from mpl_toolkits.mplot3d import Axes3D
... 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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.optimize._optimize.rosen
scipy.optimize._optimize.rosen_hess
scipy.optimize._optimize.rosen_der
scipy.optimize._minimize.minimize
scipy.optimize._differentialevolution.differential_evolution
scipy.optimize._optimize.rosen_hess_prod
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