B4J Code Snippet [PyBridge] Finding local minimum of a function with SciPy

SciPy is a very powerful scientific library with a vast range of features: https://scipy.org/. This code snippet provides a tiny glimpse to this library.

Imports:
pip install scipy
pip install matplotlib

It uses the optimize module to find a local minimum of a n-dimension function.

First example:

Dim func As PyWrapper = Py.Lambda("x: (x - 3) ** 2 + 2 * x - 1") '** is the power operator: (x - 3) ^ 2 + 2 * x - 1
Dim res As PyWrapper = optimize.Run("minimize").Arg(func).Arg(Array(10)) '10 is the starting point for the optimization algorithm.
res.Print

Second example with a 2d function:

Dim func As PyWrapper = Py.Lambda("x: np.sin(x[0]) + np.cos(x[1]) + 0.1 * (x[0] ** 2 + x[1] ** 2)") 'x is a vector with 2 elements
Dim res As PyWrapper = optimize.Run("minimize").Arg(func).Arg(Array(2, 2)) '(2, 2) is the starting point.
res.Print

np is the numpy module.

Project is attached.