Use optimize.minimize from scipy with 2 variables and interpolated function

Question

I didn\'t find a way to perform optimize.minimize from scipy with a multidimensional function. In nearly all examples an analytical function is optimized while my function is in

Answer 1

One more possible solution (hope you get the idea):

One more function is created (f), and the minimized values are sent as arguments to this function.

from scipy.optimize import minimize

x = data.Height.values
y = data.Weight.values

def f(params):
    w0, w1 = params
    return mse(w0, w1, x, y)

optimum = minimize(f, (0,0), method = 'L-BFGS-B', bounds = ((-100, 100), (-5,5)) )
w0 = optimum.x[0]
w1 = optimum.x[1]

Also tried implementation with lambda function, but had no luck.