I am using scipy.optimize.minimize to find optimal parameters for my objective function.
My code :
import numpy as np
from scipy.optimize import minimize
from scipy.optimize import Bounds
bounds = Bounds([26,26,8,6,400,100,0,25,2],[36,38,28,28,1800,800,100,50,7])
energy_history = []
x_values = []
def objective(x):
x_trail = x.reshape(1,-1)
x_trail = sc_X.transform(x_trail)
y_trail = regressorSVR.predict(x_trail)
y_trail = y_trail.reshape(1,-1)
y_trail = sc_Y.inverse_transform(y_trail)
return y_trail[0]
def callback(x,y):
fobj = objective(x)
energy_history.append(fobj)
x_values.append(x)
x0 = np.array([26,28,15,7,400,377,40,43,4.3])
res = minimize(objective, x0, method='trust-constr',
options={'verbose': 1}, bounds=bounds,callback=callback)
optimal_values = res.x
energy = res.fun
With the initial values given, the minimized value(res.fun) that I get is -7.1. I am creating a list(energy_history) to see how it is reaching this value. I see some values which are less than -7.1 in that list, but still, why is -7.1 being returned as the minimal value.
There are multiple times where objective function reached a value of -21, but why is still -7 being returned as a minimum ?
from Scipy.optimize.minimize is not giving the minimum value even though it sees that value
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