import numpy as np from scipy.optimize import minimize # 1. Define the objective function to minimize def objective_function(x): # Example: A simple 2D parabola f(x, y) = (x - 3)^2 + (y + 5)^2 return (x[0] - 3)**2 + (x[1] + 5)**2 # 2. Define your starting parameters (Initial Guess) # The optimizer starts searching from x = 0.0, y = 0.0 starting_parameters = np.array([0.0, 0.0]) # 3. Run the optimization result = minimize(objective_function, x0=starting_parameters, method='Nelder-Mead') # 4. Display the results print("Success:", result.success) print("Optimized Parameters (x, y):", result.x)