Code Explanation:
1. Importing Libraries
import numpy as np
from scipy.optimize import minimize
numpy (np) → used for numerical operations (though here it’s not directly used).
scipy.optimize.minimize → function from SciPy that finds the minimum of a given function.
2. Defining the Function
f = lambda x: (x-3)**2
This defines an anonymous function (lambda function).
Input: x
Output: (x-3)²
So:
At x=3 → f(3) = 0 (minimum point).
The function is a parabola opening upwards.
3. Running the Minimization
res = minimize(f, x0=0)
minimize takes:
f → the function to minimize
x0=0 → starting guess (initial value for the search)
Here, we start from x=0.
The optimizer then iteratively moves toward the point that minimizes f(x).
Since (x-3)² is minimized at x=3, the algorithm should find x=3.
res is an OptimizeResult object containing details:
res.x → solution (minimum point)
res.fun → minimum function value
res.success → whether optimization succeeded
res.message → status
4. Printing the Result
print(round(res.x[0],2))
res.x is an array containing the optimized value of x.
res.x[0] → extracts the first element (the solution).
round(...,2) → rounds the solution to 2 decimal places.
So the output is:
3.0
Final Output
3.0
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