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4_GDA.py

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+def f(x):
+    return (x + 3)**2
+
+def df(x):
+    return 2 * (x + 3)
+
+# Step 2: Initialize parameters
+x = 2                # starting point
+learning_rate = 0.1  # step size
+epochs = 30          # number of iterations
+
+# Step 3: Gradient Descent loop
+for i in range(epochs):
+    grad = df(x)                    # compute gradient
+    x = x - learning_rate * grad    # update x
+    print(f"Iteration {i+1}: x = {x:.4f}, f(x) = {f(x):.4f}")
+
+print("\nLocal minima occurs at x =", round(x, 4))
+print("Minimum value of function =", round(f(x), 4))