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