Optimization callbacks and monitoring in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a callback function that prints the current iteration number during optimization.

SciPy

def callback(xk):
    print("Iteration:", [1])

Drag options to blanks, or click blank then click option'

Axk

Biteration

Cxk[0]

Dlen(xk)

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to print a variable not defined inside the function.

Using an undefined variable like 'iteration'.

The callback function receives the current parameter vector xk. Printing xk shows the current point in the optimization, which can be used to monitor progress.

2fill in blank

medium

Complete the code to call scipy.optimize.minimize with the callback function to monitor optimization.

SciPy

from scipy.optimize import minimize

result = minimize(func, x0, method='BFGS', [1]=callback)

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Amonitor

Btrack

Cobserve

Dcallback

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect argument names like 'monitor' or 'track'.

The minimize function accepts a callback argument to specify a function called after each iteration.

3fill in blank

hard

Fix the error in the callback function to correctly count and print the iteration number.

SciPy

def callback(xk):
    callback.iteration = getattr(callback, 'iteration', 0) + [1]
    print(f"Iteration {callback.iteration}: {xk}")

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A0

B1

Ccallback.iteration

Dxk

Attempts:

3 left

💡 Hint

Common Mistakes

Adding 0 instead of 1, causing iteration count to never increase.

Incrementing the iteration count by 1 each call tracks the number of iterations correctly.

4fill in blank

hard

Fill both blanks to create a callback that stops optimization after 5 iterations.

SciPy

def callback(xk):
    callback.count = getattr(callback, 'count', 0) + [1]
    if callback.count >= [2]:
        print('Stopping optimization')
        return True

Drag options to blanks, or click blank then click option'

A1

B5

C0

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 to increment count, so count never increases.

Stopping at wrong iteration number.

The callback increments a count by 1 each call and stops when count reaches 5.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that tracks parameter norms greater than 1 during optimization.

SciPy

norms = {i: [1] for i, x in enumerate(params) if [2] > [3]

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Aabs(x)

C1

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Using raw x instead of abs(x), missing negative values.

Comparing x directly to 1 without abs.

The comprehension stores the absolute value of each parameter x if its absolute value is greater than 1.