Simulated annealing (dual_annealing) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the dual_annealing function from scipy.optimize.

SciPy

from scipy.optimize import [1]

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

Adual_annealing

Banneal

Cminimize

Dbasinhopping

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'anneal' which is deprecated.

Importing 'minimize' which is for other optimization methods.

Importing 'basinhopping' which is a different algorithm.

The dual_annealing function is imported from scipy.optimize to perform simulated annealing optimization.

2fill in blank

medium

Complete the code to define the objective function that returns the sum of squares of x.

SciPy

def objective(x):
    return [1]

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Asum(x)

Bsum(x**2)

Cmax(x)

Dmin(x)

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum(x) which sums elements but not squared.

Using max(x) or min(x) which do not represent sum of squares.

The objective function returns the sum of squares of the input array x, which is a common test function for optimization.

3fill in blank

hard

Fix the error in the bounds definition to correctly specify bounds for two variables between -5 and 5.

SciPy

bounds = [[1], [1]]

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

A[-5, 5]

B(5, -5)

C(-5, 5)

D[5, -5]

Attempts:

3 left

💡 Hint

Common Mistakes

Using lists instead of tuples for bounds.

Reversing the order of bounds as (5, -5).

Bounds must be tuples specifying (min, max) for each variable. Using (-5, 5) is correct.

4fill in blank

hard

Fill both blanks to run dual_annealing with the objective function and bounds, storing the result in 'result'.

SciPy

result = [1](objective, [2])

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Adual_annealing

Bobjective

Cbounds

Dminimize

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the objective function as the second argument.

Using 'minimize' instead of 'dual_annealing'.

The dual_annealing function is called with the objective function and bounds to perform optimization.

5fill in blank

hard

Fill all three blanks to print the minimum value found and the corresponding variables from the result.

SciPy

print('Minimum value:', result.[1])
print('At variables:', result.[2])
print('Number of function evaluations:', result.[3])

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

Ax

Bfun

Cnfev

Dsuccess

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing 'x' and 'fun' attributes.

Using 'success' which is a boolean, not a value.

The result.fun gives the minimum value, result.x the variables, and result.nfev the number of function evaluations.