Nonlinear constraint optimization in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function for nonlinear optimization from scipy.

SciPy

from scipy.optimize import [1]

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

Aminimize

Blinprog

Ccurve_fit

Droot

Attempts:

3 left

💡 Hint

Common Mistakes

Using linprog which is for linear programming.

Using curve_fit which is for curve fitting.

Using root which is for finding roots of equations.

The minimize function from scipy.optimize is used for nonlinear optimization problems.

2fill in blank

medium

Complete the code to define the nonlinear constraint function that must be greater than or equal to zero.

SciPy

def constraint(x):
    return x[0]**2 + x[1]**2 - [1]

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A1

B0

C-1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 which would change the constraint meaning.

Using negative values which are not correct for this constraint.

Using 2 which changes the radius.

The constraint x[0]**2 + x[1]**2 - 1 >= 0 means the point lies outside or on the unit circle.

3fill in blank

hard

Fix the error in the constraint dictionary by completing the 'type' field correctly.

SciPy

nonlinear_constraint = {'type': '[1]', 'fun': constraint}

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Amax

Beq

Cmin

Dineq

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'eq' which means equality constraint.

Using 'min' or 'max' which are not valid types.

The 'ineq' type means the constraint function should be >= 0, which is correct for nonlinear inequality constraints.

4fill in blank

hard

Fill both blanks to complete the call to minimize with the objective function and constraints.

SciPy

result = minimize([1], x0, constraints=[2])

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Aobjective

Bx0

C[nonlinear_constraint]

Dconstraints

Attempts:

3 left

💡 Hint

Common Mistakes

Passing x0 as the first argument instead of the objective function.

Passing constraints directly without a list.

The minimize function requires the objective function as the first argument and a list of constraints as the constraints parameter.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps indices to their optimized values if the value is greater than zero.

SciPy

positive_vars = { [1]: result.x[[2]] for [3] in range(len(result.x)) if result.x[[3]] > 0 }

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A{var

Bi

Cvar

D}

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string like '{var' for the key.

Using incorrect loop variable names.

Not filtering values greater than zero.

This dictionary comprehension creates a dictionary with keys as indices and values as their positive optimized values.