Practice Constrained Optimization (2.6) - Optimization Methods - Advance Machine Learning
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Constrained Optimization

Practice - Constrained Optimization

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the purpose of Lagrange Multipliers in optimization?

💡 Hint: Think about transforming a constrained problem.

Question 2 Easy

What does KKT stand for?

💡 Hint: Recall the two names in the acronym.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What do Lagrange Multipliers help us with?

Finding only global minima
Finding local minima/maxima under constraints
Neither

💡 Hint: Think about their utility in optimization.

Question 2

True or False: KKT conditions can be applied only to problems with equality constraints.

True
False

💡 Hint: Revisit the definition of KKT conditions.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are tasked with developing a machine learning model with a fairness constraint – ensure that the model does not discriminate based on gender. Apply KKT conditions to outline your approach.

💡 Hint: Think about how to formalize fairness in terms of a mathematical model.

Challenge 2 Hard

How would you implement Projected Gradient Descent with a budget constraint in an optimization problem? Describe the key steps.

💡 Hint: Focus on both the gradient descent step and the projection process.

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Reference links

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