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Practice Questions
Test your understanding with targeted questions related to the topic.
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.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
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.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
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.
Question 2
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.
Challenge and get performance evaluation