Practice Convex And Non-convex Optimization (2.2) - Optimization Methods
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Convex and Non-Convex Optimization

Practice - Convex and Non-Convex Optimization

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define a convex function.

💡 Hint: Think about the properties of the graph of a function.

Question 2 Easy

What does a local minimum mean?

💡 Hint: Consider how it compares to the rest of the function.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is a characteristic of a convex function?

It has multiple local minima
A line segment between any two points lies below the graph
A line segment between any two points lies above or on the graph

💡 Hint: Think about the direction of the line segment when plotted.

Question 2

True or False: Non-convex functions guarantee a global minimum.

True
False

💡 Hint: Remember the definition of non-convexity.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Suppose you have a neural network with a complex loss surface. Discuss strategies to mitigate the problem of getting stuck in local minima.

💡 Hint: Consider recent advancements in optimization algorithms.

Challenge 2 Hard

Given a convex function, describe the steps you would take to optimize it using Gradient Descent.

💡 Hint: Think about the iterative process and key settings involved.

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Reference links

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