This is a graduate level introductory course focusing on optimization fundamentals and their practical applications.

Slides (Spring 2025)

These slides keep being updated through the course.

• Introduction

• 1. Vectors and matrices

• 2. Algebra background

• 3. Derivatives

• 4. Nonlinear equations

• 5. Optimization problems

• 6. Unconstrained optimization

• 7. Least squares

• 8. Constrained optimization

• 9. Convex optimization

• 10. Special convex optimization problems

• 11. Duality

• 12. Algorithms for constrained optimization

• 13. Neural networks