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

Slides (Spring 2025)

• Introduction

• 1. Vectors and matrices

• 2. Algebra background

• 3. Derivatives

• 4. Nonlinear equations

• 5. Optimization problems

• 6. Unconstrained optimization

• 7 Least squares

Slides (Fall 2023)

• 8. Constrained optimization

• 9. Convex optimization

• 10. Linear programs

• 11. Special classes of convex optimization

• 12. Duality

• 13. Dual based method

• 14. Neural networks