Plan for the lectures
| 1. | Overview of the course, introduction to Linear Programming (LP) | Chapter 1,2 |
| 2. | The Simplex method for solving LP problems | 3, 4, 5.1, 5.2 |
| 3. | More on the Simplex method | 5 |
| 4. | Network flows and linear algebra | 7, 23-26 |
| 5. | Duality in LP, linear algebra, Lagrange relaxation | 6, 22-26, |
| 6. | LP duality and a game, Quadratic functions | 6 |
| 7. |
Duality, Convex optimization |
6, 8 |
| 8. | Quadratic optimization no constraints, positive definite matrices | 8, 9, 27 |
| 9. | Quadratic optimization with equality constraints | 10, 27 |
| 10. | Least Squares problems | 11 |
| 11. | Nonlinear optimization and Newton and Gauss-Newton methods | 8, 16-18 |
| 12. | NLP without constraints. | 8, 12-15 |
| 13. |
Equality constraints and the Lagrange conditions, Karush-Kuhn-Tucker conditions and inequality constraints |
19, 20-21 |
| 14. | Lagrange relaxation | 22 |
| 15. | Lagrange relaxation and summary of the course. | 22 |