Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd continues his lecture on Conjugate Gradient Methods and then starts lecturing on the Truncated Newton Method.

1. Introduction
2. Subgradients
3. Subgradient Methods
4. Subgradient Methods for Constrained Problems.
5. Stochastic Programing and the Localization and Cutting-Plane Methods
6. Analytic Center Cutting-Plane Methods
7. Ellipsoid Methods
8. Primal and Dual Decomposition Methods
9. Primal and Dual Decomposition Methods (cont.)
10. Decomposition Applications
11. Sequential Convex Programming
12. Conjugate Gradient Methods
13. Truncated Newton Method
14. L1-Norm Methods for Convex-Cardinality Problems
15. L1 Methods for Convex-Cardinality Problems (cont.)
16. Model Predictive Control
17. Branch-and-Bound Methods
18. Branch-and-Bound Methods (cont.)

Continuation of 364a. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Course requirements include a substantial project.

Tags: Math, Math Calculus