Lecture Description
Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd continues lecturing on L1 Methods for Convex-Cardinality Problems.
Course Index
- Introduction
- Subgradients
- Subgradient Methods
- Subgradient Methods for Constrained Problems.
- Stochastic Programing and the Localization and Cutting-Plane Methods
- Analytic Center Cutting-Plane Methods
- Ellipsoid Methods
- Primal and Dual Decomposition Methods
- Primal and Dual Decomposition Methods (cont.)
- Decomposition Applications
- Sequential Convex Programming
- Conjugate Gradient Methods
- Truncated Newton Method
- L1-Norm Methods for Convex-Cardinality Problems
- L1 Methods for Convex-Cardinality Problems (cont.)
- Model Predictive Control
- Branch-and-Bound Methods
- Branch-and-Bound Methods (cont.)
Course Description
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
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