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EE364B: Convex Optimization II (Stanford Univ.). Taught by Professor Stephen Boyd, this course concentrates on recognizing and solving convex optimization problems that arise in engineering. Continuation of Convex Optimization I.
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Description
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. (from see.stanford.edu)
Course content
- Lecture 01 – Introduction, Subgradients Unlimited
- Lecture 02 – Subgradients (cont.) Unlimited
- Lecture 03 – Convergence Proof, Subgradient Methods, Linear Equality Constraints Unlimited
- Lecture 04 – Subgradient Method for Constrained Optimization, Convergence Unlimited
- Lecture 05 – Stochastic Programming, Localization and Cutting-Plane Methods Unlimited
- Lecture 06 – Analytic Center Cutting-Plane Method Unlimited
- Lecture 07 – ACCPM With Constraint Dropping, Ellipsoid Method Unlimited
- Lecture 08 – Recap: Ellipsoid Method, Primal Decomposition, Dual Decomposition Unlimited
- Lecture 09 – Recap: Primal Decomposition, Dual Decomposition Unlimited
- Lecture 10 – Decomposition Applications Unlimited
- Lecture 11 – Sequential Convex Programming Unlimited
- Lecture 12 – Recap: Difference Of Convex Programming, Conjugate Gradient Method Unlimited
- Lecture 13 – Recap: Conjugate Gradient Method and Krylov Subspace Unlimited
- Lecture 14 – Truncated Newton Method, L1-Norm Methods Unlimited
- Lecture 15 – L1-Norm Methods Unlimited
- Lecture 16 – Model Predictive Control Unlimited
- Lecture 17 – Stochastic Model Predictive Control, Branch and Bound Methods Unlimited
- Lecture 18 – Branch and Bound Methods Unlimited
Instructor
