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Description
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.
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 Curriculum
- 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
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