EE364B - Convex Optimization II
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)
| Lecture 01 - Introduction, Subgradients |
| Lecture 02 - Subgradients (cont.) |
| Lecture 03 - Convergence Proof, Subgradient Methods, Linear Equality Constraints |
| Lecture 04 - Subgradient Method for Constrained Optimization, Convergence |
| Lecture 05 - Stochastic Programming, Localization and Cutting-Plane Methods |
| Lecture 06 - Analytic Center Cutting-Plane Method, Infeasible Start Newton Method Algorithm |
| Lecture 07 - ACCPM With Constraint Dropping, Ellipsoid Method |
| Lecture 08 - Recap: Ellipsoid Method, Primal Decomposition, Dual Decomposition |
| Lecture 09 - Recap: Primal Decomposition, Dual Decomposition |
| Lecture 10 - Decomposition Applications |
| Lecture 11 - Sequential Convex Programming |
| Lecture 12 - Recap: 'Difference Of Convex' Programming, Conjugate Gradient Method, Krylov Subspace |
| Lecture 13 - Recap: Conjugate Gradient Method and Krylov Subspace, Truncated Newton Method |
| Lecture 14 - Truncated Newton Method, L1-Norm Methods |
| Lecture 15 - L1-Norm Methods |
| Lecture 16 - Model Predictive Control |
| Lecture 17 - Stochastic Model Predictive Control, Branch and Bound Methods |
| Lecture 18 - Branch and Bound Methods |
| References |
EE364B - Convex Optimization II
Instructors: Professor Stephen Boyd. Handouts. Assignments. Exams. This course concentrates on recognizing and solving convex optimization problems that arise in engineering.
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