EEE461 Numerical Optimization
Weekly schedule
| Week | Part | Topic |
|---|---|---|
| 1 | introduction to optimization; math review | |
| 2 | linear algebra and least squares | block matrices and norms; linear independence and rank |
| 3 | linear algebra and least squares | subspaces and linear equations; least squares method |
| 4 | linear algebra and least squares | vector derivatives and positive semidefinite matrices; orthogonality |
| 5 | linear algebra and least squares | matrix norms and the singular value decomposition; principal component analysis |
| 6 | optimization (theory) | fundamental concepts; convex sets and functions |
| 7 | optimization (theory) | convex optimization; duality |
| 8 | Midterm exam | |
| 9 | optimization (algorithms) | unconstrained optimization; gradient descent and variants |
| 10 | optimization (algorithms) | Newton-type algorithms |
| 11 | optimization (algorithms) | calculating derivatives |
| 12 | optimization (algorithms) | equality constrained optimization |
| 13 | optimization (algorithms) | inequality constrained optimization |
| 14 | optimization (applications) | electrical and electronics engineering applications: circuit design, optimal power flow, electrical power generation |
| 15 | optimization (applications) | dynamical systems, estimation, and control; machine learning |
Resources
Textbooks
Convex Optimization (Boyd&Vandenberghe)
Constrained Optimization and Lagrange Multiplier Methods (Bertsekas)
Convex Optimization Theory (Bertsekas)
Convex Optimization Algorithms (Bertsekas)
Computational Optimization Open Textbook (Cornell University)
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