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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