EEE461 Numerical Optimization

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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)

Convex Optimization: Algorithms and Complexity (Bubeck)

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