EEE461 Optimization

2024-2025 Fall

Week	Part	Topic
1		Introduction to optimization
2	Linear algebra and least squares	Block matrices and norms
3	Linear algebra and least squares	Linear independence and rank
4	Linear algebra and least squares	Subspaces and linear equations
5	Linear algebra and least squares	Least squares method
6	Linear algebra and least squares	Vector derivatives; positive semidefinite matrices
7	Linear algebra and least squares	Orthogonality; Gram-Schmidt algorithm
8		Midterm exam
9
10	Linear algebra and least squares	Least-squares classification and cross-validation
11	Linear algebra and least squares	Matrix norms; singular value decomposition
12	Optimization - Fundamentals	Problem formulation; convex sets and functions
13	Optimization - Theory	Optimality conditions for unconstrained problems
14	Optimization - Theory	Duality; optimality conditions for constrained problems
15	Optimization - Algorithms	Gradient descent; steepest descent; Newton’s method