Provides a review of linear algebra concepts crucial for convex optimization, covering topics such as vector norms, eigenvalues, and positive semidefinite matrices.
Explores the applications and theorems of Singular Value Decomposition in linear algebra, including image processing, matrix rotation, and transition probabilities.
