Matrix Norms: Spectral and Frobenius

Convex Optimization: Linear Algebra Review

Provides a review of linear algebra concepts crucial for convex optimization, covering topics such as vector norms, eigenvalues, and positive semidefinite matrices.

Signal Representations

Covers the norm of a matrix, operator, singular values, and unitary matrices in linear algebra.

Mathematics of Data: Optimization Basics

Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.

Optimization Basics: Linear Algebra, Analysis, Convexity

Introduces optimization basics, covering linear algebra, analysis, and convexity principles.

Orthogonality and Least Squares Method

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.