Lectures related to Singular Value Decomposition: Theory and Applications

Explores matrix decompositions for solving linear systems and simulating dynamics.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.