Lectures related to Tensor Decomposition: Jennrich's Theorem

Covers matrix eigenvalues, eigenvectors, and their linear independence.

Explores the diagonalization of symmetric matrices using eigenvectors and eigenvalues, emphasizing orthogonality and real eigenvalues.

Covers eigenvalues and eigenvectors, explaining their importance in linear algebra.

Explains how to find free variables in matrix equations and analyze characteristic polynomials.

Introduces special and general relativity, Einstein equations, and gravitational dynamics.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Covers quantum eigenfunctions and the importance of A and B commuting for the same set of eigenfunctions.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Covers tensor algebras, symmetric and exterior algebras, and tensor products of modules.