Lectures related to Hadamard product (matrices)

Introduces key concepts in linear algebra, including matrices, operations, and numerical invariants.

Covers matricizations, ALS method, and tensor decomposition, emphasizing the alignment of fibers in matrices.

Explores the equivalence between different properties of linear transformations represented by matrices and various matrix operations.

Covers matrix operations, focusing on the product and inverse of matrices.

Explores eigenvalues, matrix trace, and similarity, highlighting their significance in matrix properties.

Explores the Eigen library for linear algebra, covering vectors, matrices, arrays, memory management, reshaping, and per-component operations.

Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Explores linear combinations, matrix-vector product, and matrix equation solutions.

Covers the composition and product of matrices, including matrix multiplication.

Covers Jacobian matrix, composite function derivative, change of variables, and integral derivative.

Covers matrix multiplication, properties, and inverses in linear algebra.

Explores linear applications, matrices, injectivity, unique solutions, and matrix operations.

Explores Theorem 7.6 on the rank of the dual map in linear applications and delves into matrices and scalar matrices.

Covers the definition of determinants using cofactors and matrix operations.

Covers eigenvalues, eigenvectors, and matrix similarity for dynamic systems.

Explores methods for solving linear systems and building solutions step by step.

Covers characteristic polynomials, similar matrices, eigenvalues, and algebraic multiplicities.

Covers the l2-embeddability theorem, isometric embeddings, positive semidefinite matrices, and eigenvalues computation.

Covers matrix notation, arithmetic, multiplication, algorithms, and complexity of matrix multiplication.