Linear Algebra: Cofactors and Eigenvectors

Related lectures (29)

Page 2 of 3

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

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

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.

Explores examples of diagonalization in linear algebra, focusing on eigenvalues and eigenvectors.

Explores matrix similarity, eigenvalues, and diagonalization in linear algebra.

Explores eigenvalues and eigenvectors, demonstrating their importance in linear algebra and their application in solving systems of equations.

Covers the diagonalization of matrices with distinct eigenvalues and the importance of this process.

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Covers the diagonalization of matrices using eigenvectors and eigenvalues.

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