Non-Diagonalizable Case: Two Eigenvalues (Example)

Related lectures (29)

Page 2 of 3

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

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.

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

Covers the reduction of a linear transformation in a 2-dimensional space to find a simpler matrix representation.

Explores the diagonalization of linear maps by finding a basis formed by eigenvectors.

Explores eigenvalues and minimal polynomial, emphasizing their importance in linear algebra.

Explores the properties and examples of diagonalizable matrices, emphasizing the relationship between eigenvectors and eigenvalues.

Introduces eigenvalues, eigenvectors, and similar matrices, emphasizing diagonalization and geometric interpretations.

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.