Diagonalizability of Matrices: Examples and Proofs

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Diagonalization of Matrices and Least Squares

Explores diagonalization of matrices, similarity relations, and eigenvectors in linear algebra.

Decomposition Spectral: Symmetric Matrices

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Diagonalization of Symmetric Matrices

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

Spectral Decomposition of Symmetric Matrices

Explores the spectral decomposition of symmetric matrices, including diagonalization and orthogonal basis change matrices.

Linear Algebra: Canonical Basis

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

Diagonalization of Matrices

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

Spectral Decomposition

Explores spectral and singular value decompositions of matrices.

Diagonalizability of Matrices

Explores the diagonalizability of matrices based on eigenvalue multiplicities and eigenvectors.

Symmetric Matrices and Eigenvectors

Covers the concept of symmetric matrices, orthogonal bases, and eigenvectors.

Linear applications and eigenvalues

Covers the representation of linear applications through matrices, diagonalizable matrices, bases, dot product, orthogonality, and orthogonal vectors.