Linear Algebra: Decomposition Theorems

Related lectures (29)

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Covers the concept of eigenvalues and eigenvectors in linear algebra, focusing on the properties of endomorphisms.

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

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.

Explores eigenvalues and eigenvectors in 3D linear algebra, covering characteristic polynomials, stability under transformations, and real roots.

Covers matrix eigenvalues, eigenvectors, and their linear independence.

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

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

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

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.