Jordan Normal Form: Theory and Applications

Related lectures (23)

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

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

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

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

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

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

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

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

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.