Polynomial Characteristic: Definition, Properties, and Applications

Related lectures (30)

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

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.

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

Covers eigenvalues, eigenvectors, characteristic polynomial, and geometric multiplicities in linear transformations.

Explores spectral and singular value decompositions of matrices.

Explores eigenvalues, eigenvectors, and methods for solving linear systems with a focus on rounding errors and preconditioning matrices.

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

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

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

Showcases a non-diagonalizable matrix example and explores eigenvalues and eigenvectors.