Linear Algebra: Spectral Decomposition

Related lectures (25)

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

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

Covers eigenvalues and eigenvectors, explaining their importance in linear algebra.

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

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

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

Explores matrices, change of bases, eigenvectors, eigenvalues, and vector spaces.

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

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

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