Eigenvalues and Eigenvectors of Matrices

Related lectures (29)

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

Covers the diagonalization of matrices with distinct eigenvalues and the importance of this process.

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

Explores matrix representation of linear applications, emphasizing eigenvalues and bases.

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.

Covers the criteria for diagonalizing a matrix and provides illustrative examples.

Covers the second criterion of diagonalization and similar matrices in linear applications.

Introduces eigenvalues, eigenvectors, and similar matrices, emphasizing diagonalization and geometric interpretations.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.