Eigenvalues and Eigenvectors

Related lectures (28)

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

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

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

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

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

Covers the concept of polynomial characteristic of a matrix and its relation to eigenvalues.

Covers the reduction of a linear transformation in a 2-dimensional space to find a simpler matrix representation.

Explores algebraic and geometric multiplicities of eigenvalues in linear algebra.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

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