Determinant of a Matrix

Related lectures (27)

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

Explores the diagonalizability of matrices through eigenvectors and eigenvalues, emphasizing their importance and practical implications.

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

Explores eigenvalues, matrix trace, and similarity, highlighting their significance in matrix properties.

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

Explores eigenvalues, eigenvectors, and matrix diagonalization with examples and proofs.

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

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

Covers the diagonalization of matrices using eigenvectors and eigenvalues.

Explores the equivalence between different properties of linear transformations represented by matrices and various matrix operations.