Reducing an Application

Related lectures (26)

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Covers matrices, linear applications, vector spaces, and bijective functions.

Explores vector space operations, linear transformations, matrix representation, and linear applications.

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

Covers translations, homotheties, and geometric transformations using vectors and matrices.

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

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

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Introduces vector definitions, displacement, addition, and applications in geometry.

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