Matrix Representations of Linear Applications

Related lectures (25)

Page 2 of 3

Covers matrix rank, determinant, and decomposition in linear algebra.

Introduces key concepts in linear algebra, including matrices, operations, and numerical invariants.

Explains row-echelon and reduced row-echelon matrices and their role in simplifying system resolution.

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

Explores the properties of invertible matrices, including unique solutions and linear independence.

Covers LU factorization, linear independence, and matrix equations.

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

Covers elementary row operations, equivalent matrices, invertibility, and bases in linear algebra.

Covers the concept of basis, linear transformations, matrices, inverses, determinants, and bijective transformations.

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