Lectures related to Non-Diagonalizable Case: Triple Eigenvalue

Explores injective and surjective linear transformations, kernel, image, and matrix operations.

Explores the special linear group, matrix properties, and determinant theorems.

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

Covers optimization with constraints using KKT conditions and matrix invertibility in numerical analysis.

Introduces the Simplex Method for optimal decision making in linear programming, covering basic and advanced concepts.

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

Covers the proofs related to non-diagonalizable cases and linear applications in IR³.