Lectures related to The Simplex Algorithm

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

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

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

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

Explores the equality of row spaces for equivalent row matrices and the determination of row ranks.

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

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

Covers LU factorization, linear independence, and matrix equations.

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

Explores matrix rank and vector space inclusion through elementary row operations.

Covers LU factorization, determinants, subspaces, and matrix products.

Covers elementary row operations, row-equivalent matrices, row-echelon form, Gauss method, uniqueness of reduced form, and matrix rank.

Explains the Gaussian elimination method for solving linear equations through row operations and pivoting.

Explores forming bases, dependencies, and relationships in linear applications using invertible matrices and canonical bases.

Explores the properties and uniqueness of reduced echelon matrices and the criterion of invertibility.

Explores orthogonally diagonalizable matrices, eigenvectors, bases, and matrix properties.

Explains linear independence, basis, and matrix rank with examples and exercises.

Covers direct methods for solving linear systems in numerical analysis.

Covers the fundamentals of linear equations, matrices, and systems of linear equations, including matrix operations and solutions.

Covers matrix operations, definitions, properties, and vector operations in Rn, essential for understanding linear algebra concepts.