Lectures related to Linear Systems Resolution

Covers the resolution of linear systems and its link to optimization problems.

Linear Systems: LU Factorization with Pivoting

Explains the Gaussian elimination algorithm with pivoting and LU factorization for linear systems.

Solving Linear Systems: Methods and Solutions

Explores methods for solving linear systems and building solutions step by step.

Explores the Eigen library for linear algebra, covering vectors, matrices, arrays, memory management, reshaping, and per-component operations.

Solving Linear Systems with Matrices

Explains solving linear systems with matrices and conditions for infinite solutions.

Singular Value Decomposition: Applications and Interpretation

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

Linear Systems: Factorization and Cholesky

Explores linear systems, Cholesky factorization, LU factorization, and matrix precision.

Linear Systems: Direct Methods

Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.

Linear Systems Resolution: Echelon & Reduced Echelon Forms

Explains echelon and reduced echelon forms of matrices and the pivot method for solving linear systems.

Number of Solutions in Linear Systems

Explores the number of solutions in linear systems of equations and the conditions for no solution, a unique solution, or an infinite number of solutions.

Linear Equations and Matrix Calculations

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

Linear Systems Resolution

Covers the resolution of linear systems using the Gauss method to determine unique, infinite, or no solutions.

Iterative Methods: Linear Systems

Covers iterative methods for solving linear systems, emphasizing convergence analysis and well-chosen matrices.

Controllability of Linear Systems

Explores controllability in linear systems, discussing invertibility, associated matrices, and system behavior implications.

Diagonalization of Matrices: Theory and Examples

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Systems of Equations and Matrices

Covers systems of equations, matrices, solving examples, and consequences of invertible matrices.

Singular Value Decomposition (SVD)

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Linear Recurrent Sequences Systems

Covers explicating sequences using matrices and their implications, illustrated with examples.

Linear Algebra: Systems of Equations

Explores solving systems of equations using matrices and determinants.

Matrix Representations of Linear Applications

Covers the matrix representations of linear applications and the equivalence between matrices.