Lectures related to Iterative Solvers: Eigenvalue Problems

Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.

Vectorization in Python: Efficient Computation with Numpy

Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.

Eigenvalues and Eigenvectors

Explores eigenvalues, eigenvectors, and methods for solving linear systems with a focus on rounding errors and preconditioning matrices.

Jacobi and Gauss-Seidel methods

Explains the Jacobi and Gauss-Seidel methods for solving linear systems iteratively.

Calcul de valeurs propres

Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.

Characteristic Polynomials and Similar Matrices

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

Eigenvalue Problems: Methods and Applications

Explores eigenvalue problems, iterative methods, convergence properties, and applications in computational physics.

Matrix Computations: Eigenvalues and Eigenvectors

Explores the complexity of matrix computations, focusing on eigenvalues and eigenvectors of symmetric matrices and the challenges in computing them.

Eigenvalue Problems: Methods and Applications

Explores eigenvalue problems, iterative methods, and their applications in quantum physics and network analysis.

Effect of Rounding Errors in Linear Systems

Explores the effect of rounding errors in solving linear systems using the LU factorization method.

Diagonalization of Linear Transformations

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

Iterative Methods: Linear Systems

Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.

Link-Based Ranking: HITS

Covers the HITS algorithm, computing hubs and authorities, convergence, and sample results.

Matrix Eigenvalues and Eigenvectors

Covers matrix eigenvalues, eigenvectors, and their linear independence.

Convex Optimization: Notation and Matrix Norms

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Matrix Computations: Eigenvalues and Eigenvectors

Delves into the complexity of matrix computations, focusing on eigenvalues and eigenvectors, algorithms, errors, and numerical stability.

Diagonalizability of Matrices

Covers the concept of diagonalizability of matrices and explores eigenvalues and eigenvectors.

Diagonalization of Matrices

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

Diagonalization of Matrices: Theory and Examples

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

Linear Algebra: Operations and Applications

Covers linear algebra operations and applications, including matrix multiplication and eigenvalues.