Lectures related to Richardson's Methods for Linear Systems

Iterative Methods for Linear Equations

Covers iterative methods for solving linear equations and analyzing convergence, including error control and positive definite matrices.

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.

Regression & Systemed Lineaires

Covers the principles of regression and linear systems, focusing on iterative methods.

Numerical Analysis: Linear Systems

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

Linear Systems: Iterative Methods

Explores linear systems and iterative methods like gradient descent and conjugate gradient for efficient solutions.

Linear Systems: Direct Methods

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

Iterative Methods for Linear Equations

Introduces iterative methods for solving linear equations and discusses the gradient method for minimizing errors.

Cholesky Factorization: Theory and Algorithm

Explores the Cholesky factorization method for symmetric positive definite matrices.

Direct and Iterative Methods for Linear Equations

Explores direct and iterative methods for solving linear equations, emphasizing symmetric matrices and computational cost.

Effect of Rounding Errors in Linear Systems

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

Conjugate Gradient Method

Covers the Conjugate Gradient method for solving linear systems efficiently.

Linear Systems: Iterative Methods

Covers iterative methods for solving linear systems, including Jacobi and Gauss-Seidel methods.

Eigenvalues and Optimization: Numerical Analysis Techniques

Discusses eigenvalues, their calculation methods, and their applications in optimization and numerical analysis.

Linear Systems: Direct and Iterative Methods

Explores linear systems, covering direct and iterative methods for solving them with a focus on round-off errors and Richardson's algorithm.

Linear Systems Resolution

Summarizes methods for resolving linear systems, including Gaussian elimination and LU decomposition.

Direct Methods for Linear Systems of Equations

Explores direct methods for solving linear systems of equations, including Gauss elimination and LU decomposition.

Iterative Methods: Linear Systems

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

Conjugate Gradient Method: Iterative Optimization

Covers the conjugate gradient method, stopping criteria, and convergence properties in iterative optimization.

Jacobi Method: Part I

Introduces the Jacobi method for solving linear systems by iteratively updating the diagonal elements of a matrix.

Linear Systems: Diagonal and Triangular Matrices, LU Factorization

Covers linear systems, diagonal and triangular matrices, and LU factorization.