Richardson's Methods for Linear Systems

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Related lectures (29)

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Linear systems resolution

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

Iterative Methods for Linear Equations

Explores iterative methods for linear equations, including Jacobi and Gauss-Seidel methods, convergence criteria, and the conjugate gradient method.

Linear Systems: Convergence and Methods

Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.

Nonlinearity: Methods and Examples

Explores nonlinearity in numerical flow simulation, covering linearization methods and practical examples.

Jacobi and Gauss-Seidel methods

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

Linear Systems: Chapters 4, 5, 6

Explores the link between linear systems and optimization through elimination and LU decomposition.

Convergence Analysis: Iterative Methods

Covers the convergence analysis of iterative methods and the conditions for convergence.

Construction d'une méthode itérative II

Covers the construction of Richardson's method for solving linear systems with reflection and residuals.

The Conjugate Gradients Method (CG)

Covers the Conjugate Gradients method for solving linear systems iteratively with quadratic convergence and emphasizes the importance of linear independence among conjugate directions.