Iterative Methods: Linear Systems

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Related lectures (30)

Page 3 of 3

Introduces iterative methods for linear equations, convergence criteria, gradient of quadratic forms, and classical force fields in complex atomistic systems.

Regression & Systemed Lineaires

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

Cholesky Factorization: Theory and Algorithm

Explores the Cholesky factorization method for symmetric positive definite matrices.

Iterative Solvers: Theory and Comparison

Explores solving linear systems iteratively and compares different solvers based on worst-case assumptions and convergence measures.

Preconditioned Richardson Method

Covers the Preconditioned Richardson Method for solving linear systems and the impact of preconditioning on convergence.

Linear Systems Resolution

Covers methods to solve linear systems with a finite number of operations.

Conjugate Gradient Method: Iterative Optimization

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

Conjugate Gradient Methods: Overview

Provides an overview of conjugate gradient methods, including preconditioning, nonlinear conjugate gradient, and singular value decomposition.

Richardson Method: Preconditioned Iterative Solvers

Covers the Richardson method for solving linear systems with preconditioned iterative solvers and introduces the gradient method.

Diagonalization Techniques: Jacobi Method

Covers the Jacobi method, Givens rotation, QR decomposition, and diagonalization techniques in computational physics.