Richardson Method: Preconditioned Iterative Solvers

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

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

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

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

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