Linear Systems: Iterative Methods

Related lectures (30)

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

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

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

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

Introduces the Jacobi method for solving linear systems and determining convergence.

Explores the Newton method for non-linear equations, discussing convergence criteria and stopping conditions.

Covers iterative methods for solving linear systems of equations and discusses the convergence properties of methods like Richardson's method.

Covers the Gauss-Seidel method for solving linear systems through progressive substitution and iterative processes.

Explores matrix inversion, pivoting, and iterative methods for solving linear systems of equations.

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