Conjugate Gradient Method

Related lectures (29)

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Explores Richardson's methods for iterative linear system solutions and error control in symmetric positive definite systems.

Covers the Cholesky decomposition of a symmetric positive definite matrix and its applications.

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

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

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

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

Explores optimal control and KKT conditions for non-linear optimization with constraints.

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

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