The University of Arizona

Variations on the conjugate Gradient Method

Variations on the conjugate Gradient Method

Series: Program in Applied Mathematics Brown Bag Seminar
Location: Math 402
Presenter: David Ropp, Program in Applied Mathematics, University of Arizona

Solving the equation Ax=b when A is a sparse, symmetric, positive-definite matrix is often done with the conjugate gradient method, usually with a preconditioner. However, what if A isn't exactly positive-definite, or symmetric, or sparse? Can we adjust the method to work on these systems as well, or are we better off using a different method? Answers, as well as a review of CG, will be provided.

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