Variations on the conjugate Gradient Method
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.