Lectures 24, 25, 32, 33, 35, 38, and 40. * Basic iterative methods (Jacobi, Gauss-Siedel, SOR, SSOR) (SOR and SSOR were covered in *much* less detail) * Convergence of some of these algorithms for diagonally dominant matrices * The Fundamental Theorem of Iterative Methods (and the theorems leading up to it) * Gershgorin theorems (I will give you the definition of the disks on the exam) * Arnoldi/Krylov techniques (you should know what a Krylov subspace is and what Arnoldi does, but there is no need to memorize algorithms) * Variational/minimization methods: Steepest Decent (SD), Conjugate Gradient (CG), and GMRES (Understand the proofs, but don't memorize the algorithms) * Preconditioning (just the main ideas, we didn't go into much detail here) * Eigenvalue problems and classical eigenvalue algorithms (we didn't have homework on this, so if it appears on the exam, it will only be over what we discussed in class)