Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications (DG)

1 Introduction and Motivations 1.1 Notes on error analysis 1.2 Sparse matrices 1.3 On parallel computing and hardware acceleration 2 Some iterative algorithms for linear systems 2.1 Iterative algorithms and simple one-dimensional techniques 2.2 Krylov subspace algorithms, orthogonal projections: CG and GMRES 2.3 Krylov subspace algorithms, oblique projections: BiCG, QMR, CGS, BiCGstab, BiCGstab 2.4 Which Krylov method should I use? 3 General purpose preconditioning strategies 3.1 Generalities on preconditioning 3.2 Krylov iterative methods for preconditioned iterations 3.3 On Jacobi, SOR, and SSOR preconditioners 3.4 Incomplete factorizations preconditioners 3.5 Approximate Inverse Preconditioners 3.6 Preconditioning sequences of linear systems 3.7 Parallelizing preconditioners and available software 3.8 Which general purpose preconditioner should I use? 4 Preconditioners for some structured linear systems 4.1 Toeplitz and block Toeplitz systems 4.2 Notes on multigrid preconditioning 4.3 Complex symmetric systems 4.4 Saddle point systems Appendix A: A Review of Numerical Linear Algebra A.1 Vector and matrix norms A.2 Eigenvalues and singular values Appendix B: Data sets and software codes B.1 Test matrices and data sets B.2 Numerical Linear Algebra software