| Review: |
This updated volume covers iterative methods (providing sequences of approximations that converge to a solution) for solving large sparse linear systems (involving only a few unknowns). As parallel computing has become more widely used, iterative models are easier to solve than direct solvers, but they require new approaches and solutions that are different from classical methods. Since publication of the first edition, six years ago, iterative methods for linear systems have made good progress in science and engineering fields. This volume contains a new chapter on multigrid techniques and chapters are updated on: sparse matrices; Krylov subspace methods; preconditioning techniques; and parallel preconditioners. |