Iterative linear system solvers with approximate matrix-vector products


There are classes of linear problems for which a matrix-vector product is a time consuming operation because an expensive approximation method is required to compute it to a given accuracy. One important example is simulations in lattice QCD with Neuberger fermions where a matrix multiply requires the product of the matrix sign function of a large sparse matrix times a vector. The recent interest in this and similar type of applications has resulted in research efforts to study the effect of errors in the matrix-vector products on iterative linear system solvers. In this paper we give a very general and abstract discussion on this issue and try to provide insight into why some iterative system solvers are more sensitive than others.

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@inproceedings{Eshof2005IterativeLS, title={Iterative linear system solvers with approximate matrix-vector products}, author={Jasper van den Eshof and Martin B. van Gijzen}, year={2005} }