Preconditioned Krylov subspace methods have proved to be ecient in solving large, sparse linear systems in many areas of scienti®c computing. The success of these methods in many cases is due to the existence of good preconditioning techniques. In problems of structural mechanics, like the analysis of heat transfer and deformation of solid bodies, iterative solution of the linear equation syst...

Multiplicative preconditioning is a popular SVD-based techniques for the solution of linear systems of equations, but our SVD-free additive preconditioners are more readily available and better preserve matrix structure. We combine additive preconditioning with aggregation and other relevant techniques to facilitate the solution of linear systems of equations and some other fundamental matrix c...

Kernel methods have achieved very good performance on large scale regression and classification problems by using the Nystrom method preconditioning techniques. The approximation---base...

In this paper, we consider the numerical solution of poroelasticity problem with stochastic properties. We present a Two-stage Markov Chain Monte Carlo method for geomechanical subsidence. work, study two techniques preconditioning: (MS) multiscale model order reduction and (ML) machine learning technique. The purpose preconditioning is fast sampling, where new proposal first tested by cheap so...

A number of recently proposed preconditioning techniques based on sparse approximate inverses are considered. A description of the preconditioners is given, and the results of an experimental comparison performed on one processor of a Cray C98 vector computer using sparse matrices from a variety of applications are presented. A comparison with more standard preconditioning techniques, such as i...

A number of recently proposed preconditioning techniques based on sparse approximate inverses are considered. A description of the preconditioners is given, and the results of an experimental comparison performed on a Cray C98 vector processor using sparse matrices from a variety of applications are presented. A comparison with more standard preconditioning techniques, such as incomplete factor...

Incomplete factorization preconditioners such as ILU, ILUT and MILU are well-known robust general-purpose techniques for solving linear systems on serial computers. However, they are difficult to parallelize efficiently. Various techniques have been used to parallelize these preconditioners, such as multicolor orderings and subdomain preconditioning. These techniques may degrade the performance...