The thermally coupled incompressible inductionless magnetohydrodynamics (MHD) problem models the flow of an electrically charged fluid under the influence of an external electromagnetic field with thermal coupling. This system of partial differential equations is strongly coupled and highly nonlinear for real cases of interest. Therefore, fully implicit time integration schemes are very desirab...

The purpose of this work is to compare the performance of some preconditioned iterative methods for solving the linear systems of equations, formed at each time-integration step of two-dimensional incompressible NavierStokes equations of fluid flow. The Navier-Stokes equations are discretized in an implicit and upwind control volume formulation. The iterative methods used in this paper include ...

This paper describes a Multilevel Incomplete QR (MIQR) factorization for solving large sparse least-squares problems. The algorithm builds the factorization by exploiting structural orthogonality in general sparse matrices. At any given step, the algorithm finds an independent set of columns, i.e., a set of columns that have orthogonal patterns. The other columns are then block orthogonalized a...

A new preconditioner for symmetric positive definite systems is proposed, analyzed, and tested. The preconditioner, compressed incomplete modified Gram–Schmidt (CIMGS), is based on an incomplete orthogonal factorization. CIMGS is robust both theoretically and empirically, existing (in exact arithmetic) for any full rank matrix. Numerically it is more robust than an incomplete Cholesky factoriza...

With the advent of detailed numerical models, computing runtime can become excessive. We propose the facilitation of an innovative multi-level inverse-based LU preconditioning approach to improve computational efficiency while processing EIT system matrices. This combines static reordering and scaling, controlled growth of the inverse of triangular factors and approximation of the Schurcompleme...

Preconditioning strategies based on incomplete LU factorization using thresholding with dual dropping (ILUT) are investigated for iterative solution of sparse linear systems arising in semiconductor dopant diiusion modeling. Of particular interest are questions associated with selection and adaption of threshold parameters with spatial resolution, timestep in the adaptive ODE integrator and the...

In this paper we study the parallel aspects of IMGS, Incomplete Modiied Gram-Schmidt preconditioner which can be used for ef-ciently solving sparse and large linear systems and least squares problems on massively parallel distributed memory computers. The performance of this preconditioning technique on this kind of architecture is always limited because of the global communication required for...

In this paper an efficient incomplete factorization preconditioner is proposed for the Least Mean Squares (LMS) adaptive filter. The proposed preconditioner is approximated from a priori knowledge of the factors of input correlation matrix with an incomplete strategy, motivated by the sparsity patter of the upper triangular factor in the QRD-RLS algorithm. The convergence properties of IPLMS al...

In this article, we present several new permutations for I-matrices making these more suitable for incomplete LDU-factorization preconditioners used in solving linear systems by iterative methods. A general matrix can be transformed by row permutation as well as row and columns scaling into an I-matrix, i.e. a matrix having elements of modulus 1 on the diagonal and elements of modulus of no mor...