When solving large linear systems of equations arising from the discretization of elliptic boundary value problems, a combination of iterative methods and preconditioners based on incomplete LU factorizations is frequently used. Given a model problem with variable coefficients, we investigate a class of incomplete LU factorizations depending on a relaxation parameter. We show that the associate...

This paper presents a parallel mixed direct/iterative method for solving linear systems Ax = b arising from circuit simulation. The systems are solved by a block LU factorization with an iterative method for the Schur complement. The Schur complement is a small and rather dense matrix. Direct LU decomposition of the Schur complement takes too much time in order to achieve reasonable speedup res...

This paper presents a preconditioning method based on combining two-sided permutations with a multilevel approach. The nonsymmetric permutation exploits a greedy strategy to put large entries of the matrix in the diagonal of the upper leading submatrix. The method can be regarded as a complete pivoting version of the incomplete LU factorization. This leads to an effective incomplete factorizati...

We have recently developed a new program analysis strat egy called fractal symbolic analysis that addresses some of limitations of techniques such as dependence analysis In this paper we show how fractal symbolic analysis can be used to convert between left looking and right looking versions of three kernels of central importance in compu tational science Cholesky factorization LU factorization...

We discuss and compare two greedy algorithms, that compute discrete versions of Fekete-like points for multivariate compact sets by basic tools of numerical linear algebra. The first gives the so-called “Approximate Fekete Points” by QR factorization with column pivoting of Vandermonde-like matrices. The second computes Discrete Leja Points by LU factorization with row pivoting. Moreover, we st...

Tridiagonal systems play a fundamental role in matrix computation. In particular, in recent years parallel algorithms for the solution of tridiagonal systems have been developed. Among these, the cyclic reduction algorithm is particularly interesting. Here the stability of the cyclic reduction method is studied under the assumption of diagonal dominance. A backward error analysis is made, yield...

Block tridiagonal systems of linear equations occur frequently in scientific computations, often forming the core of more complicated problems. Numerical methods for solution of such systems are studied with emphasis on efficient methods for a vector computer. A convergence theory for direct methods under conditions of block diagonal dominance is developed, demonstrating stability, convergence ...

Computing the null space of a sparse matrix, sometimes a rectangular sparse matrix, is an important part of some computations, such as embeddings and parametrization of meshes. We propose an efficient and reliable method to compute an orthonormal basis of the null space of a sparse square or rectangular matrix (usually with more rows than columns). The main computational component in our method...

Incomplete LU factorizations are among the most eeective preconditioners for solving general large, sparse linear systems arising from practical engineering problems. This paper shows how an ILU factorization may be easily computed in sparse skyline storage format, as opposed to traditional row-by-row schemes. This organization of the factorization has many advantages, including its amenability...