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Journal ArticleDOI

ILUT: A dual threshold incomplete LU factorization

Yousef Saad
- 01 Jul 1994 - 
- Vol. 1, Iss: 4, pp 387-402
TLDR
This ILUT factorization extends the usual ILU(O) factorization without using the concept of level of fill-in, and is a compromise between these two extremes.
Abstract: 
In this paper we describe an Incomplete LU factorization technique based on a strategy which combines two heuristics. This ILUT factorization extends the usual ILU(O) factorization without using the concept of level of fill-in. There are two traditional ways of developing incomplete factorization preconditioners. The first uses a symbolic factorization approach in which a level of fill is attributed to each fill-in element using only the graph of the matrix. Then each fill-in that is introduced is dropped whenever its level of fill exceeds a certain threshold. The second class of methods consists of techniques derived from modifications of a given direct solver by including a dropoff rule, based on the numerical size of the fill-ins introduced, traditionally referred to as threshold preconditioners. The first type of approach may not be reliable for indefinite problems, since it does not consider numerical values. The second is often far more expensive than the standard ILU(O). The strategy we propose is a compromise between these two extremes.

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Citations
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Book

Iterative Methods for Sparse Linear Systems

Yousef Saad
TL;DR: This chapter discusses methods related to the normal equations of linear algebra, and some of the techniques used in this chapter were derived from previous chapters of this book.
Book

Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book

TL;DR: This book is a tutorial written by researchers and developers behind the FEniCS Project and explores an advanced, expressive approach to the development of mathematical software.
Journal ArticleDOI

Preconditioning techniques for large linear systems: a survey

TL;DR: This article surveys preconditioning techniques for the iterative solution of large linear systems, with a focus on algebraic methods suitable for general sparse matrices, including progress in incomplete factorization methods, sparse approximate inverses, reorderings, parallelization issues, and block and multilevel extensions.
Journal ArticleDOI

Numerical methods for large eigenvalue problems

TL;DR: Over the past decade considerable progress has been made towards the numerical solution of large-scale eigenvalue problems, particularly for nonsymmetric matrices, and the methods and software that have led to these advances are surveyed.
Dissertation

Krylov Projection Methods for Model Reduction

TL;DR: The cornerstone of this dissertation is a collection of theory relating Krylov projection to rational interpolation, based on which three algorithms for model reduction are proposed, which are suited for parallel or approximate computations.
References
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MonographDOI

Direct methods for sparse matrices

TL;DR: This book aims to be suitable also for a student course, probably at MSc level, and the subject is intensely practical and this book is written with practicalities ever in mind.
Journal ArticleDOI

An iterative solution method for linear systems of which the coefficient matrix is a symmetric -matrix

TL;DR: A particular class of regular splittings of not necessarily symmetric M-matrices is proposed, if the matrix is symmetric, this splitting is combined with the conjugate-gradient method to provide a fast iterative solution algorithm.
Journal ArticleDOI

Sparse matrix test problems

TL;DR: The Harwell-Boeing sparse matrix collection is described, a set of standard test matrices for sparse matrix problems that comprises problems in linear systems, least squares, and eigenvalue calculations from a wide variety of scientific and engineering disciplines.
Journal ArticleDOI

Sparse matrix test problems

TL;DR: A comprehensive set of test problems will lead to a better understanding of the range of structures in sparse matrix problems and thence to better classification and development of algorithms.
Journal ArticleDOI

Solving sparse triangular linear systems on parallel computers

TL;DR: This paper describes and compares three parallel algorithms for solving sparse triangular systems of equations and considers both row-wise and jagged diagonal storage for the offdiagonal blocks.