Verified Results for Linear Systems with Sparse Matrices

1989 ◽  
pp. 137-161 ◽  
Author(s):  
W. Klein
2012 ◽  
Vol 20 (3) ◽  
pp. 241-255 ◽  
Author(s):  
Eric Bavier ◽  
Mark Hoemmen ◽  
Sivasankaran Rajamanickam ◽  
Heidi Thornquist

Solvers for large sparse linear systems come in two categories: direct and iterative. Amesos2, a package in the Trilinos software project, provides direct methods, and Belos, another Trilinos package, provides iterative methods. Amesos2 offers a common interface to many different sparse matrix factorization codes, and can handle any implementation of sparse matrices and vectors, via an easy-to-extend C++ traits interface. It can also factor matrices whose entries have arbitrary “Scalar” type, enabling extended-precision and mixed-precision algorithms. Belos includes many different iterative methods for solving large sparse linear systems and least-squares problems. Unlike competing iterative solver libraries, Belos completely decouples the algorithms from the implementations of the underlying linear algebra objects. This lets Belos exploit the latest hardware without changes to the code. Belos favors algorithms that solve higher-level problems, such as multiple simultaneous linear systems and sequences of related linear systems, faster than standard algorithms. The package also supports extended-precision and mixed-precision algorithms. Together, Amesos2 and Belos form a complete suite of sparse linear solvers.


2017 ◽  
Vol 17 (2) ◽  
pp. 201-215 ◽  
Author(s):  
Michele Benzi ◽  
Bora Uçar

AbstractWe introduce a class of preconditioners for general sparse matrices based on the Birkhoff–von Neumann decomposition of doubly stochastic matrices. These preconditioners are aimed primarily at solving challenging linear systems with highly unstructured and indefinite coefficient matrices. We present some theoretical results and numerical experiments on linear systems from a variety of applications.


2018 ◽  
Vol 18 (3) ◽  
pp. 449
Author(s):  
Thiago Nascimento Rodrigues ◽  
Maria Claudia Silva Boeres ◽  
Lucia Catabriga

The Reverse Cuthill-McKee (RCM) algorithm is a well-known heuristicfor reordering sparse matrices. It is typically used to speed up the computation ofsparse linear systems of equations. This paper describes two parallel approachesfor the RCM algorithm as well as an optimized version of each one based on someproposed enhancements. The first one exploits a strategy for reducing lazy threads,while the second one makes use of a static bucket array as the main data structureand suppress some steps performed by the original algorithm. These related changesled to outstanding reordering time results and significant bandwidth reductions.The performance of two algorithms is compared with the respective implementationmade available by Boost library. The OpenMP framework is used for supportingthe parallelism and both versions of the algorithm are tested with large sparse andstructural symmetric matrices.


Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2527
Author(s):  
József Abaffy ◽  
Szabina Fodor

Efficient solution of linear systems of equations is one of the central topics of numerical computation. Linear systems with complex coefficients arise from various physics and quantum chemistry problems. In this paper, we propose a novel ABS-based algorithm, which is able to solve complex systems of linear equations. Theoretical analysis is given to highlight the basic features of our new algorithm. Four variants of our algorithm were also implemented and intensively tested on randomly generated full and sparse matrices and real-life problems. The results of numerical experiments reveal that our ABS-based algorithm is able to compute the solution with high accuracy. The performance of our algorithm was compared with a commercially available software, Matlab’s mldivide (\) algorithm. Our algorithm outperformed the Matlab algorithm in most cases in terms of computational accuracy. These results expand the practical usefulness of our algorithm.


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