Method for Verifying Solutions of Sparse Linear Systems with General Coefficients

TL;DR

This paper introduces a new verification method for sparse linear systems with general coefficients using LDL^T decomposition, improving efficiency and applicability over traditional methods.

Contribution

The paper presents a novel verification approach based on LDL^T decomposition that reduces fill-in and enhances verification of sparse systems with general coefficients.

Findings

The proposed method effectively verifies solutions of sparse systems.

It reduces fill-in compared to traditional methods.

An iterative refinement improves solution accuracy.

Abstract

This paper proposes a verification method for sparse linear systems $Ax=b$ with general and nonsingular coefficients. A verification method produces the error bound for a given approximate solution. Conventional methods use one of two approaches. One approach is to verify the computed solution of the normal equation $A^TAx=A^Tb$ by exploiting symmetric and positive definiteness; however, the condition number of $A^TA$ is the square of that for $A$. The other approach uses an approximate inverse matrix of the coefficient; however, the approximate inverse may be dense even if $A$ is sparse. Here, we propose a method for the verification of solutions of sparse linear systems based on $LDL^T$ decomposition. The proposed method can reduce the fill-in and is applicable to many problems. Moreover, an efficient iterative refinement method is proposed for obtaining accurate solutions.