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LU-decomposition with iterative refinement for solving sparse linear systems

机译:LU迭代迭代精解法求解稀疏线性系统

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In the solution of a system of linear algebraic equations Ax = b with a large sparse coefficient matrix A, the LU-decomposition with iterative refinement (LUIR) is compared with the LU-decomposition with direct solution (LUDS), which is without iterative refinement. We verify by numerical experiments that the use of sparse matrix techniques with LUIR may result in a reduction of both the computing time and the storage requirements. The powers of a Boolean matrix strategy (PBS) is used in an effort to achieve such a reduction and in an attempt to control the sparsity. We conclude that iterative refinement procedures may be efficiently used as an option in software for the solution of sparse linear systems of equations. (c) 2005 Published by Elsevier B.V.
机译:在具有大的稀疏系数矩阵A的线性代数方程组Ax = b的系统中,将具有迭代精化的LU分解(LUIR)与没有迭代精化的直接解LU分解(LUDS)进行比较。我们通过数值实验验证了将稀疏矩阵技术与LUIR结合使用可以减少计算时间和存储需求。布尔矩阵策略(PBS)的功能用于实现这种简化并试图控制稀疏性。我们得出结论,迭代优化程序可以有效地用作软件中的稀疏线性方程组解决方案。 (c)2005年由Elsevier B.V.

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