利用Frobenius范数最小化求实矩阵逆的迭代方法的收敛加速

2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2016-09-01 DOI:10.1109/SYNASC.2016.031

Ajinkya Borle, S. Lomonaco

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引用次数: 2

摘要

计算广义矩阵逆的schulz型方法是一系列迭代方法，它们因其高收敛阶(≥2)而广受欢迎。我们提出了两种新的基于Frobenius范数最小化的实际矩阵的此类迭代方法的缩放加速技术，并提出了实现这些技术的有效算法。测试结果表明，我们的一种技术对密集矩阵最有效，但也适用于稀疏情况。

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Convergence Acceleration of Iterative Methods for Inverting Real Matrices Using Frobenius Norm Minimization

The Schulz-type methods for computing generalizedmatrix inverses are a family of iterative methods that are popular for their high order of convergence (≥ 2). We propose two new scaled acceleration techniques for such type of iterative methods for real matrices (based on Frobenius norm minimization) andlay out efficient algorithms to implement these techniques. Testresults show one of our techniques to be most effective for densematrices but also works for sparse cases as well.

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来源期刊

2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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