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Construction of three kinds of QR algorithm based on optimal power evaluation model for solving the matrix eigenvalue

机译：基于最优功率评估模型的三种QR算法的求解矩阵特征值

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As the most basic, the most important knowledge system In linear algebra, matrix solution is the basis of learning and application of linear algebra. Therefore, the eigenvalues of the matrix becomes one of the focus of the study naturally. In this paper, their are a series of studies for the eigenvalues of the matrix algorithm. It starts with the concept and nature of the matrix, the eigenvalues and algorithm. The final purpose of research is the three constraint conditions. They are simple in calculation,easy understanding, accurate results. Analysis of Schmidt Orthogonalization, elementary transformation of matrix and Givens transformation of the three algorithms, thus draw the conclusion: Computing characteristic value of matrix is not only the simple problems of mathematical evaluation and calculation, but also it relates to many areas of life such as engineering and technology. In the solution process, the algorithm is more common, the most widely one. And for Schmidt Orthogonalization, elementary transformation of matrix and Givens transformation is studied, Through the construction of optimal power evaluation model, found the elementary transformation of matrix is the most suitable for matrix eigenvalue algorithm finally.
机译：作为线性代数中最基本，最重要的知识体系，矩阵解是线性代数学习和应用的基础。因此，矩阵的特征值自然成为研究的重点之一。本文对矩阵算法的特征值进行了一系列研究。它从矩阵的概念和性质，特征值和算法开始。研究的最终目的是三个约束条件。它们计算简单，易于理解，结果准确。通过对施密特正交化，矩阵的基元变换和纪梵transformation变换这三种算法的分析，得出以下结论：矩阵的特征值计算不仅是数学评估和计算的简单问题，而且还涉及许多生活领域，例如工程和技术。在求解过程中，该算法较为普遍，是最广泛的一种。对于Schmidt正交化，研究了矩阵的初等变换和Givens变换，通过最优功率评价模型的构建，最终发现矩阵的初等变换最适合矩阵特征值算法。

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