This new method reduces the computational complexity by using the block tridiagonal structure of the input sample correlation matrix, and at the same time keeps the property of fast convergence.

在保持了该算法快速收敛优点的同时，利用相关矩阵块三对角的特殊结构，降低了该算法的计算复杂度。

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First take the gray correlation coefficient matrix between primitive data sample and ideal scheme as new decision-making matrix, and then use TOPSIS to evaluate and arrange all schemes.

该方法以原始数据样本与理想方案之间的灰色关联系数矩阵为新的决策矩阵，利用理想解法对方案进行排序。

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In this paper, based on the correlation matrix analysis of sample data, a principal component analysis method is used.

在本文的模型估计部分，依据样本数据相关矩阵分析结果，采用了样本主成分分析的方法。

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