| 应用奇异值分解求解最小二乘法问题 |
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| 引用本文： | 施吕蓉.应用奇异值分解求解最小二乘法问题[J].芜湖职业技术学院学报,2008,10(4):6-8. |
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| 作者姓名： | 施吕蓉 |
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| 作者单位： | 芜湖信息技术职业学院软件工程系,安徽芜湖,241000 |
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| 摘 要： | 奇异值分解定理（SVD）是一种非常重要的矩阵分解定理。使用奇异值分解，可以挖掘矩阵中隐藏的重要结构信息，并可以降低矩阵的维数。该定理还应用于解决最小二乘法问题。
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| 关 键 词： | 奇异值分解 矩阵秩 范数 |
Application singular value decomposition for method of least squares problem |
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| SHI lv-rong.Application singular value decomposition for method of least squares problem[J].Journal of Wuhu Vocational Institute of Technology,2008,10(4):6-8. |
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| Authors: | SHI lv-rong |
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| Institution: | SHI lv-rong |
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| Abstract: | Singular value decomposition (SVD) is a very important matrix decomposition theory. Singular value decomposition can find the important structural information which hides in the matrix. And it can reduce the dimension of the matrix. This theorem is also applied in the solving of the least squares problem. |
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| Keywords: | singular value decomposition rank of a matrix normal number |
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