1·Parallel Lanczos SVD (Singular Value Decomposition) solver.
并行Lanczos SVD(奇异值分解)计算。
2·Singular value decomposition (SVD) has very important applications in image processing.
奇异值分解(SVD)在图像处理中具有极其重要的应用。
3·A least squares solution via singular value decomposition is used to solve the matrix equation.
本文使用奇异值分解法求解矩阵方程的最小二乘解。
4·Then, a method is presented based on the singular value decomposition to compute the minimal norm solution.
然后用奇异值分解给出了求解最小范数解的一种方法。
5·The problem of image matching and target tracking based on singular value decomposition (SVD) was discussed.
研究了基于奇异值分解的图像匹配和目标跟踪问题。
6·Using the singular value decomposition technique, the method for measuring the modal controllability is determined.
利用奇异值分解技术确定了定量度量模态可控程度的方法。
7·A face identification method based on singular value decomposition (SVD) and data fusion is proposed in this paper.
提出了一种基于奇异值分解和数据融合的脸像鉴别方法。
8·The singular value decomposition least squares(SVDLS)method was improved for the various dynamic spectrum analysis.
本文改进了处理动态光谱的奇异值分解最小二乘法（SVDLS）。
9·The GGE data is then subjected to singular value decomposition and is approximated by the first two principal components.
对GGE 作单值分解，并以第一和第二主成分近似之。
10·The Singular Value Decomposition (SVD) method for the equilibrium matrix is developed and a physical explanation is given.
引入了平衡矩阵的奇异值分解（SVD）方法并解释了其力学含义。