Diagonalize

SVD in numerical analysis was the numerical algorithm that diagonalize the matrice .

数值分析中的奇异值分解（SVD）是一种将矩阵对角化的数值算法。

Singular value decomposition ( SVD ) is one kind of orthogonal transformation , it can diagonalize matrix .

奇异值分解（SVD，singularvaluedecomposition）是一种正交变换，它可以将矩阵对角化。

Chapter 4 deals with the problem of how to diagonalize and solve a class of systems of differential equations .

第四章研究微分方程组的对角化和求解问题。

Based on supersymmetric mechanics , it is introduced supersymmetric unitary transformation to diagonalize the Hamiltonian .

根据超对称量子力学理论，引入了超对称幺正变换使其哈密顿量对角化。

We develop an effective algorithm to solve the pseudo-inverse generalized eigenvalue problem via using a matrix diagonalization technique , which can diagonalize three matrices simultaneously .

通过利用同时对角化三个矩阵的技术，我们得到了一种求解包含伪逆的广义特征值问题的有效算法。

This method try to diagonalize covariance matrix of original data , expect to find the most important elements and structures , then get rid of noises and redundancy , so the original data can be simplified .

这种方法试图通过对数据协方差矩阵进行对角化，从而找到数据中最主要的元素和结构，去除噪音和冗余，对原数据进行简化处理。

In this paper , the Parameter dependant formulation of the Bogoliubov transformation is used to diagonalize Hamiltonian of Heisenberg antiferromagnet two sublattice modle , and to derive the spin dependant unitary operator performing this transformation and the exact eigenstates .

利用参量相关的Bogoliubov变换公式对角化Heisenberg反铁磁双子晶格模型哈密顿，得到施行变换的自旋相关的幺正算符和精确本征态。