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rank of matrix相关的网络例句

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On the basis of the conversion relation of inverse matrix and adjoint matrix, by using 1/ω instead of |B'(xBx(subscript k|, a new Landweber iteration formula x(superscript δ subscript k+1)=x(superscript δ subscript k)-ωB'(x(superscript δ subscript k)Bx(superscript δ subscript kB'x(superscript δ kf(x(superscript δ subscript k)-y(superscript δ)is constructed for solving rank deficiency nonlinear least squares problem, with which the phenomenon that leads to ill-posed problem because the iteration matrix is rank-deficient and very ill-conditioned in numerical iterative process is avoided.

基于逆矩阵和矩阵伴随算子的换算关系,并用1/ω代替|B′(xBx(下标k|,构造出一个新的求解秩亏非线性最小二乘问题的Landweber迭代格式,x(上标δ下标 k+1)=x-ωB'(xBx(上标δ下标 k*B'x(上标δ下标 kf(x-y,从而避免了在迭代过程中由于迭代矩阵的秩亏和病态而产生的不适定现象。

In this paper, for m× n Loewner-type matrix or symmetric Loewner-type matrix with full column rank, we form special block matrix firstly and research their triangular factorization or of its inverse, and then get the fast algorithm of the minimal norm least squaresolution .

本文对于秩为n的m×n阶Loewner型或对称Loewner型矩阵L,通过构造特殊分块矩阵并研究其逆的三角分解或自身三角分解,进而得到了线性方程组Lx=b的极小范数最小二乘解快速算法,计算复杂度为O+O(n~2),而一般方法的计算复杂度为O(mn~2)+O(n~3)。

A new fast algorithm of the minimal norm least squares solution for the linear system whose coefficient matrix is an m×n Loewner-type matrix with full column rank is given by forming a special block matrix and researching its triangular factorization.

通过构造特殊分块矩阵及其三角分解给出了求秩为n的m×n阶Loewner型矩阵为系数阵的线性方程组极小范数最小二乘解的快速算法,该算法的计算复杂度为O+O(n2),而一般方法的计算复杂度为O(mn2)+O(n3)。

Firstly, we apply block AOR method to find the least square solution of minimal norm to the rank deficient linear systems where A is a complex m*n matrix with rank k.

首先,我们把求解线性方程组的块加速超松弛迭代法应用于求解亏秩线性方程组的最小范数最小二乘解,其中线性方程组的系数矩阵A是秩为k的m*n复矩阵。

Means of non -singular determinant is not equal to 0, the matrix must be non - degenerate square matrix is full rank is, the matrix is not necessarily ...

非奇异的意思是行列式不等于0,矩阵一定是方阵非退化是矩阵是满秩的,矩阵不一定是。。。

Based on the special decomposition of Hankel matrix, we can transform the problem of approximating a given matrix with a positive semidefinite real Hankel matrix of lower rank to a smooth unconstrained optimization problem.

对矩阵的低秩Hankel半正定矩阵逼近,采用特殊的分解形式,可将其转化为一个无约束的优化问题进行求解,本文应用拟牛顿方法求解无约束优化问题。

Focusing on the computational bottleneck of traditional CCA,StreamCCA introduces a low-rank approximation technique to reduce the dimensionality of product matrix resulted from sample correlation matrix and sample variance matrix,which improves computational performance efficiently on the premise of holding approximate precision.

经理论分析和实验证明,S treamCCA能够在线精确地识别两条多维数据流的相关关系,可以作为通用的预报和诊断分析工具广泛应用于数据流挖掘领域。

The second method is to structure a toeplitz matrix to approach the toeplitzed signal subspace under the rule of mimimum power of noise, when the algorithm is converged, the structured toeplitz matrix is a full rank matrix, the angle information of target are all reserved and the influence of noise is minimum at this time.

第二种改进的方法是在噪声功率最小约束准则下构造一个Toeplitz矩阵,去逼近Toeplitz化后的信号子空间,当算法收敛时,所构造的Toeplitz矩阵是一个满秩矩阵,它既无损的保留了信源的角度信息又使噪声对DOA估计的影响最小。

Using the judgment matrix of detectable and locatable of gross errors in surveying error and reliability theory, it is proved firstly that the number of zero column vectors in LZD\'s judgment matrix equals to the rank defects of LZD\'s coefficient matrix. And then, LZD\'s matching equation having the detectability and locatability of gross errors is verified by a series of tests. These conclusions powerfully support the following researches on algorithms of detecting DEM deformations in theory.

运用误差与可靠性理论中的粗差可发现和可定位判断矩阵,证明了LZD算法判断矩阵中零列向量的个数等于LZD算法系数阵列秩亏数,试验验证了LZD算法匹配方程具备粗差发现和定位能力,为DEM差异探测算法提供了理论支持。

In chapter two, we first consider the linear system WAWx=b, where A is an m*n rank-deficient matrix. Then we consider the weighted Frobenius norm on data, and get the formula of W-weighted Drazin inverse solution of the linear system.

第二章的前半部分介绍了线性系统 WAWx=b 的加 W 权的 Drazin 逆解在范数带参数的 Frobenius 范数意义下的条件数,这里矩阵 A 为一个 m*n 的秩亏阵。

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