非奇异H-矩阵新的含参数细分迭代判别法

结合矩阵自身的元素,构造了含参数的迭代公式,进而细分了矩阵非对角占优行指标集.利用广义严格α-对角占优矩阵与非奇异H-矩阵的关系,给出了非奇异H-矩阵一组新的细分迭代判定准则,推广和改进了已有的结果,通过数值算例说明了结果的优越性.     

非奇异H矩阵迭代式充分条件

非奇异H矩阵是一类应用非常广泛的特殊矩阵.从矩阵元素出发,给出了一组非奇异H矩阵新的简捷而实用的迭代形式的充分条件.该迭代形式的充分条件推广并改进了相关的结果.最后用数值算例验证了该迭代式条件的优越性.     

一类线性约束矩阵不等式及其最小二乘问题

本文主要考虑一类线性矩阵不等式及其最小二乘问题,它等价于相应的矩阵不等式最小非负偏差问题.之前相关文献提出了求解该类最小非负偏差问题的迭代方法,但该方法在每步迭代过程中需要精确求解一个约束最小二乘子问题,因此对规模较大的问题,整个迭代过程需要耗费巨大的计算量.为了提高计算效率,本文在现有算法的基础上,提出了一类修正迭代方法.该方法在每步迭代过程中利用有限步的矩阵型LSQR方法求解一个低维矩阵Krylov子空间上的约束最小二乘子问题,降低了整个迭代所需的计算量.进一步运用投影定理以及相关的矩阵分析方法证明了该修正算法的收敛性,最后通过数值例子验证了本文的理论结果以及算法的有效性.     

求解低秩矩阵填充的改进的交替最速下降法

矩阵填充是指利用矩阵的低秩特性而由部分观测元素恢复出原矩阵,在推荐系统、信号处理、医学成像、机器学习等领域有着广泛的应用。采用精确线搜索的交替最速下降法由于每次迭代计算量小因而对大规模问题的求解非常有效。本文在其基础上采用分离地精确线搜索,可使得每次迭代下降更多但计算量相同,从而可望进一步提高计算效率。本文分析了新算法的收敛性。数值结果也表明所提出的算法更加有效。     

Toeplitz矩阵填充的四种流形逼近算法比较

 本文提出Toeplitz矩阵填充的四种流形逼近算法。在左奇异向量空间中对已知部分运用最小二乘法逼近,形成新的可行矩阵;并将对角线上的元素分别用均值,l₁范数,l_∞范数和中间数四种方法逼近使得迭代后的矩阵仍保持Toeplitz结构,节约了奇异向量空间的分解时间。最终找到合理的低秩矩阵来逼近未知的高秩矩阵,进而精确地完成Toeplitz矩阵的填充。理论上,分析了在一定条件下算法的收敛性。实验上,通过取不同的采样密度进行数值实验展示了四种算法的优劣。实验结果说明均值算法和l_∞范数算法大多用的时间较少,但是当采样密度和矩阵规模较大时,中间数算法的精度较高。     

非奇异H矩阵的迭代条件

非奇异H矩阵是一类具有重要意义的特殊矩阵.从矩阵元素出发,通过迭代方法得到了一组新的非奇异H矩阵简捷而实用的充分条件,最后用数值例子验证了充分条件的优越性.     

接触问题中的快速凝缩消元法

用分离解法求解弹性接触问题时，在增量加载和迭代过程中，由于接触区某些节点的状态发生改变而导致方程组的系数矩阵某些行和列元素随之变化。根据此特点，本推导了一种新的自适应迭代算法-快速凝缩消元法，并给出具体的迭代步骤，避免了系数矩阵变化时必须重新形成矩阵的重复计算。     

广义Nekrasov矩阵的细分迭代判别法

利用细分矩阵下标集合的思想,构造递进系数和特殊的正对角矩阵,结合不等式的放缩,给出广义Nekrasov矩阵的迭代判别法,推广和改进了已有相关结果,并用数值实例说明了所得结果的优越性.     

制造业技术创新效率评价模型及其应用研究

从技术创新系统的内部过程出发,将制造业技术创新过程划分为技术研究与开发、技术应用与改造、环境污染治理三个阶段;然后运用虚拟系统法构建出制造业三阶段链式网络DEA交叉效率评价模型,并利用熵值法来确定交叉效率矩阵中各决策单元的权重,再通过加权求和法计算最终评价值;最后将此模型应用于福建省制造业技术创新效率的评价中。研究表明,福建省制造业各行业的技术创新效率,无论是整个技术创新系统,还是技术创新系统的各个子阶段,其交叉效率值普遍偏低,具有较大的提升空间。     

水环境质量评价的模糊聚类法

将模糊聚类最大矩阵元原理与基于数据迭代为基础的水质模糊评价理论模型相结合，形成模糊聚类迭代方法．并用该方法对甘肃金昌市地下水质进行了分类评价，得到了今人满意的结果．     

模糊层次分析法在矩阵论教材评价方面的应用

基础学科的教材直接影响学生的基本功,尤其是几乎每个学科都会涉及的数学类教材,如矩阵论.矩阵论是研究生的基础课程,在对学生以后的学术道路有举足轻重的作用.所以选择一本合适的教材,对学生和教师来说都有不小的帮助.然而,对教材的评价而言,不能单单从一个方面入手,因此将模糊综合分析法与层次分析法结合,在矩阵论教材的评价方面建立评价体系,为高校选择合适的教材提供依据.     

评估中不确定情况下指标权重的一种确定方法

指标权系数的确定是多属性决策的评估中的一个关键问题，目前常用的方法都要利用专家判断矩阵。然而在实际操作中判断矩阵的元素在各种因素的影响下，表现出一定的不确定性。本文利用整体法来研究这种不确定情况，对原有的模型进行推广，并提出有效的算法。对类似问题的解决，提供了一种新的思路。     

Pascal矩阵与Vandermonde矩阵的关系

EI-Mikkawy M证明了对称Pascal矩阵Q_n和Vlandermonde矩阵V_n之间满足矩阵方程Q_n=T_nV_n,这里T_n是一个随机矩阵。本文证明了随机矩阵T_n能够分解成第一类Stirling矩阵和对角矩阵的乘积,得到了矩阵T_n的元素之间的递推关系,从而回答了EI-Mikkawy M的一个公开问题。同时得到了一些与Stirling数相关的组合恒等式。     

Mapped barycentric Chebyshev differentiation matrix method for the solution of regular Sturm-Liouville problems

In this paper, using spectral differentiation matrix and an elimination treatment of boundary conditions, Sturm-Liouville problems (SLPs) are discretized into standard matrix eigenvalue problems. The eigenvalues of the original Sturm-Liouville operator are approximated by the eigenvalues of the corresponding Chebyshev differentiation matrix (CDM). This greatly improves the efficiency of the classical Chebyshev collocation method for SLPs, where a determinant or a generalized matrix eigenvalue problem has to be computed. Furthermore, the state-of-the-art spectral method, which incorporates the barycentric rational interpolation with a conformal map, is used to solve regular SLPs. A much more accurate mapped barycentric Chebyshev differentiation matrix (MBCDM) is obtained to approximate the Sturm-Liouville operator. Compared with many other existing methods, the MBCDM method achieves higher accuracy and efficiency, i.e., it produces fewer outliers. When a large number of eigenvalues need to be computed, the MBCDM method is very competitive. Hundreds of eigenvalues up to more than ten digits accuracy can be computed in several seconds on a personal computer.     

求分块带状方程组的新算法

<正>1引言在网络,自动化理论,差分方程求解及逻辑电路等实际问题中,往往需要求解分块带状方程组HX=F(1)这里H=(H_(ij)_(n×n),其中     

Fast algorithms for placing large entries along the diagonal of a sparse matrix

Solving a sparse system of linear equations Ax=b is one of the most fundamental operations inside any circuit simulator. The equations/rows in the matrix A are often rearranged/permuted before factorization and applying direct or iterative methods to obtain the solution. Permuting the rows of the matrix A so that the entries with large absolute values lie on the diagonal has several advantages like better numerical stability for direct methods (e.g., Gaussian elimination) and faster convergence for indirect methods (such as the Jacobi method). Duff (2009) [3] has formulated this as a weighted bipartite matching problem (the MC64 algorithm). In this paper we improve the performance of the MC64 algorithm with a new labeling technique which improves the asymptotic complexity of updating dual variables from O(|V|+|E|) to O(|V|), where |V| is the order of the matrix A and |E| is the number of non-zeros. Experimental results from using the new algorithm, when benchmarked with both industry benchmarks and UFL sparse matrix collection, are very promising. Our algorithm is more than 60 times faster (than Duff’s algorithm) for sparse matrices with at least a million non-zeros.     

An efficient monotone projected Barzilai–Borwein method for nonnegative matrix factorization

In this paper, we present an efficient method for nonnegative matrix factorization based on the alternating nonnegative least squares framework. Our approach adopts a monotone projected Barzilai–Borwein (MPBB) method as an essential subroutine where the step length is determined without line search. The Lipschitz constant of the gradient is exploited to accelerate convergence. Global convergence of the proposed MPBB method is established. Numerical results are reported to demonstrate the efficiency of our algorithm.     

Wavelet analysis method for solving linear and nonlinear singular boundary value problems

In this paper, a robust and accurate algorithm for solving both linear and nonlinear singular boundary value problems is proposed. We introduce the Chebyshev wavelets operational matrix of derivative and product operation matrix. Chebyshev wavelets expansions together with operational matrix of derivative are employed to solve ordinary differential equations in which, at least, one of the coefficient functions or solution function is not analytic. Several examples are included to illustrate the efficiency and accuracy of the proposed method.     

基于梯度的Sylvester共轭矩阵方程的迭代解

本文提出了一种基于梯度的Sylvester共轭矩阵方程的迭代算法.通过引入一个松弛参数和采用递阶辨识原理,构造一个迭代算法求解Sylvester矩阵方程.通过应用复矩阵的实数表达以及实数表示的一些性质,收敛性分析表明在一定假设条件下,对于任意初始值,迭代方法均收敛到精确解,数值算例也表明了所给方法的有效性.     

An efficient method for computing the inverse of arrowhead matrices

In this paper we propose a simple and effective method to find the inverse of arrowhead matrices which often appear in wide areas of applied science and engineering such as wireless communications systems, molecular physics, oscillators vibrationally coupled with Fermi liquid, and eigenvalue problems. A modified Sherman–Morrison inverse matrix method is proposed for computing the inverse of an arrowhead matrix. The effectiveness of the proposed method is illustrated and numerical results are presented along with comparative results.