线性矩阵方程异类约束最小二乘解的迭代算法

多矩阵变量线性矩阵方程(LME)约束解的计算问题在参数识别、结构设计、振动理论、自动控制理论等领域都有广泛应用。本文借鉴求线性矩阵方程(LME)同类约束最小二乘解的迭代算法,通过构造等价的线性矩阵方程组,建立了求多矩阵变量LME的一种异类约束最小二乘解的迭代算法,并证明了该算法的收敛性。在不考虑舍入误差的情况下,利用该算法不仅可在有限步计算后得到LME的一组异类约束最小二乘解,而且选取特殊初始矩阵时,可求得LME的极小范数异类约束最小二乘解。另外,还可求得指定矩阵在该LME的异类约束最小二乘解集合中的最佳逼近解。算例表明,该算法是有效的。     

多矩阵变量线性矩阵方程的广义自反解的迭代算法

基于求线性矩阵方程约束解的修正共轭梯度法的思想方法,通过修改某些矩阵的结构,建立了求特殊类型的多矩阵变量线性矩阵方程的广义自反解的迭代算法,证明了迭代算法的收敛性,解决了给定矩阵在该矩阵方程的广义自反解集合中的最佳逼近计算问题.当矩阵方程相容时,该算法可以在有限步计算后得到其一组广义自反解;选取特殊的初始矩阵,能够求得其极小范数广义自反解.数值算例表明,迭代算法是有效的.     

实矩阵两类广义逆的迭代算法

将计算实矩阵的Moore-Penrose逆和Drazin逆转化为线性矩阵方程组的求解问题,然后采用修正共轭梯度法求线性矩阵方程组的一般解,并通过简单的矩阵乘法运算或者直接得到实矩阵的Moore-Penrose逆和Drazin逆.修正共轭梯度法不同于通常的共轭梯度法,它不要求涉及的线性代数方程组的系数矩阵正定、可逆或者列满秩,因此总是可行的.数值算例表明,这种算法是有效的.     

一类广义Riccati矩阵方程对称解的双迭代算法

研究了一类广义系统控制理论导出的Riccati矩阵方程对称解的数值计算方法．运用牛顿算法将Riccati矩阵方程的对称解问题转化为线性矩阵方程的对称解或者对称最小二乘解问题,采用修正共轭梯度法解决导出的线性矩阵方程的对称解问题,可建立求Riccati矩阵方程对称解的双迭代算法．数值算例表明,双迭代算法是有效的．     

Riccati方程子矩阵约束对称解的非精确Newton-MCG算法

采用修正共轭梯度法(MCG算法)求由Newton算法每一步迭代计算导出的线性矩阵方程的近似子矩阵约束(SMC)对称解或者近似SMC对称最小二乘解,建立求离散时间代数Riccati矩阵方程SMC对称解的非精确Newton-MCG算法.该算法仅要求Riccati矩阵方程有SMC对称解,不要求它的SMC对称解唯一,也不要求导出的线性矩阵方程有相应的SMC对称解.数值算例表明,非精确Newton-MCG算法是有效的.     

单变量矩阵方程子矩阵约束下牛顿-MCG算法

子矩阵约束问题源于实际应用中的子系统扩张问题,文中研究了子矩阵约束下二次矩阵方程对称解的迭代算法,先用牛顿算法把二次矩阵方程转化为关于校正矩阵的线性矩阵方程,再用修正共轭梯度算法(MCG算法)求解导出线性矩阵方程对称解或最小二乘解,建立了求单变量二次矩阵方程子矩阵约束下对称解牛顿-MCG算法.数值算例表明,该牛顿-MCG是有效的,能在有限步迭代得到方程的子矩阵约束解.     

矩阵方程组A_1XB_1=C_1,A_2XB_2=C_2的迭代算法

矩阵方程组的求解在结构设计、参数识别、生物学、电学、分子光谱学、固体力学、自动控制理论、振动理论、有限元、线性最优控制等领域都有着重要应用。本文从解线性代数方程组的共轭梯度法中受到启示,不是采用传统的矩阵分解的方法,而是采用迭代算法给出了求矩阵方程组A1XB1=C1,A2XB2=C2的解、极小范数解及其最佳逼近解的方法。     

求矩阵方程AXB=C的双对称最小二乘解的迭代算法

基于求解线性代数方程组的共轭梯度法的思想,通过特殊的变形与近似处理,建立了求矩阵方程AXB=C的双对称最小二乘解的迭代算法,并证明了迭代算法的收敛性.不考虑舍入误差时,迭代算法能够在有限步计算之后得到矩阵方程的双对称最小二乘解;选取特殊的初始矩阵时,还能够求得矩阵方程的极小范数双对称最小二乘解.同时,也能够给出指定矩阵的最佳逼近双对称矩阵.算例表明,迭代算法是有效的.     

求多变量线性矩阵方程组自反解的迭代算法

利用矩阵分解的方法求多变量线性矩阵方程组的自反解是很困难的.本文建立了一种迭代方法来解决这个问题,利用此迭代方法可以判断多变量线性矩阵方程组的可解性,且当矩阵方程组相容时,可以在有限步迭代后得到其自反解.选取特殊的初始矩阵时,能够求得矩阵方程组的极小范数自反解.进一步,通过求新的线性矩阵方程组的极小范数自反解,能够求得给定矩阵的最佳逼近矩阵.数值算例表明,迭代算法是有效的.     

一类离散时间代数Riccati矩阵方程对称解的双迭代算法

利用逆矩阵的Neumann级数形式,将在线性二次优化问题中遇到的含未知矩阵之逆的离散时间代数Riccati矩阵方程(DTARME)转化为高次多项式矩阵方程,然后采用牛顿算法求高次多项式矩阵方程的对称解,并采用修正共轭梯度法求由牛顿算法每一步迭代计算导出的线性矩阵方程的对称解或者对称最小二乘解,建立求DTARME的对称解的双迭代算法。双迭代算法仅要求DTARME有对称解,不要求它的对称解唯一,也不对它的系数矩阵做附加限定。数值算例表明双迭代算法是有效的。     

基于GPU-CUDA的共轭斜量法实现及性能对比

偏微分方程数值解法(包括有限差分法、有限元法)以及大量的数学物理方程数值解法最终都会演变成求解大型线性方程组。因此,探讨快速、稳定、精确的大型线性方程组解法一直是数值计算领域不断深入研究的课题且具有特别重要的意义。在迭代法中,共轭斜量法(又称共轭梯度法)被公认为最好的方法之一。但是,该方法最大缺点是仅适用于线性方程组系数矩阵为对称正定矩阵的情况,而且常规的CPU算法实现非常耗时。为此,通过将线性方程组系数矩阵作转换成对称矩阵后实施基于GPU-CUDA的快速共轭斜量法来解决一般性大型线性方程组的求解问题。试验结果表明:在求解效率方面,基于GPU-CUDA的共轭斜量法运行效率高,当线性方程组阶数超过3000时,其加速比将超过14;在解的精确性与求解过程的稳定性方面,与高斯列主元消去法相当。基于GPU-CUDA的快速共轭斜量法是求解一般性大型线性方程组快速而非常有效的方法。     

The use of matrix visualization in algorithmic design

The use of matrix visualization in the design and development of numerical algorithms for supercomputers is discussed. Using color computer graphics, numerical analysts can gain new insights into algorithm behavior, which can then be used to design more efficient (parallel) numerical algorithms. The application of a matrix visualization tool, MatVu, in the design of algorithms from numerical linear algebra is the primary focus. Specific examples include the derivation of optimal preconditioning matrices for a conjugate gradient method, the design of parallel hybrid algorithms for solving the symmetric eigenvalue problem, the effects of operator splitting in the solution of incompressible Navier-Stokes equations, and the monitoring of Jacobian matrices associated with the application of Newton's method to a corresponding nonlinear system of equations.     

An iterative algorithm for least squares problem in quaternionic quantum theory

Quaternionic least squares (QLS) problem is one method of solving overdetermined sets of quaternion linear equations AXB=E that is appropriate when there is error in the matrix E. In this paper, by means of real representation of a quaternion matrix, we introduce a concept of norm of quaternion matrices, which is different from that in [T. Jiang, L. Chen, Algebraic algorithms for least squares problem in quaternionic quantum theory, Comput. Phys. Comm. 176 (2007) 481-485; T. Jiang, M. Wei, Equality constrained least squares problem over quaternion field, Appl. Math. Lett. 16 (2003) 883-888], and derive an iterative method for finding the minimum-norm solution of the QLS problem in quaternionic quantum theory.     

An application of rationalized Haar functions to solution of linear partial differential equations

Rationalized Haar functions (RHFs, for short) are applied for solving linear first- and second-order partial differential equations (PDEs, for short). For this purpose, new operational matrices of integration and differentiation based on a double rationalized Haar series are derived. By using these operational matrices for their solution, the PDEs are transformed into matrix equations quite easily. Coefficients of double rationalized Haar series related to their solutions can be obtained by solving these matrix equations. Some numerical examples are also included.     

Explicit approximate inverse preconditioning techniques

Summary  The numerical treatment and the production of related software for solving large sparse linear systems of algebraic equations, derived mainly from the discretization of partial differential equation, by preconditioning techniques has attracted the attention of many researchers. In this paper we give an overview of explicit approximate inverse matrix techniques for computing explicitly various families of approximate inverses based on Choleski and LU—type approximate factorization procedures for solving sparse linear systems, which are derived from the finite difference, finite element and the domain decomposition discretization of elliptic and parabolic partial differential equations. Composite iterative schemes, using inner-outer schemes in conjunction with Picard and Newton method, based on approximate inverse matrix techniques for solving non-linear boundary value problems, are presented. Additionally, isomorphic iterative methods are introduced for the efficient solution of non-linear systems. Explicit preconditioned conjugate gradient—type schemes in conjunction with approximate inverse matrix techniques are presented for the efficient solution of linear and non-linear system of algebraic equations. Theoretical estimates on the rate of convergence and computational complexity of the explicit preconditioned conjugate gradient method are also presented. Applications of the proposed methods on characteristic linear and non-linear problems are discussed and numerical results are given.     

BCR method for solving generalized coupled Sylvester equations over centrosymmetric or anti-centrosymmetric matrix

This paper introduces another version of biconjugate residual method (BCR) for solving the generalized coupled Sylvester matrix equations over centrosymmetric or anti-centrosymmetric matrix. We prove this version of BCR algorithm can find the centrosymmetric solution group of the generalized coupled matrix equations for any initial matrix group within finite steps in the absence of round-off errors. Furthermore, a method is provided for choosing the initial matrices to obtain the least norm solution of the problem. At last, some numerical examples are provided to illustrate the efficiency and validity of methods we have proposed.     

一个反求Bezier曲面控制点的算法

本文将反求m×n次Bezier曲面控制点问题,转化为求解m+1个n+1阶线性方程组和n+1个m+1阶线性方程组问题。这些线性方程组的系数矩阵是著名的Vandermonde矩阵。通过求解Vandermonde矩阵的逆矩阵,使CAD/CAM曲面造型中常常遇到的反求Bezier曲面控制点问题得到有效的解决。同时本文给出了一种求解Vandermonde矩阵的逆矩阵的方法。     

关于一般耦合矩阵方程的迭代对称解

本文构造了一个有效的迭代方法(CGL)去求解一般耦合矩阵方程的对称解.若一般耦合矩阵方程关于对称解相容,则对于任意给定的初始对称矩阵组,利用所构造的迭代算法,都能在有限步迭代出所求问题的一组对称解,若选用一些特殊的初值,则可获得矩阵方程的极小范数对称解.最后的数值例子表明了所给算法的有效性.     

Normalized explicit finite element approximate inverse preconditioning

Normalized explicit approximate inverse matrix techniques for computing explicitly various families of normalized approximate inverses based on normalized approximate factorization procedures for solving sparse linear systems, which are derived from the finite difference and finite element discretization of partial differential equations are presented. Normalized explicit preconditioned conjugate gradient-type schemes in conjunction with normalized approximate inverse matrix techniques are presented for the efficient solution of linear and non-linear systems. Theoretical estimates on the rate of convergence and computational complexity of the normalized explicit preconditioned conjugate gradient method are also presented. Applications of the proposed methods on characteristic linear and non-linear problems are discussed and numerical results are given.