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《数学的实践与认识》2013,(16)
通过介绍一个含四个位势的4×4矩阵谱问题,得到一个新的非线性发展方程族,其中较有意义的一个方程是耦合Kaup-Newell方程.利用迹恒等式,得到了它的双哈密顿结构.在某个约束条件下,通过特征值问题的非线性化方法,得到了Liouville意义下耦合Kaup-Newell方程新的可积分解. 相似文献
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1引言子矩阵约束下的矩阵方程问题是指限定矩阵方程的解X的一个子矩阵X_(0),然后在某个约束集合中求解矩阵方程.如求满足X([1:q])=X_(0)的对称解,这里X([1:q])表示矩阵X的q阶顺序主子阵.子矩阵约束下的矩阵方程问题来源于实际中的系统扩张问题[1],有一定的实际意义和重要性,受到了许多学者的关注,如[2-4]中,彭分别研究了子矩阵约束条件下实矩阵方程AX=B的实矩阵解,中心对称解和双对称解. 相似文献
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1 引言 约束矩阵方程问题就是在满足一定条件的矩阵集合中求矩阵方程的解,不同的矩阵方程或不同的约束条件都将导致不同的约束矩阵方程问题.早在1989年戴华就提出了线性约束条件下矩阵束的最佳逼近及其应用问题.此类问题在最优化设计、参数识别、自动控制、图像复原等许多科学计算领域有着广泛应用.迄今,针对该类问题中解矩阵属于同类矩阵集合的情形(同类约束解问题),中外学者已用奇异值分解、标准相关分解、 相似文献
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在共轭梯度思想的启发下,结合线性投影算子,给出迭代算法求解了线性矩阵方程AXB+CYD=E的M对称解[X,Y]及其最佳逼近.当矩阵方程AXB+CYD=E有M对称解时,应用迭代算法,在有限的误差范围内,对任意初始M对称矩阵对[X_,Y_1],经过有限步迭代可得到矩阵方程的M对称解;选取合适的初始迭代矩阵,还可得到极小范数M对称解.而且,对任意给定的矩阵对[X,Y],矩阵方程AXB+CYD=E的最佳逼近可以通过迭代求解新的矩阵方程AXB+CYD=E的极小范数M对称解得到.文中的数值例子证实了该算法的有效性. 相似文献
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在共轭梯度思想的启发下,本文给出了迭代算法求解约束矩阵方程AXB+CXD=F的对称解及其最佳逼近.应用迭代算法,矩阵方程AXB+CXD=F的相容性可以在迭代过程中自动判断.当矩阵方程AXB+CXD=F有对称解时,在有限的误差范围内,对任意初始对称矩阵X1,运用迭代算法,经过有限步可得到矩阵方程的对称解;选取合适的初始迭代矩阵,还可以迭代出极小范数对称解.而且,对任意给定的矩阵X0,矩阵方程AXB+CXD=F的最佳逼近对称解可以通过迭代求解新的矩阵方程A(X)B+C(X)D=(F)的极小范数对称解得到.文中的数值例子证实了该算法的有效性. 相似文献
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提出一种求解线性矩阵方程AX+XB=C双对称解的迭代法.该算法能够自动地判断解的情况,并在方程相容时得到方程的双对称解,在方程不相容时得到方程的最小二乘双对称解.对任意的初始矩阵,在没有舍入误差的情况下,经过有限步迭代得到问题的一个双对称解.若取特殊的初始矩阵,则可以得到问题的极小范数双对称解,从而巧妙地解决了对给定矩... 相似文献
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本文研究Catalan矩阵方程和另一个类似的矩阵方程在GL2(Z)上的可解性,并且得到了它们在GL2(Z)上的所有解. 相似文献
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Unified Gradient Approach to Performance Optimization Under a Pole Assignment Constraint 总被引:1,自引:0,他引:1
General closed-loop performance optimization problems with pole assignment constraint are considered in this paper under a unified framework. By introducing a free-parameter matrix and a matrix function based on the solution of a Sylvester equation, the constrained optimization problem is transformed into an unconstrained one, thus reducing the problem of closed-loop performance optimization with pole placement constraint to the computation of the gradient of the performance index with respect to the free-parameter matrix. Several classical performance indices are then optimized under the pole placement constraint. The effectiveness of the proposed gradient method is illustrated with an example. 相似文献
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利用矩阵的奇异值分解及广义逆,给出了矩阵约束下矩阵反问题AX=B有实对称解的充分必要条件及其通解的表达式.此外,给出了在矩阵方程的解集合中与给定矩阵的最佳逼近解的表达式. 相似文献
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61.IntroductionTheconsiderableprogresseshavebeenmadeinthetheoryofmatricesoverskewfields-However,sofar,wehavenotseenanyofstudyresultsofthe1eastsquareproblemofmatricesoverskewfields.Thematrixequation'AX=B,(l)whichisveryimportant,wereinvestigateddeeplyin[1~3J.lnthispaper,wedefineanormofarealquaternionmatrix,giveexpressionsoftheleastsquaresolutionsofthequaternionma-trixequation(l)andtheequationwiththeconstraintconditionDX=E.Throughoutthispaper,wedenotetherealquaternionfieldbyH,thesetofallmXnma… 相似文献
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We consider a control system described by a nonlinear second order evolution equation defined on an evolution triple of Banach spaces (Gelfand triple) with a mixed multivalued control constraint whose values are nonconvex closed sets. Alongside the original system we consider a system with the following control constraints: a constraint whose values are the closed convex hull of the values of the original constraint and a constraint whose values are extreme points of the constraint which belong simultaneously to the original constraint. By a solution to the system we mean an admissible trajectory-control pair. In this part of the article we study existence questions for solutions to the control system with various constraints and density of the solution set with nonconvex constraints in the solution set with convexified constraints. 相似文献
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We present algorithms for solving quadratically constrained linear least squares problems that do not necessarily require expensive dense matrix factorizations. Instead, only black box solvers for certain related unconstrained least squares problems, as well as the solution of two related linear systems involving the coefficient matrixA and the constraint matrixB, are required. Special structures in the problem can thus be exploited in these solvers, and iterative as well as direct solvers can be used. Our approach is to solve for the Lagrange multiplier as the root of an implicitly-defined secular equation. We use both a linear and a rational (Hebden) local model and a Newton and secant method. We also derive a formula for estimating the Lagrange multiplier which depends on the amount the unconstrained solution violates the constraint and an estimate of the smallest generalized singular value ofA andB. The Lagrange multiplier estimate can be used as a good initial guess for solving the secular equation. We also show conditions under which this estimate is guaranteed to be an acceptable solution without further refinement. Numerical results comparing the different algorithms are presented.Research supported by SRI International and by the National Science Foundation under grants DMS-87-14612 and ASC 9003002. 相似文献
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Zhen‐Yun Peng 《Numerical Linear Algebra with Applications》2008,15(4):373-389
In this paper, two new matrix‐form iterative methods are presented to solve the least‐squares problem: and matrix nearness problem: where matrices and are given; ??1 and ??2 are the set of constraint matrices, such as symmetric, skew symmetric, bisymmetric and centrosymmetric matrices sets and SXY is the solution pair set of the minimum residual problem. These new matrix‐form iterative methods have also faster convergence rate and higher accuracy than the matrix‐form iterative methods proposed by Peng and Peng (Numer. Linear Algebra Appl. 2006; 13 : 473–485) for solving the linear matrix equation AXB+CYD=E. Paige's algorithms, which are based on the bidiagonalization procedure of Golub and Kahan, are used as the framework for deriving these new matrix‐form iterative methods. Some numerical examples illustrate the efficiency of the new matrix‐form iterative methods. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Qiu Yuyang Qiu Chunhan 《高校应用数学学报(英文版)》2007,22(4):441-448
The matrix equation AXB = E with the constraint PX=sXP is considered,where P is a given Hermitian matrix satisfying p~2=I and s=±1.By an eigenvalue decomposition of P,the constrained problem can be equivalently transformed to a well-known unconstrained problem of matrix equation whose coefficient matrices contain the corresponding eigenvector, and hence the constrained problem can be solved in terms of the eigenvectors of P.A simple and eigenvector-free formula of the general solutions to the constrained problem by generalized inverses of the coefficient matrices A and B is presented.Moreover,a similar problem of the matrix equation with generalized constraint is discussed. 相似文献