具最小二乘谱约束的结构矩阵逼近问题及其扰动分析

从两个方面讨论具有最小二乘谱约束的对称斜哈密尔顿矩阵的逼近问题:(Ⅰ)研究使AX-XA的Frobenius范数最小的n阶实对称斜哈密尔顿矩阵A的集合C,其中X,A分别是特征向量和特征值矩阵, (Ⅱ)求(A)∈c使得‖C-(A)‖=min ‖C-A‖,这里‖·‖是Frobenius范数.给出了C的元素的一般表达式和(A)的显示表达式,分析了该最佳逼近矩阵A的扰动理论,并给出了数值实验.     

等式约束加权线性最小二乘问题的解法

1 引言 在实际应用中常会提出解等式约束加权线性最小二乘问题 min||b-Ax||_M,(1.1) x∈C~n s.t.Bx=d, 其中B∈C~(p×n),A∈C~(q×n),d∈C~p,b∈C~q,M∈C~(q×q)为Hermite正定阵. 对于问题(1.1),目前已有多种解法,见文[1—3).本文将利用广义逆矩阵的知识,给出(1.1)的通解及迭代解法.本文中关于矩阵广义逆与投影算子(矩阵)的记号基本上与文[4]的相同.例如,A~+表示A的MP逆,P_L表示到子空间L上的正交投影算子,λ_(max)(MAY)表示矩阵M~(1/2)AY的最大特征值.我们还要用到广义BD逆的概念: 设A∈C~(n×n),L为C~n的子空间,则称A_(L)~(+)=P_L(AP_L+P_L⊥)~+为A关于L的广义BD逆.     

加权广义逆、加权最小二乘和约束最小二乘问题

本文采用如下记号:记C~m×n是具有复数域的m×n长方矩阵的集合,C~m=C~m×1是m维向量的集合.对A∈C~m×n称A~H∈C~m×n是A的共轭转置矩阵,rank(A)表示A的秩,R(A)和N(A)分别为A的值域和零空间,||·||=||·||2和||·||F分别为2-范数和Frobenius范数;I表示恒等矩阵.人们在研究数学规划、数值分析、数据处理,散射理论和电磁学等领域中都将问题归纳为如下的最小二乘问题:     

解等式约束加权线性最小二乘问题的矩阵校正方法

1 引言 在实际应用中常会提出解等式约束加权线性最小二乘问题 min(b_2-A_2x)~TW(b_2-A_2x) x∈R~n (1) s.t.A_1x=b_1,其中A_1∈R~(p×n),A~2∈R(q×n),b_1∈R~p,b_2∈R~q,W∈R(q×q)为对称正定矩阵. 对于问题(1),目前已有多种数值求解方法,如Paige利用(1)的对偶公式给出了一个向后稳定的数值方法.Gulliksson和Wedin利用加权QR分解技巧给出了解(1)的一个直接解法.作者利用广义Cholesky分解构造了解(1)的矩阵分解方法.     

等式约束不定最小二乘问题的双曲MGS消去算法

众所周知,加权法是解等式约束不定最小二乘问题的方法之一.通过探讨极限意义下,双曲MGS算法解对应加权问题的本质,得到一类消去算法.实验表明,该算法以和文献中现有的GHQR算法达到一样的精度,但实际计算量只需要GHQR算法的一半.     

退化椭圆问题的最小二乘混合元逼近

１．引言我们将考虑具有退化系数的椭圆问题其中 Ω为 ＩＲ２中的一个凸多边形区域,定义为这里的ｇ＞０为分片线性连续函数．从物理背景来看,问题（１．１）来源于轴对称共振结构中的电磁场研究．Ｍａｒｉｎｉ,Ｐｉｅｔｒａ（１９９５）［４］研究了问题（１．１）的混合有限元逼近,并得到了最优误差估计． 此文,我们采用一种新的混合元,即最小二乘混合元方法［５］;对退化问题（１．１）进行逼近,利用插值投影证明了近似解具有最优阶精度的收敛性．比较起经典混合元方法来,最小二乘混合元方法有两个优越性：有限元空间不必满足Ｌ…     

两种块校正最小二乘问题的代数关系

1 引言 在求解工程问题中,我们常常应用最小二乘方法 min‖Ax-b‖_2,A∈R~(m×n),m≥n (1.1) x∈R~n去得到问题的数值近似解或估计系统的未知参数.我们常常已知(1)的解,而希望求解修改问题     

广义次对称矩阵反问题的最小二乘解

讨论了广义次对称矩阵反问题的最小二乘解,得到了解的一般表达式,并就该问题的特殊情形:矩阵反问题,得到了可解的充分必要条件及解的通式.此外,证明了最佳逼近问题解的存在唯一性,并给出了其解的具体表达式.     

非线性最小二乘问题的一种迭代解法

本文给出了求解非线性最小二乘问题的一种迭代解法 ,即由已知节点数据 (xi,yi) (i=1 ,2 ,… ,m)求函数 y=f(x,b1,b2 ,… ,bn)中非线性参数 b1,b2 ,… ,bn 的一种迭代解法 .并用实际算例的结果说明了该迭代解法优于一般线性化方法 ,说明了该种方法在实际工程领域中的应用     

TLS和LS问题的比较

There are a number of articles discussing the total least squares(TLS) and the least squares(LS) problems.M.Wei(M.Wei, Mathematica Numerica Sinica 20(3)(1998),267-278) proposed a new orthogonal projection method to improve existing perturbation bounds of the TLS and LS problems.In this paper,wecontinue to improve existing bounds of differences between the squared residuals,the weighted squared residuals and the minimum norm correction matrices of the TLS and LS problems.     

A SUCCESSIVE LEAST SQUARES METHOD FOR STRUCTURED TOTAL LEAST SQUARES

A new method for Total Least Squares (TLS) problems is presented. It differs from previous approaches and is based on the solution of successive Least Squares problems.The method is quite suitable for Structured TLS (STLS) problems. We study mostly the case of Toeplitz matrices in this paper. The numerical tests illustrate that the method converges to the solution fast for Toeplitz STLS problems. Since the method is designed for general TLS problems, other structured problems can be treated similarly.     

A NONMONOTONE TRUST REGION METHOD FOR NONLINEAR LEAST SQUARES PROBLEMS

In this paper we present a nonmonotone trust region method for nonlinear least squares problems with zero-residual and prove its convergence properties. The extensive numerical results are reported which show that the nonmonotone trust region method is generally superior to the usual trust region method.     

A NEW SECANT METHOD FOR NONLINEAR LEAST SQUARES PROBLEMS

This paper is concerned with the solution of the nonlinear least squares problems. A new secant method is suggested in this paper, which is based on an affine model of the objective function and updates the first-order approximation each step when the iterations proceed. We present an algorithm which combines the new secant method with Gauss-Newton method for general nonlinear least squares problems. Furthermore, we prove that this algorithm is Q-superlinearly convergent for large residual problems under mild conditions.     

ON THE SEPARABLE NONLINEAR LEAST SQUARES PROBLEMS

Separable nonlinear least squares problems are a special class of nonlinear least squares problems, where the objective functions are linear and nonlinear on different parts of variables. Such problems have broad applications in practice. Most existing algorithms for this kind of problems are derived from the variable projection method proposed by Golub and Pereyra, which utilizes the separability under a separate framework. However, the methods based on variable projection strategy would be invalid if there exist some constraints to the variables, as the real problems always do, even if the constraint is simply the ball constraint. We present a new algorithm which is based on a special approximation to the Hessian by noticing the fact that certain terms of the Hessian can be derived from the gradient. Our method maintains all the advantages of variable projection based methods, and moreover it can be combined with trust region methods easily and can be applied to general constrained separable nonlinear problems. Convergence analysis of our method is presented and numerical results are also reported.     

ON THE ACCURACY OF THE LEAST SQUARES AND THE TOTAL LEAST SQUARES METHODS

Consider solving an overdetermined system of linear algebraic equations by both the least squares method (LS) and the total least squares method (TLS). Extensive published computational evidence shows that when the original system is consistent. one often obtains more accurate solutions by using the TLS method rather than the LS method. These numerical observations contrast with existing analytic perturbation theories for the LS and TLS methods which show that the upper bounds for the LS solution are always smaller than the corresponding upper bounds for the TLS solutions. In this paper we derive a new upper bound for the TLS solution and indicate when the TLS method can be more accurate than the LS method.Many applied problems in signal processing lead to overdetermined systems of linear equations where the matrix and right hand side are determined by the experimental observations (usually in the form of a lime series). It often happens that as the number of columns of the matrix becomes larger, the ra     

线性子空间上求解AX=B的最小二乘问题的迭代算法

应用共轭梯度方法和线性投影算子,给出迭代算法求解了线性矩阵方程AX=B在任意线性子空间上的最小二乘解问题.在不考虑舍入误差的情况下,可以证明,所给迭代算法经过有限步迭代可得到矩阵方程AX=B的最小二乘解、极小范数最小二乘解及其最佳逼近.文中的数值例子证实了该算法的有效性.     

EXTENDED OBLIQUE PROJECTION METHOD FOR GENERALIZED LEAST SQUARES PROBLEM

We extend the oblique projection method given by Y.Saad to solve the generalized least squares problem. The corresponding oblique projection operator is presented and the convergence theorems are proved. Some necessary and sufficient conditions for computing the solution or the minimum N-norm solution of the min || A x- b ||M2 have been proposed as well.     

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.