由谱数据数值稳定地构造实对称带状矩阵

§1.引言 设r,n是正整数并且0r有a_(ij)=0.     

一般二次规划问题的交换型零空间方法

一般二次规划问题的形式为:QP:min{f(x)=1/2x~TGx+c~Tx|a_i~Tx≥b_i 1≤i≤m},(1.1)其中 x,c,a_i∈E~n,b_i∈E~1,i=1,2,…,m;G 为 n 阶对称矩阵;“T”表示转置运算.设 x~k∈R={x|a_i~Tx≥b_i,1≤i≤m}.若 a_i~Tx~k=b_i 成立,则称约束 a_i~Tx≥b_i 在x~k 点有效.记:I_k={i|a_i~Tx~k=b_i,1≤i≤m},A_k={a_i|i∈I_k}.以后当不加区别地使用术语“有效集”时,视实际背景或指 I_k 或指 A_k,或指在 x~k 点有效的约束条件的集合.设 A_k 是 n×t_k 的满秩矩阵,Z_k 为 A_k 的零空间     

一个不等式链与一个连等式

本文拟出初等代数中一个新的不等式链,并获得一连等式。设a_1,a_2,…,a_n均是正实数,n≥2,且sum from i=1 to n a_i=n。记f(k)=1 a_k a_ka_(k 1) … a_ka_(k 1)·…·a_na_1·…·a_(k-2);f_i(k)表示和f(k)(自左至右)的第i个和项,i=1,2,…,n。令S_i=sum from i=1 to n (f_i(k)/f(k)),i=1,2,…,n, 则有不等式链     

GMRES方法的收敛率

1 引 言 GMRES方法是目前求解大型稀疏非对称线性方程组 Ax=b,A∈R~(n×n);x,b∈R~n (1)最为流行的方法之一.设x~((0))是(1)解的初始估计,r~((0))=b-Ax~((0))是初始残量,K_k=span{r~((0)),Ar~((0)),…A~(k-1)r~((0))}为由r~((0))和A产生的Krylov子空间.GMRES方法的第k步     

关于矩阵切触有理插值

1 矩阵切触插值连分式 设实区间[a,b]中由不同点组成的插值结点为x_1,x_2,…,x_n,它们的重数分别为a_1,a_2,… ,a_n,M=sum from i=l to n(a_i-1),与之对应的待插值矩阵集为 {A_i~(k):k=0,1,…,a_i-1,i=1,2,…,n,A_i~(k)=A~(k)(x_i)∈R~(d×d)}. 设方阵A=(a_(ij)),它的广义矩阵逆定义为 A~(-1)= A/‖A‖~2 (A≠0) (1.1)     

常系数非齐线性递推式的解的显式表示

本文给出常系数非齐线性递推式(?)的解的显式表达式 H(m)=sum from i=0 to k-1(sum from j=i to k-1 b_ja_(k-j+i))D_(m-k-i)+sum from i=0 to m-k D_if(m-i)(m≥k)其中D_m=sum x_1+2x_2+…+kx_k=m x_j≥0(i=1,2,…,k)(?)a_1~x1a_2~x2…a_k~xk.     

分式线性迭代数列的敛散性及收敛速度

若a_i,b_i0(i=1,2),|a_1 a_2b_1 b_2|≠0,则数列x_10,x_(n+1)=a_1x_n+a_2/b_1x_n+b_2收敛.若迭代过程中,xn(n=1,2,…)全不是φ(x)=a1x+a2/b1x+b2的不动点,则迭代数列{xn}线性收敛.     

布尼雅可夫斯基不等式的推广

定理:不等式 (sum from i=1 to m(a_(1i) a_(2i)…a_(ni)))~n≤≤sum from i=1 to m(a_(1i))~n sum from i=1 to m(a_(2i))~n…sum from i=1 to m(a_(ni))~n對於任意自然數n都成立,其中a_(ki)為正數(K=1,2,…,n,i=1,2,…,m). 證明: 設 A_K~n=sum from i=1 to m(a_(Ki))~n (K=1,2,…,n), x_(Ki)=a_(Ki)/A_K,(K=1,2,…,n i=1,2,…,m)則從n侗正數的幾何平均值小於或等於其算術平均值這個結果可得 x_(1i)x_(2i)…x_(ni)≤((x_(1i))~n+(x_(2i))~n+…+(x_(ni))~n)/n由此更推得a_(1i)a_(2i)…a_(ni)=A_1A_2…A_n(x_(1i)x_(2i)…x_(ni)≤     

代数特征值反问题可解的充分条件

本文讨论如下代数特征值反问题的可解性:问题G.设A=(a_(ij))和A_k=(a_(ij)~((k)))(k=1,…,n)是一组n+1个n×n实矩     

关於高維射影空間共軛网論的研究(Ⅱ)

<正> 本文是继前文[1]来討論n維射影空間S_n(n≥4)的共軛网有关的一些性貭,特別是第k类共軛和調和性貭.我們已經闡明,当k=1时,这些性貭变为普通共軛性貭和調和性貭.这里,很自然地发生一个問題:当一个拉普拉斯叙列{…X_3X_1X_2X_4…}是另一个拉普拉斯叙列{…A_3A_1A_2A_4…}的第k类內接叙列吋,能不能在这两个之間嵌入k-1个(k>1)拉普拉斯叙列{…A_3~((h))A_1~((h))A_2~((h))A_4~((h))…}(h=1,2,…,k-1),使一个內接着一个而且最后的一个內接于{…A_3A_1A_2A_4…}呢?我們将証明,問題中的嵌入完全可能,这     

解非线性方程组的一类离散的Newton算法

1．引言考虑非线性方程组设xi是当前的迭代点，为计算下一个迭代点，Newton法是求解方程若用差商代替导数，离散Newton法要解如下的方程其中这里为了计算J（；；h），需计算n‘个函数值．为了提高效能，Brown方法l‘］使用代入消元的办法来减少函数值计算量．它是再通过一次内选代从h得到下一个迭代点14＋1．设n；＝（《1，…，Zn尸，t二（ti，…，t＊”，t为变量．BfOWll方法的基本思想如下．对人（x）在X；处做线性近似解出然后代入第二个函数，得到这是关于tZ，…，tn的函数．当（tZ，…，t。尸一（ZZ，…，Z。厂时，由（1．4），…     

A class of difference ABS-type algorithms for a nonlinear system of equations

In this paper we give a class of algorithms for solving nonlinear algebraic equations using difference approximations of derivatives. The class is a modification of the original ABS class with the advantage of requiring less function evaluations. Special cases include the methods of Brown and Brent and the discretized Newton method, which is formulated in a way requiring fewer function evaluations per iteration.     

Numerical Solution of A Class of Systems of Volterra Polynomial Equations of the First Kind

In this paper, a class of second-order systems of Volterra nonlinear integral equations is considered. This class is related to a problem of automatic control of a dynamic object with vector inputs and outputs. A numerical solution technique based on the Newton–Kantorovich method is considered. To verify the efficiency of the algorithms developed, a series of test calculations are carried out.     

Local convergence analysis of several inexact Newton-type algorithms for general nonlinear eigenvalue problems

We study the local convergence of several inexact numerical algorithms closely related to Newton’s method for the solution of a simple eigenpair of the general nonlinear eigenvalue problem $T(\lambda )v=0$ . We investigate inverse iteration, Rayleigh quotient iteration, residual inverse iteration, and the single-vector Jacobi–Davidson method, analyzing the impact of the tolerances chosen for the approximate solution of the linear systems arising in these algorithms on the order of the local convergence rates. We show that the inexact algorithms can achieve the same order of convergence as the exact methods if appropriate sequences of tolerances are applied to the inner solves. We discuss the connections and emphasize the differences between the standard inexact Newton’s method and these inexact algorithms. When the local symmetry of $T(\lambda )$ is present, the use of a nonlinear Rayleigh functional is shown to be fundamental in achieving higher order of convergence rates. The convergence results are illustrated by numerical experiments.     

Convergence of a generalized Newton and an inexact generalized Newton algorithms for solving nonlinear equations with nondifferentiable terms

In this paper, we consider two versions of the Newton-type method for solving a nonlinear equations with nondifferentiable terms, which uses as iteration matrices, any matrix from B-differential of semismooth terms. Local and global convergence theorems for the generalized Newton and inexact generalized Newton method are proved. Linear convergence of the algorithms is obtained under very mild assumptions. The superlinear convergence holds under some conditions imposed on both terms of equation. Some numerical results indicate that both algorithms works quite well in practice.      

求解非线性互补问题的逐次逼近阻尼牛顿法

针对非线性互补问题,提出了与其等价的非光滑方程的逐次逼近阻尼牛顿法,并 在一定条件下证明了该算法的全局收敛性.数值结果表明,这一算法是有效的.     

Two-step relaxation Newton algorithm for solving nonlinear algebraic equations

We introduce a new algorithm, namely two-step relaxation Newton, for solving algebraic nonlinear equations f(x)=0. This new algorithm is derived by combining two different relaxation Newton algorithms introduced by Wu et al. (Appl. Math. Comput. 201:553–560, 2008), and therefore with special choice of the so called splitting function it can be implemented simultaneously, stably with much less memory storage and CPU time compared with the Newton–Raphson method. Global convergence of this algorithm is established and numerical experiments show that this new algorithm is feasible and effective, and outperforms the original relaxation Newton algorithm and the Newton–Raphson method in the sense of iteration number and CPU time.     

Some iterative methods for the largest positive definite solution to a class of nonlinear matrix equation

In this paper, we propose some inversion-free iteration methods for finding the largest positive definite solution of a class of nonlinear matrix equation. Then, we consider the properties of the solution for this nonlinear matrix equation. Also, we establish Newton’s iteration method for finding the largest positive definite solution and prove its quadratic convergence. Furthermore, we derive the semi-local convergence of the Newton’s iteration method. Finally, some numerical examples are presented to illustrate the effectiveness of the theoretical results and the behavior of the considered methods.     

On an Aitken–Newton type method

We study the solving of nonlinear equations by an iterative method of Aitken type, which has the interpolation nodes controlled by the Newton method. We obtain a local convergence result which shows that the q-convergence order of this method is 6 and its efficiency index is $\sqrt[5]{6},$ which is higher than the efficiency index of the Aitken or Newton methods. Monotone sequences are obtained for initial approximations farther from the solution, if they satisfy the Fourier condition and the nonlinear mapping satisfies monotony and convexity assumptions on the domain.     

Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration

The Newton iteration is basic for solving nonlinear optimization problems and studying parameter estimation algorithms. In this letter, a maximum likelihood estimation algorithm is developed for estimating the parameters of Hammerstein nonlinear controlled autoregressive autoregressive moving average (CARARMA) systems by using the Newton iteration. A simulation example is provided to show the effectiveness of the proposed algorithm.