非线性方程组的不精确仿射牛顿法

1 引言众所周知,对于非线性方程组问题 F(x)=0 F:Rn→Rn (1) 经典的牛顿法从给出一个初始点x0之后,计算第k步迭代点xk及步长sk:     

关于一类Hermite-Fejér插值算子的平均收敛

本文给出基于{xk}_(k=0)~(n+1)的Hermite-Fejér插值算子平均收敛的一些新结论,这里x0=1,xn+1=-1,xk（k=1,2,…,n）是n阶Jacobi多项式的零点.     

关于一类Hermite-Fejér插值算子的平均收敛

本文给出基于{xk}_(k=0)~(n+1)的Hermite-Fejér插值算子平均收敛的一些新结论,这里x0=1,xn+1=-1,xk（k=1,2,…,n）是n阶Jacobi多项式的零点.     

BroWn-Broyden修正算法

1　引　　言求解非线性方程组F(x) =f1 (x1 ,… ,xn)廸n(x1 ,… ,xn)=0　　 F:D Rn→ Rn,(1.1)的 Brown方法 ,是将广义的 L U分解用于 Newton迭代过程 ,而形成的一类具有内外迭代形式的有效算法 .这类算法的特点是每步迭代的函数计算量仅仅为 Newton法的一半 ,而收敛速度则与 Newton法相同 .因此 ,按 Ostrowskii定义的效率指数去衡量 ,Brown方法为一效率较高的算法之一 ,是倍受推崇的 .本文 ,采用修正算法的思想 ,对 Brown方法作进一步改造 ,在不破坏原来的内外迭代形式下 ,使算法在每步迭代中的函数计值量由原来的 O(n2 )下降到 O(…     

无正则性条件下的一个信赖域方法的全局收敛性

1.引 言考虑下列等式约束最优化问题:min f(x)x∈Rn (1.1)s.t.C(x)=0其中f:Rn→R,C（x）=（c1（x）,C2（x）,…,Cm（x））T,Ci:Rn→R,（i=1,…,m）.我们假设f（x）,Ci（x）（i=1,2,…,m）是连续可微函数.令g（x）=　f（x）,A（x）=　C（x）T.为了方便,我们通常用 Ck,fk,gk,Ak分别表示 C（xk）,f（xk）,g（xk）A（xk）. SQP方法是一迭代方法.在 xk点,通过解下列子问题来得到搜索方向 dk     

带扰动项的FR共轭梯度法

本文提出了两种搜索方向带有扰动项的Fletcher-Reeves (abbr. FR)共轭梯度法.其迭代公式为xk 1=xk αk(sk ωk),其中sk由共轭梯度迭代公式确定,ωk为扰动项,αk采用线搜索确定而不是必须趋于零.我们在很一般的假设条件下证明了两种算法的全局收敛性,而不需要目标函数有下界或水平集有界等有界性条件.     

非线性方程组的Newton流线法

为求解非线性方程组F(x)=0, 研究了Newton流方程x_t=V(x)=-(DF(x))^-1F(x),x(0)=x⁰,及数值Newton流x^j+1=x^j+hV(x^j),h∈(0,1].导出了减幅指标g_j(h)=||F(x^j+1)||/||F(x^j)||=1-h+h²d_jh<1和m重根x^*附近的表示g_j(h)=(1-h/m)^m+h²O(||x^j-x^*||).最后基于4个可计算量g_j,d_j,K_j,q_j,提出了新的Newton流线法,如果投入大量的随机初始点, 能找到所有实根、重根和复根.     

新二元三角插值多项式的构造

设Ω=[-πxπ,-πyπ],C(Ω)表示关于x,y均以2π为周期的连续函数空间.若f(x,y)∈C(Ω),取结点组为(xk,yl)=(2k+2n 1)π,(2l 2+m 1)πk=0,1,2,…,2n,l=0,1,2,…,2m,则我们获得一个二元三角插值多项式Cn,m(f;x,y)=M1N∑k=2n0∑l=2m0f(xk,yl).1+2∑nα=1cosα(x-xk)+2∑mβ=1cosβ(y-yl)+4∑nα=1∑mβ=1cosα(x-xk)cosβ(y-yl)其中M=2m+1,N=2n+1.为改进其收敛性,本文构造一个新的因子ρα,β,使得带有该因子ρα,β的二元三角插值多项式Ln,m(f;x,y)可以在全平面上一致地收敛到每个连续的f(x,y),且具有最佳逼近阶.     

一类加强不等式的推广

本文给出了 n个正数 x1 ,x2 ,… ,xn 的如下不等式 :∏nk=1( xαk+x-αk )≥ ( Aαn( x) +A-αn ( x) ) n ,每个 xk≤ xα,∏nk=1( xαk+x-αk )≤ ( Aαn( x) +A-αn ( x) ) n ,每个 xk≥ e.其中 α>0 ,xα=[4α2 +1 +2 α]12α ,常数 e=2 .71 81 82 81 8… ,An( x) =1n∑nk=1xk.     

阻尼牛顿法迭代中病态问题趋于稳定的渐变(瞬变)过程分析

<正>1引言设映像F:DR~n→R~n,考虑非线性方程组F(x)=0,x∈DR~n,其中F(x)=(f_1(x),f_2(x),…,f_n(x))T,分量f_i(x):R~n→R(i=1,2,…,n)是连续可微实值函数.目前,非线性方程组求解的数值方法有牛顿法、同伦型法、单纯形法与胞腔排除法等[1]~[3]牛顿法是一种非常实用的计算方法,迭代公式如下x=x+p,(2)其中x为前次迭代近似,x为紧接着x后的迭代近似,p=-[F'(x)]~(-1)F(x)为牛顿修正,F'(x)为x处的雅可比矩阵.     

Convergences of splitting iterative methods for symmetric indefinite linear systems

In this paper, we generalize the saddle point problem to general symmetric indefinite systems, we also present a kind of convergent splitting iterative methods for the symmetric indefinite systems. A special divergent splitting is introduced. The sufficient condition is discussed that the eigenvalues of the iteration matrix are real. The spectral radius of the iteration matrix is discussed in detail, the convergence theories of the splitting iterative methods for the symmetric indefinite systems are obtained. Finally, we present a preconditioner and discuss the eigenvalues of preconditioned matrix.     

Uzawa methods for a class of block three‐by‐three saddle‐point problems

In this work, we consider numerical methods for solving a class of block three‐by‐three saddle‐point problems, which arise from finite element methods for solving time‐dependent Maxwell equations and some other applications. The direct extension of the Uzawa method for solving this block three‐by‐three saddle‐point problem requires the exact solution of a symmetric indefinite system of linear equations at each step. To avoid heavy computations at each step, we propose an inexact Uzawa method, which solves the symmetric indefinite linear system in some inexact way. Under suitable assumptions, we show that the inexact Uzawa method converges to the unique solution of the saddle‐point problem within the approximation level. Two special algorithms are customized for the inexact Uzawa method combining the splitting iteration method and a preconditioning technique, respectively. Numerical experiments are presented, which demonstrated the usefulness of the inexact Uzawa method and the two customized algorithms.     

A QMR-based interior-point algorithm for solving linear programs

A new approach for the implementation of interior-point methods for solving linear programs is proposed. Its main feature is the iterative solution of the symmetric, but highly indefinite 2×2-block systems of linear equations that arise within the interior-point algorithm. These linear systems are solved by a symmetric variant of the quasi-minimal residual (QMR) algorithm, which is an iterative solver for general linear systems. The symmetric QMR algorithm can be combined with indefinite preconditioners, which is crucial for the efficient solution of highly indefinite linear systems, yet it still fully exploits the symmetry of the linear systems to be solved. To support the use of the symmetric QMR iteration, a novel stable reduction of the original unsymmetric 3×3-block systems to symmetric 2×2-block systems is introduced, and a measure for a low relative accuracy for the solution of these linear systems within the interior-point algorithm is proposed. Some indefinite preconditioners are discussed. Finally, we report results of a few preliminary numerical experiments to illustrate the features of the new approach.     

Symmetric-triangular decomposition and its applications part II: Preconditioners for indefinite systems

As an application of the symmetric-triangular (ST) decomposition given by Golub and Yuan (2001) and Strang (2003), three block ST preconditioners are discussed here for saddle point problems. All three preconditioners transform saddle point problems into a symmetric and positive definite system. The condition number of the three symmetric and positive definite systems are estimated. Therefore, numerical methods for symmetric and positive definite systems can be applied to solve saddle point problems indirectly. A numerical example for the symmetric indefinite system from the finite element approximation to the Stokes equation is given. Finally, some comments are given as well. AMS subject classification (2000) 65F10     

对称不定系统的一类预处理子的注记

由于对称正定系统已有很多有效的求解方法,因此将对称的、或者非对称的不定系统转化为对称正定系统就成为解决这类问题的方法之一构造了一类简洁有效的预处理子,将对称不定系统转化为对称正定型,研究了所得预处理系统的谱性质,估计了其谱条件数,推广了现有结论.     

On block preconditioners for saddle point problems with singular or indefinite (1, 1) block

We discuss a class of preconditioning methods for the iterative solution of symmetric algebraic saddle point problems, where the (1, 1) block matrix may be indefinite or singular. Such problems may arise, e.g. from discrete approximations of certain partial differential equations, such as the Maxwell time harmonic equations. We prove that, under mild assumptions on the underlying problem, a class of block preconditioners (including block diagonal, triangular and symmetric indefinite preconditioners) can be chosen in a way which guarantees that the convergence rate of the preconditioned conjugate residuals method is independent of the discretization mesh parameter. We provide examples of such preconditioners that do not require additional scaling. Copyright © 2010 John Wiley & Sons, Ltd.     

Preconditioning Indefinite Systems in Interior Point Methods for Optimization

Every Newton step in an interior-point method for optimization requires a solution of a symmetric indefinite system of linear equations. Most of today's codes apply direct solution methods to perform this task. The use of logarithmic barriers in interior point methods causes unavoidable ill-conditioning of linear systems and, hence, iterative methods fail to provide sufficient accuracy unless appropriately preconditioned. Two types of preconditioners which use some form of incomplete Cholesky factorization for indefinite systems are proposed in this paper. Although they involve significantly sparser factorizations than those used in direct approaches they still capture most of the numerical properties of the preconditioned system. The spectral analysis of the preconditioned matrix is performed: for convex optimization problems all the eigenvalues of this matrix are strictly positive. Numerical results are given for a set of public domain large linearly constrained convex quadratic programming problems with sizes reaching tens of thousands of variables. The analysis of these results reveals that the solution times for such problems on a modern PC are measured in minutes when direct methods are used and drop to seconds when iterative methods with appropriate preconditioners are used.     

Detecting “dense” columns in interior point methods for linear programs

During the iterations of interior point methods symmetric indefinite systems are decomposed by LD̂L ^T factorization. This step can be performed in a special way where the symmetric indefinite system is transformed to a positive definite one, called the normal equations system. This approach proved to be efficient in most of the cases and numerically reliable, due to the positive definite property. It has been recognized, however, that in case the linear program contains “dense” columns, this approach results in an undesirable fill–in during the computations and the direct factorization of the symmetric indefinite system is more advantageous. The paper describes a new approach to detect cases where the system of normal equations is not preferable for interior point methods and presents a new algorithm for detecting the set of columns which is responsible for the excessive fill–in in the matrix AA ^T . By solving large–scale linear programming problems we demonstrate that our heuristic is reliable in practice. This work was supported in part by the Hungarian Scientific Research Fund OTKA K60480.     

Sparse matrices

One gives a survey of methods and programs for solving large sparse spectral problems based on the Lanczos algorithm. Practically all the important works on this topic are reflected in this survey. One also considers applications of the variants of the Lanczos method to the solution of symmetric indefinite systems of linear equations and to a series of other problems of linear algebra.Translated from Itogi Nauki i Tekhniki, Seriya Matematicheskii Analiz, Vol. 20, pp. 179–260, 1982.     

Minimum residual methods for augmented systems

For large systems of linear equations, iterative methods provide attractive solution techniques. We describe the applicability and convergence of iterative methods of Krylov subspace type for an important class of symmetric and indefinite matrix problems, namely augmented (or KKT) systems. Specifically, we consider preconditioned minimum residual methods and discuss indefinite versus positive definite preconditioning. For a natural choice of starting vector we prove that when the definite and indenfinite preconditioners are related in the obvious way, MINRES (which is applicable in the case of positive definite preconditioning) and full GMRES (which is applicable in the case of indefinite preconditioning) give residual vectors with identical Euclidean norm at each iteration. Moreover, we show that the convergence of both methods is related to a system of normal equations for which the LSQR algorithm can be employed. As a side result, we give a rare example of a non-trivial normal(1) matrix where the corresponding inner product is explicitly known: a conjugate gradient method therefore exists and can be employed in this case. This work was supported by British Council/German Academic Exchange Service Research Collaboration Project 465 and NATO Collaborative Research Grant CRG 960782