求解矩阵方程组的ABS算法

本文基于一类线性空间(Rn,n)n,n,建立求解( )X=B形式的矩阵方程组的ABS算法.讨论基本的ABS算法和两个特殊的ABS算法及其性质.并将其中的Huang算法用于求解带有各种约束(包括对称和稀疏约束)的拟牛顿方程.     

关于再生产点性质的一个反例

具有再生产点性质的问题一次生产足后续几个阶段内的产品之和的决定方法 ,把 n个阶段的决策裂解为几个子问题决策 ,使计算量大量的减少 .然而最近发现 ,对于一些实际问题 ,这样计算得到的并不是最优解 .　举例如下 :设各阶段的需求如下表 :时间 (k) 1 2 3需求 (dk) 44 4又设第 k阶段生产量为 xk时生产成本为 :Ck(xk) =0当 xk=05+xk 　　　当 xk=1 ,2 ,… ,6∞当 xk>6第 k阶段末库存量为 vk时存储费为 :hk(vk) =0 .2 vk第 k阶段的总成本为 :Ck(xk) +hk(vk)若用再生产点性质计算 :(1 )计算阶段 j到阶段 i的总成本 C(j,i) (j≤i) ;C(1 ,1 ) =…     

大型稀疏线性互补问题的行作用法

且引言考虑线性互补问题＊＊P（q，M）：求X二（X；，x。，…，x。厂E”使得x＞O，训x）E＊x＋g＞o，／U（X）一O（1）其中M一（m；。）为nXn矩阵（不必对称），q一切，q。，…，q。）rER“为给定常向量．通常情况下已有求解LCP（q，M）的若干著名算法［‘－’j．本文提出求解LCP（q，M）的一种新算法一行作用法，方法具有如下特点：（i）每次迭代只需n个简单的投影运算，每次投影只涉及矩阵M的一行；（n）生成新的迭代点x‘“‘时只利用前次迭代点／；（iii）对矩阵M不实施任何整体运算．因而适合于求解大型（巨型）稀疏问题，且…     

求解特征值互补问题的一类ABS算法

ABS算法是20世纪80年代初,由Abaffy,Broyden和Spedicato完成的用于求解线性方程组的含有三个参量的投影算法,是一类有限次迭代直接法。目前,ABS算法不仅可以求解线性与非线性方程组,还可以求解线性规划和具有线性约束的非线性规划等问题。本文即是利用ABS算法求解特征值互补问题的一种尝试,构造了求解特征值互补问题的ABS算法,证明了求解特征值互补问题的ABS算法的收敛性。数值例子充分验证了求解特征值互补问题的ABS算法的有效性。     

2008年上海市高考数学（理）第11题赏析

上海市2008年高考数学（理）第11题为：题目方程x^2＋√2x-1=0的解可视为函数y=x＋√2的图像与函数y=1/x的图像交点的横坐标，若方程x^4＋ax-4=0的各个实根x1,x2,…,xk（k≤4）所对应的点（xi,4/xi）（i=1,2,…,k）均在直线y=x的同侧，则实数a的取值范围是______.     

大型稀疏ND线性方程组的实用ABS算法

本文根据ND矩阵(Nested Dissection Matrix)的结构特点以及ABS算法解ND线性方程组的性质,进一步探讨ABS算法求解ND线性方程组所应采取的内存管理策略,使迭代过程对内存容量的要求大大地小于存贮初始系数矩阵非零元的容量,而其它方法既使采用稀疏矩阵的压缩存贮技术其所需存贮单元也要多于初始系数矩阵非零元的个数。本文所述算法节约存贮单元不是以牺牲速度为代价的,从而达到了既提     

闭凸集约束下线性矩阵方程求解的松弛交替投影算法

研究线性矩阵方程AXB=C在闭凸集合R约束下的数值迭代解法.所考虑的闭凸集合R为(1)有界矩阵集合,(2)Q-正定矩阵集合和(3)矩阵不等式解集合.构造松弛交替投影算法求解上述问题,并用算子理论证明了由该算法生成的序列具有弱收敛性.给出了矩阵方程AXB=C求对称非负解和对称半正定解的数值算例,大量数值实验验证了该算法的可行性和高效性,并说明该算法与交替投影算法和谱投影梯度算法比较在迭代效率上的明显优势.     

ABS共轭方向算法

本文提出ABS共轭方向算法，它可以产生一大类共轭方向．尤其，Dennis和Turner(1987)提出的广义共轭方向方法也可以由该算法产生。     

实对称矩阵在相合分解下的Moore-Penrose型广义逆的通式

1引言与符号说明对m×n矩阵A,下列矩阵方程：(1)AXA=A,(2)XAX=x,(3)(AX)~T=AX,(4)(XA)~T=XA称为Penrose方程．如果X满足上述方程(i)(j),…(k),则称X为(ij…k)逆,其全体记为A(ij…k)．(1234)逆常记为A~ ．所有这种矩阵叫广义逆(矩阵)或Moore- Penrose型逆(矩阵)．广义逆矩阵在许多数学领域有广泛应用．它在解矩阵方程中的作用     

利用交替投影算法求解矩阵方程AXB=C的广义中心对称解

利用交替投影算法求解矩阵方程AXB=C的广义中心对称解,当矩阵方程AXB=C不相容时,利用Dykstra's交替投影算法来求其广义中心对称解的最佳逼近,数值结果表明该方法是行之有效的.     

一种两点迭代法

本文给出了一个求超越方程实根的新的两点格式ｘｋ＋１＝ｘｋ－ｘｋ－ｘｋ－１３ｆ（ｘｋ）－４ｆｘｋ＋ｘｋ－１２＋ｆ（ｘｋ－１）ｆ（ｘｋ），它集弦割法和抛物线法的优点于一身，具有更快的收敛速度，且收敛阶为二阶．     

ABS methods and ABSPACK for linear systems and optimization: A review

ABS methods are a large class of methods, based upon the Egervary rank reducing algebraic process, first introduced in 1984 by Abaffy, Broyden and Spedicato for solving linear algebraic systems, and later extended to nonlinear algebraic equations, to optimization problems and other fields; software based upon ABS methods is now under development. Current ABS literature consists of about 400 papers. ABS methods provide a unification of several classes of classical algorithms and more efficient new solvers for a number of problems. In this paper we review ABS methods for linear systems and optimization, from both the point of view of theory and the numerical performance of ABSPACK.Work partially supported by ex MURST 60% 2001 funds.E. Spedicato     

Convergence analysis of the nonlinear block scaled ABS methods

In this paper, by a further investigation of the algorithm structure of the nonlinear block scaled ABS methods, we convert it into an inexact Newton method. Based on this equivalent version, we establish the semilocal convergence theorem of the nonlinear block scaled ABS methods and obtain convergence conditions that mainly depend on the behavior of the mapping at the initial point. This complements the convergence theory of the nonlinear block scaled ABS methods.     

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.     

Stein implicit Runge-Kutta methods with high stage order for large-scale ordinary differential equations

The Runge-Kutta method is one of the most popular implicit methods for the solution of stiff ordinary differential equations. For large problems, the main drawback of such methods is the cost required at each integration step for computing the solution of a nonlinear system of equations. In this paper, we propose to reduce the cost of the computation by transforming the linear systems arising in the application of Newton's method to Stein matrix equations. We propose an iterative projection method onto block Krylov subspaces for solving numerically such Stein matrix equations. Numerical examples are given to illustrate the performance of our proposed method.     

A generalization of the implicit LU algorithm to an arbitrary initial matrix

The implicit LU algorithm of the (basic) ABS class corresponds to the parameter choices H ₁=I, z _i =w _i =e _i . The algorithm can be considered as the ABS version of the classic LU factorization algorithm. In this paper we consider the generalization where the initial matrix H₁ is arbitrary except for a certain condition. We prove that every algorithm in the ABS class is equivalent, in the sense of generating the same set of search directions, to a generalized implicit LU algorithm, with suitable initial matrix, that can be interpreted as a right preconditioning matrix. We discuss some consequences of this result, including a straightforward derivation of Bienaymé's (1853) classical result on the equivalence of the Gram–Schmidt orthogonalization procedure with Gaussian elimination on the normal equations. This revised version was published online in June 2006 with corrections to the Cover Date.     

结构的延拓及三斜结构系统的代数弹性运动的数学性质

本文的目的并非简单地评述弹性静力学。著名的Cauchy六方程,其命题是由位移函数(u_i, u_j, u_k)=u(xi, xj, xk)的九个偏导数线性表达的,但其逆命题是该六个方程不可能表达阵(?(u_i, u_j, u_k)/?(xi, xj, xk))的九个元素,这是由于在给定点上的变形的几何表示至今尚不完全[1]。用几何语言来说,其逆命题的含意就是:在空间中任意三角形(正交除外)边的“平方长”运算用Pythogora's定理的结论是不真的[2]。本文将叙述代数弹性运动的某些数学规律及其与上述问题的关系。     

Interior dual proximal point algorithm using preconditioned conjugate gradient †

Serial and parallel implementations of the interior dual proximal point algorithm for the solution of large linear programs are described. A preconditioned conjugate gradient method is used to solve the linear system of equations that arises at each interior point interation. Numerical results for a set of multicommodity network flow problems are given. For larger problem preconditioned conjugate gradient method outperforms direct methods of solution. In fact it is impossible to handle very large problems by direct methods     

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.