大系统试验选优理论研究

针对某些高维动态、大型线性、复杂非线性、复杂模拟仿真和定性知识模型，该文全面总结了作者采用数学模型、知识模型和试验选优理论相结合的方法而提出的试验选优理论，该理论体系可以使一些目前无法求解或很难求解的大型问题获得近似解。并在复杂水资源系统的优化中取得了良好的效果。     

一类具有小周期系数的椭圆型边值问题的双尺度渐近分析方法

1．引言在大型水坝和海港等大体积混凝土结构的施工及运行中,为防止结构开裂,必须采取水管冷却等控制温度技术;在大型仓储和化学反应器中,为了控制温度,必须采用有规律的供暖或冷却技术;刚性夹杂复合材料结构的稳态问题以及具有周期性约束的是网结构的平衡问题等．这些工程实际问题,在稳定状态或某个瞬时状态,都可以归结为一类具有周期性的复杂构造的椭圆型方程边值问题：其中路（x）二a。柞）,河为结构所占据的区域,其。维模型如图1．l所示．图1．1具有周期性特征的二维结构这类问题的特点是材料系数a勺（x）呈现高度振荡的以。…     

大型线性方程组系数阵不限带宽的直接解法

在许多重要实际问题中,特别是在大系统的应用中,经常会遇见大型线性代数方程组的求解。由于一般的计算机的容量有限,使大型方程组的系数阵无法全部进入内存。因此,在直接解法中,出现了不少分块的求解方法,即利用计算机的外部设备(磁盘或磁带),将系数阵分块放进外存中,然后使用内外信息交换,完成方程组的求解。     

大型不正定陀螺系统本征值问题

陀螺动力系统可以导入哈密顿辛几何体系,在哈密顿陀螺系统的辛子空间迭代法的基础上提出了一种能够有效计算大型不正定哈密顿函数的陀螺系统本征值问题的算法．利用陀螺矩阵既为哈密顿矩阵而本征值又是纯虚数或零的特点,将对应哈密顿函数为负的本征值分离开来,构造出对应哈密顿函数全为正的本征值问题,利用陀螺系统的辛子空间迭代法计算出正定哈密顿矩阵的本征值,从而解决了大型不正定陀螺系统的本征值问题,算例证明,本征解收敛得很快．     

用方阵乘幂求和法求线性方程组的数值解

介绍了一种新型的,不同于传统的雅克比或高斯塞德尔迭代法的,求解线性方程组的方阵乘幂求和法,并引入了方阵意义上求积分的龙贝格法.该算法成立须以方阵A为实阵,非奇异且主对角元素占优.该法较雅克比或高斯塞德尔迭代的计算量小,特别有助于求解大型线性方程组的问题.     

求解大型超定线性代数方程组的块Kaczmarz算法

正1 引言考虑大型超定线性代数方程组Ax=b,(1)其中 A ∈ C~(m×n) (m n),b ∈C~m.当m=n时,线性代数方程组求解的相关理论和算法较为成熟,但在很多实际问题中,系数矩阵A的行数和列数不相等(m≠n),如超定或欠定线性代数方程组.因此,有必要研究此类线性代数方程组的数值解法.在结构分析,计算机辅助几何设计,图像恢复,模型参数估计等众多领域中,经常需要求解大型超定线性代数方程组.Vuik [1]研究了大型超定线性代数方程组最小二乘问题的预处理Krylov迭代方法;Bai [2]提出列分解松弛法;Yin[3]提出了求解大型稀疏最小二乘问题的不完备Givens正交化的预处理GMRES方法;Hayami[4]考虑引入一个新的矩阵将GMRES方法应用到最小二乘问题,求得方程组的最小二乘解;Finta [5]推导了加权超定线性代数方程组的梯度法,并证明该方法是收敛的.     

不连续大系统的实用稳定性

本文研究具有各种不同分解形式的不连续大型动态系统的实用稳定性,而这一类问题几乎还未研究过.我们提出了不连续大系统的各种实用稳定性和不稳定性的概念,得到了判别具有各种不同分解形式的不连续大系统的实用稳定性或实用不稳定性的若干有效判别准则;末了提出了进一步可以考虑的问题.     

基于可拓集方法改进波士顿矩阵研究

传统波士顿矩阵分析模型衡量指标少、僵化,仅比较适用于市场上处于寡头垄断的大型企业.基于可拓集方法改进后的新波士顿矩阵分析模型,首先根据企业、行业实际情况设定评价指标,然后建立可拓集与综合关联函数,以对业务类型进一步细分.其次,再根据拓展分析方法和可拓变换方法对细分后属于问题类的业务进行拓展、变换,可形成使问题类业务转变为明星类或金牛类业务的若干策略,为企业市场决策提供参考.改进后的新波士顿矩阵分析模型不但适用于大型企业,亦适用于中小企业,更具有普适性.     

Fuzzy理论在工程环境灾害预测中的应用

针对矿山大型排土场所出现的滑坡等工程环境灾害的预测与防治问题,着重探讨排土场边坡失稳破坏预测问题.文中采用Fuzzy数学中的Fuzzy概率测度理论建立理论预测分析模型,并对大型排土场边坡失稳破坏的Fuzzy概率测度进行具体的预测分析,所获结果与已有的经典分析方法所获理论结果一致.针对矿山大型排土场所出现的滑坡等工程环境灾害防治问题,提出了具体的防治技术措施.     

大型互联非线性分布参数系统的分散迭代学习控制

研究大型互联非线性分布参数系统的分散迭代学习控制问题,该类大型互联分布参数系统由抛物型偏微分方程组或由双曲型偏微分方程组构成.针对系统所满足的性质,基于P型学习律构建得到迭代学习控制律,在这种分散式控制方案中,每个子系统的控制器仅依赖于该子系统的输出变量,不需要与其它子系统交换信息.利用压缩映射原理,证明这种学习律能使得系统的输出跟踪误差于L~2空间内沿迭代轴方向收敛.仿真算例说明了所得结论的可行性和有效性.     

一类特殊矩阵方程的并行预处理变形共轭梯度算法

研究了求解一类矩阵方程AXB=C,提出了一种并行预处理变形共轭梯度法．该方法给出一种迭代法的预处理模式．首先给出的预处理矩阵是严格对角占优矩阵,构造并行迭代求解预处理矩阵方程的迭代格式,进而使用变形共轭梯度法并行求解．通过数值试验,预处理变形共轭梯度法与直接使用变形共轭梯度法相比较,该算法不仅有效提高了收敛速度,而且具有很高的并行性．     

Block two-stage preconditioners

Linear systems of the form Ax = b, where the matrix A is symmetric and positive definite, often arise from the discretization of elliptic partial differential equations. A very successful method for solving these linear systems is the preconditioned conjugate gradient method. In this paper, we study parallel preconditioners for the conjugate gradient method based on the block two-stage iterative methods. Sufficient conditions for the validity of these preconditioners are given. Computational results of these preconditioned conjugate gradient methods on two parallel computing systems are presented.     

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     

Nested splitting CG-like iterative method for solving the continuous Sylvester equation and preconditioning

We present a nested splitting conjugate gradient iteration method for solving large sparse continuous Sylvester equation, in which both coefficient matrices are (non-Hermitian) positive semi-definite, and at least one of them is positive definite. This method is actually inner/outer iterations, which employs the Sylvester conjugate gradient method as inner iteration to approximate each outer iterate, while each outer iteration is induced by a convergent and Hermitian positive definite splitting of the coefficient matrices. Convergence conditions of this method are studied and numerical experiments show the efficiency of this method. In addition, we show that the quasi-Hermitian splitting can induce accurate, robust and effective preconditioned Krylov subspace methods.     

Preconditioned tensor format conjugate gradient squared and biconjugate gradient stabilized methods for solving stein tensor equations

This article is concerned with solving the high order Stein tensor equation arising in control theory. The conjugate gradient squared (CGS) method and the biconjugate gradient stabilized (BiCGSTAB) method are attractive methods for solving linear systems. Compared with the large-scale matrix equation, the equivalent tensor equation needs less storage space and computational costs. Therefore, we present the tensor formats of CGS and BiCGSTAB methods for solving high order Stein tensor equations. Moreover, a nearest Kronecker product preconditioner is given and the preconditioned tensor format methods are studied. Finally, the feasibility and effectiveness of the new methods are verified by some numerical examples.     

Computational results of an interior point algorithm for large scale linear programming

This paper gives computational results for an efficient implementation of a variant of dual projective algorithm for linear programming. The implementation uses the preconditioned conjugate gradient method for computing projections. Our computational experience reported in this paper indicates that this algorithm has potential as an alternative for solving very large LPs in which the direct methods fail due to memory and CPU time requirements. The conjugate gradient algorithm was able to find very accurate directions even when the system was ill-conditioned. The paper also discusses a new mathematical technique called the reciprocal estimates for estimating the primal variables. We have conducted extensive computational experiments on problems representative of large classes of applications of current interest. We have also chosen instances of the problems of future potential interest, which could not be solved in the past due to the weakness of the prior solution methods, but which represent a large class of new applications. The hypergraph model is such an example. Comparison of our implementation with MINOS 5.1 shows that our implementation is orders of magnitude faster than MINOS 5.1 for these problems.     

有界约束非线性优化问题的仿射共轭梯度路径法

本文提出仿射内点离散共轭梯度路径法解有界约束的非线性优化问题,通过构造预条件离散的共轭梯度路径解二次模型获得预选迭代方向,结合内点回代线搜索获得下一步的迭代,在合理的假设条件下,证明了算法的整体收敛性与局部超线性收敛速率,最后,数值结果表明了算法的有效性.     

Preconditioned Conjugate Gradient Methods for Integral Equations of the Second Kind Defined on the Half-Line

1.IntroductionInthispaPer,westudynumericalsolutionstointegralequationsofthesecondkinddefinedonthehalfline.Morepreciselyweconsidertheequationy(t)+Iooa(t,s)y(s)ds=g(t),OS相似文献   

A Class of Nested Iteration Schemes for Linear Systems with a Coefficient Matrix with a Dominant Positive Definite Symmetric Part

We present a class of nested iteration schemes for solving large sparse systems of linear equations with a coefficient matrix with a dominant symmetric positive definite part. These new schemes are actually inner/outer iterations, which employ the classical conjugate gradient method as inner iteration to approximate each outer iterate, while each outer iteration is induced by a convergent and symmetric positive definite splitting of the coefficient matrix. Convergence properties of the new schemes are studied in depth, possible choices of the inner iteration steps are discussed in detail, and numerical examples from the finite-difference discretization of a second-order partial differential equation are used to further examine the effectiveness and robustness of the new schemes over GMRES and its preconditioned variant. Also, we show that the new schemes are, at least, comparable to the variable-step generalized conjugate gradient method and its preconditioned variant.