双调和方程Schwarz区域分解算法的Fourier分析

Schwarz方法是一类重要的区域分解算法,以Fourier变换作为分析工具,推导了经典Schwarz交替迭代法和加性Schwarz迭代法用于求解双调和方程的误差传播阵及其谱半径的准确表达式,不但从新的角度更简洁地证明了Schwarz交替迭代法和加性schwarz迭代法的收敛性,还刻画了其收敛速度,以及收敛速度随子区域的重叠程度变化而变化的情况,所得结果不依赖于任何未知常数,不受具体离散方法的影响,同时表明经典Schwarz交替迭代法具有比加性Schwarz方法快1倍的收敛速度.     

区域分解算法——偏微数值解新技术(Ⅲ)

<正> 3.1经典Sohwar二交替方法 重迭型区域分解算法是以Schwarz交替法为理论依据.1869年德国数学家H.A.Schwarz首次用交替方法论证两个相互重迭区域和集上的Laplaoe方程Diriohlet问题解的存在性,稍后Neumann注意到这一思想可以用于求解两个相互覆盖区域的Diriohlet     

Robin型离散Schwarz波形松弛算法的收敛性分析

Schwarz波形松弛(Schwarz waveform relaxation,SWR)是一种新型区域分解算法,是当今并行计算研究领域的焦点之一,但针对该算法的收敛性分析基本上都停留在时空连续层面.从实际计算角度看,分析离散SWR算法的收敛性更重要.本文考虑SWR研究领域中非常流行的Robin型人工边界条件,分析时空离散参数t和x、模型参数等因素对算法收敛速度的影响.Robin型人工边界条件中含有一个自由参数p,可以用来优化算法的收敛速度,但最优参数的选取却需要求解一个非常复杂的极小-极大问题.本文对该极小-极大问题进行深入分析,给出最优参数的计算方法.本文给出的数值实验结果表明所获最优参数具有以下优点:(1)相比连续情形下所获最优参数,利用离散情形下获得的参数可以进一步提高Robin型SWR算法在实际计算中的收敛速度,当固定t或x而令另一个趋于零时,利用离散情形下所获参数可以使算法的收敛速度具有鲁棒性(即收敛速度不随离散参数的减小而持续变慢).(2)相比连续情形下所获收敛速度估计,离散情形下获得的收敛速度估计可以更加准确地预测算法的实际收敛速度.     

具单调收敛性的区域分解法

1 引言 区域分解法和多重网格法都被认为是求解椭圆边值问题的快速算法.这两类算法也先 后应用于变分不等式的求解并获得了较为成功的数值尝试,收敛性理论也相继建 立.但是和用于方程问题不同,建立相应的h无关收敛性理论甚至更初步的收敛率分析遇到 一定的困难.九十年代初,Kornhuber针对变分不等式第一边值问题及摩擦问题进一步 讨论了多重网格法的收敛性质并在其离散问题非退化情形证明了渐近几何收敛速度,但仍 未见到有关h无关收敛性.区域分解法起步稍晚,但自八十年代末Lions给出了Schwarz交 替法的变分解释以来发展很快.Kuznetsov等人于九十年代初证明了乘性 Schwarz和加性Schwarz算法用于求解单边障碍问题时单调收敛于解.在同样条件下, [13]得到了误差估计式并利用无约束情形的有关结果得到了h无关收敛性.但是,在前述 的各种区域分解法中,子问题的求解都是精确的,因此在子域上费时较多而且在数值上也往 往只能得到子问题的近似解.这样自然产生这样一个想法:能否在子问题上和多重网格法 一样用近似解代替?本文即是针对此问题,从加性Schwarz算法入手,不仅证明算法收敛,而     

色散方程的一类新的高精度交替分组显隐算法

对于具有周期性边界条件的色散方程,提出了一种高精度的交替分组显隐格式新解法(nAGEI)．它不但无条件稳定,而且同已有的ASEI和AGE等交替方法比较,还具有精度高、收敛快的特点,数值试验表明新方法关于空间步长具有四阶收敛速度．     

一类非线性算子障碍问题的Schwarz算法

本文对一类非线性算子的障碍问题提出了几个Schwarz算法,所得迭代序列为上解序列或下解序列,它们单调收敛于问题的准确解.     

一种近邻密度估计的强收敛速度

本文讨论了俞军（１９８６）提出的一种近邻密度估计的逐点强收敛速度和一致强收敛速度．并证明了收敛速度的主阶部分不能达到．     

一类拟互补问题的迭代法

本文研究一类非线性算子的拟互补问题,获得了在新的条件下的解的存在唯一性定理,并给出了两个Schwarz算法,所产生的近似解序列单调收敛于真解。     

更新过程的叠对数律

本文利用独立同分布随机变量序列部分和的叠对数律收敛速度结果,在一定条件下,研究了一般更新过程的叠对致律及其收敛速度。     

自适应间断Galerkin 有限元的多水平方法

本文讨论在自适应网格上间断Galerkin 有限元离散系统的局部多水平算法. 对于光滑系数和间断系数情形, 利用Schwarz 理论分析了算法的收敛性. 理论和数值试验均说明算法的收敛率与网格层数以及网格尺寸无关. 对强间断系数情形算法是拟最优的, 即收敛率仅与网格层数有关.     

The projected newton method for solving inverse eigenvalue problems - the case of multiple eigenvaues

This paper deals with the optimal solution of ill-posed linear problems, i.e..linear problems for which the solution operator is unbounded. We consider worst-case ar,and averagecase settings. Our main result is that algorithms having finite error (for a given setting) exist if and only if the solution operator is bounded (in that setting). In the worst-case setting, this means that there is no algorithm for solving ill-posed problems having finite error. In the average-case setting, this means that algorithms having finite error exist if and only lf the solution operator is bounded on the average. If the solution operator is bounded on the average, we find average-case optimal information of cardinality n and optimal algorithms using this information, and show that the average error of these algorithms tends to zero as n→∞. These results are then used to determine the [euro]-complexity, i.e., the minimal costof finding an [euro]-accurate approximation. In the worst-case setting, the [euro]comp1exity of an illposed problem is infinite for all [euro]>0; that is, we cannot find an approximation having finite error and finite cost. In the average-case setting, the [euro]-complexity of an ill-posed problem is infinite for all [euro]>0 iff the solution operator is not bounded on the average, moreover, if the the solutionoperator is bounded on the average, then the [euro]-complexity is finite for all [euro]>0.     

Methods of constructing optimal stabilizers

The problem of transforming a linear dynamical system in the neighbourhood of a state of equilibrium [1,2] is solved using the special problem of the damping of the system by controls of minimum intensity after a finite time interval. The possibility of using other problems of optimal control is discussed. The main attention is devoted to constructing algorithms of the operation of a device (a stabilizer) which is able, in real time, to generate a stabilizing control circulating in the closed optimal system when unknown perturbations operate constantly [3, 4]. The proposed method is based on the constructive theory of optimal control [5, 6]. Another form of this theory for solving the problem of stabilization is presented in [7](see also [8]).     

Solution of optimal control problems by a variational method

An algorithm for numerically solving optimal control problems by methods applied to ill-posed problems is discussed. The stable algorithms for solving such problems on compact sets developed by Academician A.N. Tikhonov in the twentieth century can be applied to problems of optimal control. The special feature of optimal control problems is the discontinuity of a control function. This difficulty is overcome by introducing a moving computational grid. The step size of the grid is determined by solving the speed problem.     

人力资源多维分配问题的混合算法

针对已有多维分配问题求解算法复杂、耗时长及精度低等问题,本文将二部图中寻求最优匹配的方法进行推广,运用试分配、饱和路调整和增广路调整对多维分配问题的最优解进行搜索,提出了求解人力资源多维分配问题的最小零面优先分配混合算法和随机试分配混合算法,对算法的有效性进行了理论证明,并分析了算法的时间和空间复杂度;同时通过这两种混合算法对初始零元素数不同的代价矩阵求解时间的计算,以及与Lagrangian松弛算法和剪枝法的耗时、精度的对比,分别得到了两种混合算法的适用性和高效性,最后通过算例验证了算法的有效性。     

Global relaxed modulus-based synchronous block multisplitting multi-parameters methods for linear complementarity problems

Recently, Bai and Zhang [Numerical Linear Algebra with Applications, 20(2013):425439] constructed modulus-based synchronous multisplitting methods by an equivalent reformulation of the linear complementarity problem into a system of ?xed-point equations and studied the convergence of them; Li et al. [Journal of Nanchang University (Natural Science), 37(2013):307-312] studied synchronous block multisplitting iteration methods; Zhang and Li [Computers and Mathematics with Application, 67(2014):1954-1959] analyzed and obtained the weaker convergence results for linear complementarity problems. In this paper, we generalize their algorithms and further study global relaxed modulus-based synchronous block multisplitting multi-parameters methods for linear complementarity problems. Furthermore, we give the weaker convergence results of our new method in this paper when the system matrix is a block H+?matrix. Therefore, new results provide a guarantee for the optimal relaxation parameters, please refer to [A. Hadjidimos, M. Lapidakis and M. Tzoumas, SIAM Journal on Matrix Analysis and Applications, 33(2012):97-110, (dx.doi.org/10.1137/100811222)], where optimal parameters are determined.     

Convergence of the forward-backward sweep method in optimal control

The Forward-Backward Sweep Method is a numerical technique for solving optimal control problems. The technique is one of the indirect methods in which the differential equations from the Maximum Principle are numerically solved. After the method is briefly reviewed, two convergence theorems are proved for a basic type of optimal control problem. The first shows that recursively solving the system of differential equations will produce a sequence of iterates converging to the solution of the system. The second theorem shows that a discretized implementation of the continuous system also converges as the iteration and number of subintervals increases. The hypotheses of the theorem are a combination of basic Lipschitz conditions and the length of the interval of integration. An example illustrates the performance of the method.     

Convergence of Fixed-Point Continuation Algorithms for Matrix Rank Minimization

The matrix rank minimization problem has applications in many fields, such as system identification, optimal control, low-dimensional embedding, etc. As this problem is NP-hard in general, its convex relaxation, the nuclear norm minimization problem, is often solved instead. Recently, Ma, Goldfarb and Chen proposed a fixed-point continuation algorithm for solving the nuclear norm minimization problem (Math. Program., doi:, 2009). By incorporating an approximate singular value decomposition technique in this algorithm, the solution to the matrix rank minimization problem is usually obtained. In this paper, we study the convergence/recoverability properties of the fixed-point continuation algorithm and its variants for matrix rank minimization. Heuristics for determining the rank of the matrix when its true rank is not known are also proposed. Some of these algorithms are closely related to greedy algorithms in compressed sensing. Numerical results for these algorithms for solving affinely constrained matrix rank minimization problems are reported.     

基于黄金分割法的离散回归方程的变点估计及其应用

本文利用变点统计学和黄金分割法讨论有多个变点的离散回归方程的交点估计和参数估计，文中提出基于黄金分割法搜索最佳变点估计和同时得到参数估计的最小二乘算法，还讨论该算法在控制领域的应用，数值模拟结果显示本文算法能给出良好的变点及参数的估计值。     

Block SOR methods for rank-deficient least-squares problems

Many papers have discussed preconditioned block iterative methods for solving full rank least-squares problems. However very few papers studied iterative methods for solving rank-deficient least-squares problems. Miller and Neumann (1987) proposed the 4-block SOR method for solving the rank-deficient problem. Here a 2-block SOR method and a 3-block SOR method are proposed to solve such problem. The convergence of the block SOR methods is studied. The optimal parameters are determined. Comparison between the 2-block SOR method and the 3-block SOR method is given also.     

Global optimization of polynomial-expressed nonlinear optimal control problems with semidefinite programming relaxation

In this paper, we propose a new deterministic global optimization method for solving nonlinear optimal control problems in which the constraint conditions of differential equations and the performance index are expressed as polynomials of the state and control functions. The nonlinear optimal control problem is transformed into a relaxed optimal control problem with linear constraint conditions of differential equations, a linear performance index, and a matrix inequality condition with semidefinite programming relaxation. In the process of introducing the relaxed optimal control problem, we discuss the duality theory of optimal control problems, polynomial expression of the approximated value function, and sum-of-squares representation of a non-negative polynomial. By solving the relaxed optimal control problem, we can obtain the approximated global optimal solutions of the control and state functions based on the degree of relaxation. Finally, the proposed global optimization method is explained, and its efficacy is proved using an example of its application.