关于非参数回归M－估计的最优收敛速度的一点记注

假设随机观测数据（Ｔ１，Ｙ１），…，（Ｔｎ，Ｙｎ）满足非参数回归模型Ｙｉ－ｇ０（Ｔｉ）＋ｕｉ，１≤ｉ≤ｎ。本文讨论非参数回归函数ｇ０的分段多项式Ｍ估计ｇｎ的最优收敛速度问题，其中ｇｎ满足为一ｍ阶分段多项式函数类，ρ是给定的一个函数，它不一定处处可微．在一定条件下，本文证明了上述稳健Ｍ估计达到非参数回归的最优收敛速度．     

指数族刻度参数EB估计的渐近最优性

依据经验Bayes(EB)估计的思想方法,研究在LINEX损失函数下指数族刻度参数的EB估计问题.在这种损失函数下,求得参数的Bayes估计,利用密度函数的核估计方法,构造了总体X的密度函数估计,从而得到参数的EB估计,证明了这种EB估计是渐近最优的,并获得了它的收敛速度,最后将这种方法推广到多参数情形,并举例、模拟说明了它的应用.     

半参数回归模型非参数分量L1模估计的最优收敛速度

对半参数回归模型，采用分段多项式逼近非参数函数，构造了参数与非参数分量Ｌ１模糊估计，并获得了非参数分量Ｌ１模估计的最优估计收敛速度为Ｏｐ（ｎ＾－ｍ＋ｒ／［２（ｍ＋ｒ）＋１］）。     

一种磨光微分方法

本文讨论一种利用磨光思想求解微分的正则化方法，并讨论了它在某种条件下的收敛性．这种磨光微分方法结合正则化参数的选取得到了最优的收敛阶，最后给出了一个数值例子，证明该方法是可行的．     

一个连续时间非平稳随机过程下的核密度估计的最优收敛速度

设{Xt,t≥0}为定义在R^d上的随机过程，它由模型Xt=zt Φ(Yt) εt确定，{Yt}和{εt}相互独立，而zt为非随机变量，对于连续观察的样本，本文给出了非参数密度核估计极其在均方意义下的最优收敛速度，并讨论了非随机项运动形式对此速度的影响。     

相容次序矩阵PSD迭代法的收敛性

本文是在求解大型线性方程组Ax=b的系数矩阵A为(1,1)相容次序矩阵且其Jacobi迭代矩阵的特征值均为纯虚数或零的条件下,得到PSD迭代法收敛的充分必要性定理,并在特殊情况下得到了相应的最优参数.     

二层随机规划逼近最优解集的上半收敛性

下层随机规划以上层决策变量作为参数,而上层随机规划是以下层随机规划的唯一最优解作为响应的一类二层随机规划问题,首先在下层随机规划的原问题有唯一最优解的假设下,讨论了下层随机规划的任意一个逼近最优解序列都收敛于原问题的唯一最优解,然后将下层随机规划的唯一最优解反馈到上层,得到了上层随机规划逼近最优解集序列的上半收敛性.     

全局收敛的鲁棒极点配置算法

本文利用一个新的鲁棒性指标,给出了一个全局收敛的用状态反馈配置鲁棒极点的算法,并证明了收敛点至少是局部最优解。     

随机规划经验逼近问题最优解集的几乎处处下半收敛性

本文给出了随机规划经验逼近最优解集几乎处处下半收敛的一个充分条件,并由此得到随机规划经验逼近最优解集几乎处处Hausdorff收敛的一个充分条件.     

线性指数分布参数的经验Bayes检验问题

分别讨论了线性指数分布参数的经验Bayes(EB)单侧和双侧检验问题.利用概率密度函数的核估计分别构造了参数的经验Bayes检验函数,在适当的条件下证明了所提出的经验Bayes检验函数的渐近最优(a.o.)性并获得了它的收敛速度.最后,给出一个有关主要结果的例子.     

预条件同时置换(PSD)迭代法的收敛性分析

1引言求解线性方程组Ax=6,(1.1)其中A∈R~(n×n)非奇异阵且对角元非零,x,b∈R~n,x未知,b已知.不失一般性,我们假设A=I-L-U,(1.2)其中L,U分别为A的严格下和上三角矩阵,相应的Jacobi迭代矩阵为B=L U.(1.3)若Q是非奇异阵且Q~(-1)易计算,于是(1.1)可以变成     

An iteration method for the symmetric solutions and the optimal approximation solution of the matrix equation AXB=C

An iteration method is constructed to solve the linear matrix equation AXB=C over symmetric X. By this iteration method, the solvability of the equation AXB=C over symmetric X can be determined automatically, when the equation AXB=C is consistent over symmetric X, its solution can be obtained within finite iteration steps, and its least-norm symmetric solution can be obtained by choosing a special kind of initial iteration matrix, furthermore, its optimal approximation solution to a given matrix can be derived by finding the least-norm symmetric solution of a new matrix equation . Finally, numerical examples are given for finding the symmetric solution and the optimal approximation symmetric solution of the matrix equation AXB=C.     

关于PSD迭代法收敛的充分必要性定理

本文在线性方程组系数矩阵A为相容次序矩阵及A的Jacobi迭代矩阵的特征值μ_j均为实数且μ_j~2<1的条件下,得出了PSD迭代法收敛的充分必要性定理,并由此而得到了一个易于判别的PSD法收敛性定理。     

Optimal Control of Linear Time-Varying Systems via Haar Wavelets

This paper introduces the application of Haar wavelets to the optimal control synthesis for linear time-varying systems. Based upon some useful properties of Haar wavelets, a special product matrix, a related coefficient matrix, and an operational matrix of backward integration are proposed to solve the adjoint equation of optimization. The results obtained by the proposed Haar approach are almost the same as those obtained by the conventional Riccati method.     

Sensitivity and Hessian matrix analysis of power spectral density functions for uniformly modulated evolutionary random seismic responses

This paper describes a numerical method for calculation of the sensitivity and Hessian matrix of the response PSD functions of structures subjected to uniformly modulated evolutionary random seismic excitation. The method is formulated based on the pseudo excitation method and Newmark method. The evolutionary non-stationary random response analysis is converted into step-by-step integration computations using the pseudo excitation method. The formulas of the pseudo responses, their first and second derivatives with respect to the structural design variables are derived based on the Newmark method. The PSD functions, their sensitivity and Hessian matrix are calculated using the pseudo responses, their first and second derivatives, respectively. Then the computation procedure of sensitivity and Hessian matrix of PSD functions is given in detail. Finally, the PSD functions’ sensitivity and Hessian matrix analysis of a three-story, two-bay planar frame subjected to the uniformly modulated evolutionary random earthquake ground motion has been studied to elucidate the proposed method.     

An alternative approach for non-linear optimal control problems based on the method of moments

We propose an alternative method for computing effectively the solution of non-linear, fixed-terminal-time, optimal control problems when they are given in Lagrange, Bolza or Mayer forms. This method works well when the nonlinearities in the control variable can be expressed as polynomials. The essential of this proposal is the transformation of a non-linear, non-convex optimal control problem into an equivalent optimal control problem with linear and convex structure. The method is based on global optimization of polynomials by the method of moments. With this method we can determine either the existence or lacking of minimizers. In addition, we can calculate generalized solutions when the original problem lacks of minimizers. We also present the numerical schemes to solve several examples arising in science and technology.     

混合边界条件下的三维定常 旋转Navier-Stokes方程的原始变量有限元计算

本文利用原始变量有限元法求解混合边界条件下的三维定常旋转Navier-Stokes方程,证明了离散问题解的存在唯一性,得到了有限元解的最优误差估计.给出了求解原始变量有限元逼近解的简单迭代算法,并证明了算法的收敛性.针对三维情况下计算资源的限制,采用压缩的行存储格式存储刚度矩阵的非零元素,并利用不完全的LU分解作预处理的GMRES方法求解线性方程组.最后分析了简单迭代和牛顿迭代的优劣对比,数值算例表明在同样精度下简单迭代更节约计算时间.     

On homotopy analysis method applied to linear optimal control problems

In this paper, the homotopy analysis method (HAM) is employed to solve the linear optimal control problems (OCPs), which have a quadratic performance index. The study examines the application of the homotopy analysis method in obtaining the solution of equations that have previously been obtained using the Pontryagin’s maximum principle (PMP). The HAM approach is also applied in obtaining the solution of the matrix Riccati equation. Numerical results are presented for several test examples involving scalar and 2nd-order systems to demonstrate the applicability and efficiency of the method.     

子空间约束下矩阵方程ATXB+BTXTA=D的解及最佳逼近

约束矩阵方程求解是指在满足一定约束条件下求矩阵方程（组）的解.在子空间约束条件下,利用共轭梯度法,结合线性投影算子,得到矩阵方程A^TXB+B^TX^TA=D的解,进一步得到其最佳逼近.最后用数值例子证实了算法的有效性.     

Numerical Solution of Hamilton-Jacobi-Bellman Equations by an Upwind Finite Volume Method

In this paper we present a finite volume method for solving Hamilton-Jacobi-Bellman(HJB) equations governing a class of optimal feedback control problems. This method is based on a finite volume discretization in state space coupled with an upwind finite difference technique, and on an implicit backward Euler finite differencing in time, which is absolutely stable. It is shown that the system matrix of the resulting discrete equation is an M-matrix. To show the effectiveness of this approach, numerical experiments on test problems with up to three states and two control variables were performed. The numerical results show that the method yields accurate approximate solutions to both the control and the state variables.