基于凝聚函数的互补问题的光滑化算法

对于不可微的"极大值"形式的函数,可以利用凝聚函数对其进行光滑逼近.借助这个技术,给出了求解线性互补问题的光滑方程组算法.首先是将互补问题转化为等价的非光滑方程组,再利用凝聚函数进行光滑逼近,从而转化为光滑方程组的求解问题.通过一些考题对这个算法进行了数值试验,结果显示了该算法的有效性和稳定性.     

依赖凝聚函数求解非线性互补问题的一种微分方程方法

利用凝聚函数一致逼近非光滑极大值函数的性质,将非线性互补问题转化为参数化光滑方程组.然后,对此方程组给出了一种微分方程解法,并且证明了非线性互补问题的解是微分方程系统的渐进稳定平衡点.在适当的假设条件下,证明了所给出的算法具有二次收敛速度.数值结果表明了此算法的有效性.     

绝对值函数的一致光滑逼近函数

绝对值函数是一个非光滑函数,研究了绝对值函数的光滑逼近函数.给出了绝对值函数的上方一致光滑逼近函数和下方一致光滑逼近函数,分别研究了其性质,并通过图像展示了逼近效果.     

用叠二次样条插值逼近导函数

在第一种边界条件下,我们证明了二重、三重、四重叠二次样条插值以h_2的精度分别逼近光滑函数的一阶、二阶、三阶导数,并说明对适当提出的边界条件.叠二次样条插值可以在h_2的精度范围内逼近光滑函数的其它阶导数.     

连续化方法求解约束凸规划问题

1．引言大型规划问题数值求解一直是计算数学工作者感兴趣的课题之一．针对大型约束规划问题,1991年李兴斯山提出凝聚函数法,该方法用光滑的凝聚函数逼近非光滑的极大值函数,从而把多个约束函数转化为带参数的单个光滑函数约束,从而降低了问题的规模．近年来,K3］研究了凸规划问题的凝聚函数法的收敛性,在目标函数强凸性及对一般凸规划研究了收敛性质．向讨论了可行解集有界的线性规划问题的凝聚函数求解算法并证明了收效性定理．上述文章均预先把凝聚参数取得充分小,然后对固定参数的单约束近似问题进行求解．一般地,凝聚参数取得…     

基于二次函数光滑化逼近的修正低阶罚函数

针对不等式约束优化问题, 给出了通过二次函数对低阶精确罚函数进行光滑化逼近的两种函数形式, 得到修正的光滑罚函数. 证明了在一定条件下, 当罚参数充分大, 修正的光滑罚问题的全局最优解是原优化问题的全局最优解. 给出的两个数值例子说明了所提出的光滑化方法的有效性.     

导数具有公共小函数的亚纯函数

进一步讨论亚纯函数的k阶导数具有公共小函数的唯一性问题,得到两个亚纯函数唯一性问题的结果,改进了李平的有关结果.     

求解一类特殊非光滑极大值函数方程的光滑保守DPRP共轭梯度法

对一类特殊极大值函数非光滑方程问题的方法进行了研究, 利用极大值函数和绝对值函数的光滑函数对提出的非光滑方程问题进行转化, 提出了一种光滑保守DPRP共轭梯度法. 在一般的条件下, 给出了光滑保守DPRP共轭梯度法的全局收敛性, 最后给出相关的数值实验表明方法的有效性.     

Feller算子对p阶有界变差函数的逼近

本文讨论了Feller算子对p阶有界变差函数的逼近.得到了两个逼近定理.它们包括了许多著名算子的估计.     

基于凝聚函数的互补问题的自调节内点算法

对于不可微的"极大值"形式的函数,可以利用凝聚函数对其进行光滑逼近.借助这个技术,给出了求解线性互补问题的一个具有自调节功能的内点算法.基于邻近度量和线性互补问题的标准中心化方程的关系,定义了一个新的邻近度量函数,并以极小化这个函数的最优性条件代替了该中心化方程.以此在摄动方程本身建立一种自调节的机制,从而使牛顿方向能够根据上次迭代点的信息做出自适应的调整.基于改造后的摄动方程组,建立了一个具有自调节功能的内点算法.通过一些考题对这个算法进行了数值试验,结果显示了算法的有效性和稳定性.     

旋转锥面$l_\infty$拟合的截断光滑化牛顿法

In this paper, the rotated cone fitting problem is considered. In case the measured data are generally accurate and it is needed to fit the surface within expected error bound, it is more appropriate to use l∞ norm than 12 norm. l∞ fitting rotated cones need to minimize, under some bound constraints, the maximum function of some nonsmooth functions involving both absolute value and square root functions. Although this is a low dimensional problem, in some practical application, it is needed to fitting large amount of cones repeatedly, moreover, when large amount of measured data are to be fitted to one rotated cone, the number of components in the maximum function is large. So it is necessary to develop efficient solution methods. To solve such optimization problems efficiently, a truncated smoothing Newton method is presented. At first, combining aggregate smoothing technique to the maximum function as well as the absolute value function and a smoothing function to the square root function, a monotonic and uniform smooth approximation to the objective function is constructed. Using the smooth approximation, a smoothing Newton method can be used to solve the problem. Then, to reduce the computation cost, a truncated aggregate smoothing technique is applied to give the truncated smoothing Newton method, such that only a small subset of component functions are aggregated in each iteration point and hence the computation cost is considerably reduced.     

Truncated Smoothing Newton Method for l_∞ Fitting Rotated Cones

In this paper, the rotated cone fitting problem is considered. In case the measured data are generally accurate and it is needed to fit the surface within expected error bound, it is more appropriate to use l∞ norm than l2 norm. l∞ fitting rotated cones need to minimize, under some bound constraints, the maximum function of some nonsmooth functions involving both absolute value and square root functions. Although this is a low dimensional problem, in some practical application, it is needed to fitting large...     

A truncated aggregate smoothing Newton method for minimax problems

Aggregate function is a useful smoothing function to the max-function of some smooth functions and has been used to solve minimax problems, linear and nonlinear programming, generalized complementarity problems, etc. The aggregate function is a single smooth but complex function, its gradient and Hessian calculations are time-consuming. In this paper, a truncated aggregate smoothing stabilized Newton method for solving minimax problems is presented. At each iteration, only a small subset of the components in the max-function are aggregated, hence the number of gradient and Hessian calculations is reduced dramatically. The subset is adaptively updated with some truncating criterions, concerning only with computation of function values and not their gradients or Hessians, to guarantee the global convergence and, for the inner iteration, locally quadratic convergence with as few computational cost as possible. Numerical results show the efficiency of the proposed algorithm.     

Exponential risk measure with application to UK asset allocation

In the paper the exponential risk measure of Damant and Satchell is used to formulate an investor's utility function and the properties of this function are investigated. The utility function is calibrated for a typical UK investor who would hold different proportions of equity. It is found that, for plausible parameter values, a typical UK investor will hold more equity under the assumption of non-normality of return if his utility function has the above formulation and not the standard mean-variance utility function. Furthermore, our utility function is consistent with positive skewness affection and kurtosis aversion. Some aggregate estimates of risk parameters are calculated for the typical UK investor. These do not seem well determined, raising issues of the roles of aggregation and wealth in this model.     

Estimation of Smooth Time-Varying Parameters in State Space Models

In this article, we propose a penalized likelihood method to estimate time-varying parameters in standard linear state space models. The time-varying parameter is modeled as a smoothing spline and then expressed as a state space model. The maximum likelihood method is used to estimate the smoothing parameter. The proposed method is assessed by a simulation study and applied to virological response data from an HIV-infected patient receiving antiretroviral treatment.     

Mixed Model-Based Hazard Estimation

This article proposes a new method for estimation of the hazard function from a set of censored failure time data, with a view to extending the general approach to more complicated models. The approach is based on a mixed model representation of penalized spline hazard estimators. One payoff is the automation of the smoothing parameter choice through restricted maximum likelihood. Another is the option to use standard mixed model software for automatic hazard estimation.     

控制点优化磨光算法在长江径流量预测中的应用

盈亏修正磨光法所得到的逼近效果仍然很差,通过控制点的参数优化和目标函数的最小,提出一种控制点优化磨光算法,利用这个算法得到参数后代入模型,使预测的精度得到提高.通过实例,该算法简单易行,并通过相对误差进行了分析,控制点优化磨光算法所得到的预测值好于神经网络模型、PPAR和小波网络模型的预测值,这为研究磨光法提供了较好的分析方法.     

Uniform consistency of automatic and location-adaptive delta-sequence estimators

Summary The class of delta-sequence estimators for a probability density includes the kernel, histogram and orthogonal series types, because each can be characterized as a collection of averages of some function that is indexed by a smoothing parameter. There are two important extensions of this class. The first allows a random smoothing parameter, for example that specified by a cross-validation method. The second allows the smoothing parameter to be a function of location, for example an estimator based on nearest-neighbor distance. In this paper a general method is presented which establishes uniform consistency for all of these estimators.Research partially supported by AFOSR Grant No. S-49620-82-C-0144, and by NSF Grant DMS-850-3347Research supported by NSF Grant DMS-8400602     

On use of the Kalman filter for spatial smoothing

A state-space model to perform discrete thin plate smoothing for data on a two-dimensional rectangular lattice is proposed with the use of the Kalman filter. The use of the Kalman filter reduces computational difficulties in the maximum likelihood estimation of a smoothing parameter. A procedure to reduce computational difficulties in the estimation of trend is given also. Numerical illustration is provided using two sets of artificial data.