遗传算法基础理论研究的新近发展

本文综述了有关遗传算法收敛性及收敛速度估计的近期研究结果,在分类概述相关的Ｖｏｓｅ－Ｌｉｅｐｉｎｓ模型、Ｍａｒｋｏｖ链模型、公理化模型、连续（积分算子）模型及收敛速度估计、迭代次数估计与时间复杂性估计的基础上,指出遗传算法理论研究存在的其它亟待解决的问题。     

非协调元特征值渐近下界

利用有限元收敛速度下界的结果获得某些非协调元方法新的Aubin-Nitsche估计形式,然后再结合非协调元特征值的展开式获得不需要额外条件下非协调元特征值渐近下界的结果.     

线性模型中参数向量的线性经验Bayes估计

本文利用线性经验Ｂａｙｅｓ估计的思想，对线性回归模型中未知参数向量构造了一类线性经验Ｂａｙｅｓ估计，并在一定条件下证明了其具有的ａ．ｏ．收敛速度．     

关于一维随机环境中非最近邻居的随机游动的尾估计

本文研究了一类一维随机环境中非最近邻居的随机游动,在暂留的情况下,给出了它的速度,并进一步研究了其偏离速度的尾概率的估计,证明了这个尾概率是以多项式的速率衰减,给出了这个指数．我们的结果是Zeitouni及其合作者在1996年的文章中结果的推广．在证明中我们用到了随机矩阵乘积的大偏差估计及随机环境中多型分支过程的总人口数的尾概率估计和矩量估计．     

熵损失下一类广义指数分布刻度参数的经验Bayes估计[英文]

本文在熵损失函数下获得一类广义指数分布刻度参数的贝叶斯估计,并构造了相应的经验贝叶斯估计.在适当的条件下证明所提出的经验贝叶斯估计的渐近最优性并获得了其收敛速度.最后,给出一个有关本文主要结果的例子.     

一类纵向数据半参数模型估计的收敛速度

本文研究了一类纵向数据半参数模型参数和回归函数的估计问题.利用最小二乘法和一般的非参数权函数方法,获得了参数估计量的强收敛速度和回归函数估计量的一致收敛速度,推广了文献[4]的相应结果.     

带超级光滑噪声密度函数的小波自适应点态估计

利用小波方法在局部Holder空间中研究一类反卷积密度函数的点态估计问题.首先,针对超级光滑噪声给出该模型任一估计器的点态风险下界;其次,构造有限求和小波估计器,并证明其在超级光滑噪声条件下达到了最优收敛阶,即该估计器在点态风险下的收敛速度与下界一致.最后,还讨论了这类小波估计器的强收敛性.值得指出的是上述估计都是自适应的.     

矩阵特征值的一类新的包含域

用盖尔圆盘定理来估计矩阵的特征值是一个经典的方法,这种方法仅利用矩阵的元素来确定特征值的分布区域.本文利用相似矩阵有相同的特征值这一理论,得到了矩阵特征值的一类新的包含域,它们与盖尔圆盘等方法结合起来能提高估计的精确度.     

样条变差缩减算子迭代极限的一个简单证明

一、引言在[1]和[2]中,胡莹生、徐叔贤利用 Markov 链终极条件概率的有关结果,确定了一类变差缩减算子的迭代极限.本文采用作者在[3]中使用的技巧,利用多项式样条的若干基本知识,不但简单地得出了[1,2]的结果,并且给出了迭代收敛速度的估计.对于等距分划的三次样条,所给的误差估计在某种意义上是最好的.     

混合误差下回归函数小波估计的一致收敛速度

该文构造了回归函数的一类小波估计，在误差序列为ψ 混合或φ 混合下得到了小波估计的强一致收敛速度和狉阶矩一致收敛速度．     

一类新的信赖域算法的全局收敛性

本文对于无约束最优化问题提出了一类非单调的信赖域算法，它是通常的单调信赖域算法的推广。当目标函数是有下界的连续可微函数，而且它的二阶导数的近似的模是线性地依赖于迭代次数时，我们证明了新算法的整体收敛性。     

A Component-Wise EM Algorithm for Mixtures

Maximum likelihood estimation in finite mixture distributions is typically approached as an incomplete data problem to allow application of the expectation-maximization (EM) algorithm. In its general formulation, the EM algorithm involves the notion of a complete data space, in which the observed measurements and incomplete data are embedded. An advantage is that many difficult estimation problems are facilitated when viewed in this way. One drawback is that the simultaneous update used by standard EM requires overly informative complete data spaces, which leads to slow convergence in some situations. In the incomplete data context, it has been shown that the use of less informative complete data spaces, or equivalently smaller missing data spaces, can lead to faster convergence without sacrifying simplicity. However, in the mixture case, little progress has been made in speeding up EM. In this article we propose a component-wise EM for mixtures. It uses, at each iteration, the smallest admissible missing data space by intrinsically decoupling the parameter updates. Monotonicity is maintained, although the estimated proportions may not sum to one during the course of the iteration. However, we prove that the mixing proportions will satisfy this constraint upon convergence. Our proof of convergence relies on the interpretation of our procedure as a proximal point algorithm. For performance comparison, we consider standard EM as well as two other algorithms based on missing data space reduction, namely the SAGE and AECME algorithms. We provide adaptations of these general procedures to the mixture case. We also consider the ECME algorithm, which is not a data augmentation scheme but still aims at accelerating EM. Our numerical experiments illustrate the advantages of the component-wise EM algorithm relative to these other methods.     

The trade-off between regularity and stability in Tikhonov regularization

When deriving rates of convergence for the approximations generated by the application of Tikhonov regularization to ill-posed operator equations, assumptions must be made about the nature of the stabilization (i.e., the choice of the seminorm in the Tikhonov regularization) and the regularity of the least squares solutions which one looks for. In fact, it is clear from works of Hegland, Engl and Neubauer and Natterer that, in terms of the rate of convergence, there is a trade-off between stabilization and regularity. It is this matter which is examined in this paper by means of the best-possible worst-error estimates. The results of this paper provide better estimates than those of Engl and Neubauer, and also include and extend the best possible rate derived by Natterer. The paper concludes with an application of these results to first-kind integral equations with smooth kernels.

     

父代种群参与竞争遗传算法几乎必然收敛

熟知,标准遗传算法如不采用“杰出者记录策略”则必不收敛。本文发现：允许父代种群参与竞争是标准遗传算法几乎必然收敛的条件。特别地,我们运用鞅收敛定理证明：允许父代种群参与竞争型遗传算法能以概率1确保在限步内达到全局最优解,且收敛与种群规模无关。所获结果对该类遗传算法的应用奠定了可靠基础。     

MULTIGRID ALGORITHM FOR THE COUPLING SYSTEM OF NATURAL BOUNDARY ELEMENT METHOD AND FINITE ELEMENT METHOD FOR UNBOUNDED DOMAIN PROBLEMS

In this paper, some V-cycle multigrid algorithms are presented for the coupling system arising from the discretization of the Dirichlet exterior problem by coupling the natural boundary element method and finite element method. The convergence of these multigrid algorithms is obtained even with only one smoothing on all levels. The rate of convergence is found uniformly bounded independent of the number of levels and the mesh sizes of all levels, which indicates that these multigrid algorithms are optimal. Some numerical results are also reported.     

级数和无穷乘积关系的某些推广和一类递推算法的收敛性

级数和无穷乘积之间的关系是数学分析中一个古典问题。近代在讨论随机逼近的渐近性质和计算数学中带误差地迭代求解非线性方程组时,研究了一类递推算法的收敛性质,上述关系中某些看来很初等的结论却成为有用的工具。但过去仅仅在一维情况讨论和描述这些结论,使得一些用它作为研究工具的重要结论不能完全平行地推广到     

Schwarz methods for inequalities with contraction operators

We prove the convergence of some multiplicative and additive Schwarz methods for inequalities which contain contraction operators. The problem is stated in a reflexive Banach space and it generalizes the well-known fixed-point problem in the Hilbert spaces. Error estimation theorems are given for three multiplicative algorithms and two additive algorithms. We show that these algorithms are in fact Schwarz methods if the subspaces are associated with a decomposition of the domain. Also, for the one- and two-level methods in the finite element spaces, we write the convergence rates as functions of the overlapping and mesh parameters. They are similar with the convergence rates of these methods for linear problems. Besides the direct use of the five algorithms for the inequalities with contraction operators, we can use the above results to obtain the convergence rate of the Schwarz method for other types of inequalities or nonlinear equations. In this way, we prove the convergence and estimate the error of the one- and two-level Schwarz methods for some inequalities in Hilbert spaces which are not of the variational type, and also, for the Navier–Stokes problem. Finally, we give conditions of existence and uniqueness of the solution for all problems we consider. We point out that these conditions and the convergence conditions of the proposed algorithms are of the same type.     

一类带非精确线搜索的修改的Broyden算法

对于文（８）和（１４）中提出的修改的Ｂｒｏｙｄｅｎ算法，本文讨论它在线搜索非精确时的收敛性质，证明这类算法作用于梯度满足Ｌｉｐｓｃｈｉｔｚ条件的目标函数时是整体收敛的，当目标函数一致凸时，算法是Ｑ－超线性收敛和二阶收敛的。     

一类求解凸规划的鞍点法

根据凸规划的Kuhn-Tucker定理,有a)假如(x~*,y~*)是L(x,y)在D上的鞍点,那么 (1)x~*是(CP)问题的最优解,     

一类超线性收敛的广义拟Newton算法

１引言考虑无约束最优化问题其中目标函数ｆ（ｘ）二阶连续可微,记ｆｋ＝ｆ（ｘ）,当充分小时,有如下近似关系：它们对二次函数皆严格成立．考虑选代其中Ｂ（Ｇ的近似）已知,为某种线搜索确定的步长．对Ｂ修正产生Ｂ,即Ｕ为待定ｎ阶矩阵．若要求Ｂ＋满足关系即Ｂ满足拟Ｎｅｗｔｏｎ方程,由它可导出许多著名的拟Ｎｅｗｔｏｎ算法［１－［４］）．若要求Ｂ满足关系则可导出伪Ｎｅｗｔｏｎ－δ族校正公式,它不再是Ｈｕａｎｇ族成员［６］．从信息资源的利用看,（１．６）仅利用了与信息,（１．７）仅利用了与信息．一般而言,较多的信…