Hilbert空间中的不可微最优化问题的基路径增量目标水平算法

主要研究了Hilbert空间中的一类不可微最优化问题的基路径增量目标水平算法.证明了当约束集合为有界集时,问题的最优解集非空,且这时通过算法生成的迭代点列是弱收敛于最优解的..     

基于圆形件排样问题的遗传算法研究

排样性问题是一类优化求解问题,在遗传算法求解过程中,若所用的算法是不收敛的,则无法得到最优解.给出了一种混合式遗传算法,并证明了算法是完全收敛的,能够得到全局最优解.     

具有机器故障的单机随机JIT排序问题

对中断－继续和中断－重复两种模型研究具有机器故障的单机随机JIT排序问题, 目标函数是期望完工时间 与工期方差和. 对中断－继续模型证明SSDE问题的最优排序具有关于期望加工时间的V-形性质, 并给出了一个拟多项式 的动态规划算法. 同时对SSDE问题和ESSD问题 进行了比较, 证明了SSDE问题的最优解是一个非常好的ESSD问题的近似最优解. 在一定的条件下, SSDE问题 的最优解就是ESSD问题的最优解. 对中断－重复模型, 由于完工时间的方差无法求出, JIT排序问题至今没得到解决, 故从实际 应用角度用SSDE问题替代ESSD问题, 证明了SSDE问题最优解具有关于期望占用机器时间的V-形性质, 并给出了 一个拟多项式的动态规划算法, 提出了一个研究JIT问题的中断－重复模型的新思路.     

带学习效应的两台平行机时间表长问题

研究机器带学习效应, 目标函数为时间表长的两台平行机排序问题, 问题是NP-难的. 首先建立了求解该问题最优解的整数规划模型. 其次, 基于模拟退火算法给出了该问题的近似算法SA, 并证明了该算法依概率1 全局收敛到最优解. 最后, 通过数值模拟对所提出的算法进行了性能分析. 数值模拟结果表明, 近似算法SA可以达到最优值的99%, 准确度高, 算法较有效.     

二层随机规划逼近解的收敛性

对二层随机规划的逼近解的收敛性作了探讨,证明了当随机向量序列{ζ(k)(w)}依分布收敛于ζ(w)时,相应于ζ(k)(w)的二层随机规划问题的任何最优解序列将收敛到原问题的最优解.     

不等式约束优化问题的低阶精确罚函数的光滑化算法

对不等式约束优化问题提出了一个低阶精确罚函数的光滑化算法. 首先给出了光滑罚问题、非光滑罚问题及原问题的目标函数值之间的误差估计,进而在弱的假
设之下证明了光滑罚问题的全局最优解是原问题的近似全局最优解. 最后给出了一个基于光滑罚函数的求解原问题的算法,证明了算法的收敛性,并给出数值算例说明算法的可行性.     

带机器故障的两台机带权误工数排序问题

讨论机器带故障中断的两台平行机排序问题,工件加工时间均为单位时间,目标是极小化带权误工工件数.当转移时间t=0时给出了最优的算法.当t≠0时,给出了一个多项式时间的近似算法,并证明算法解与最优解至多相差一个带权误工数.     

一类线性乘积规划问题的分支定界缩减方法

提出了求解一类线性乘积规划问题的分支定界缩减方法, 并证明了算法的收敛性.在这个方法中, 利用两个变量乘积的凸包络技术, 给出了目标函数与约束函数中乘积的下界, 由此确定原问题的一个松弛凸规划, 从而找到原问题全局最优值的下界和可行解. 为了加快所提算法的收敛速度, 使用了超矩形的缩减策略. 数值结果表明所提出的算法是可行的.     

基于随机搜索的一个约束优化问题的全局收敛算法

讨论了具有一般约束的全局优化问题,给出该问题的一个随机搜索算法,证明了该算法依概率1收敛到问题的全局最优解．数值结果显示该方法是有效的．     

一类0-1二次规划最优解的新算法

从矩阵的基础知识出发,给出了当目标函数矩阵是严格对角占优阵时,快速地获得0-1二次规划最优解的一个新算法;该方法具有很强的实用性,是此类问题的一个高效求解算法.     

On conditional weak convergence

In this paper we discuss a number of technical issues associated with conditional weak convergence. The main modes of convergence of conditional probability distributions areuniform, probability, andalmost sure convergence in the conditioning variable. General results regarding conditional convergence are obtained, including details of sufficient conditions for each mode of convergence, and characterization theorems for uniform conditional convergence.     

Convergence of sequences of semi-continuous functions

In this paper we investigate how three well-known modes of convergence for (real-valued) functions are related to one another. In particular, we consider order convergence, pointwise convergence and continuous convergence of sequences of nearly finite normal lower semi-continuous functions. There is a natural comparison to be made between the results we obtain for convergence of sequences of semi-continuous functions, and classic results on the convergence of sequences of measurable functions.     

集值逆上鞅的收敛定理及应用

研究了取值于Ｂａｎａｃｈ空间的集值逆上鞅的收敛性，给出了集值逆上鞅在集列Ｗｉｊｓｍａｎ收敛，弱收敛及Ｋｕｒａｔｏｗｓｋｉ－Ｍｏｓｃｏ收敛意义下的收敛定理，并给出了它们在连续参数集值鞅中的应用。     

Convergence of the Nelder-Mead method

We develop a matrix form of the Nelder-Mead simplex method and show that its convergence is related to the convergence of infinite matrix products. We then characterize the spectra of the involved matrices necessary for the study of convergence. Using these results, we discuss several examples of possible convergence or failure modes. Then, we prove a general convergence theorem for the simplex sequences generated by the method. The key assumption of the convergence theorem is proved in low-dimensional spaces up to 8 dimensions.

     

Range convergence monotonicity for vector measures and range monotonicity of the mass

We prove that the range of sequence of vector measures converging widely satisfies a weak lower semicontinuity property, that the convergence of the range implies the strict convergence (convergence of the total variation) and that the strict convergence implies the range convergence for strictly convex norms. In dimension 2 and for Euclidean spaces of any dimensions, we prove that the total variation of a vector measure is monotone with respect to the range.     

Convergence Structures for Categories

We introduce and study the concept of a convergence structure for a category. Such a structure is obtained by endowing each object of the category with a convergence class subjected to some basic convergence axioms. As a tool for expressing the convergence we use categorically viewed nets which generalize the usual ones. After describing relations between convergence structures and closure operators for categories, we investigate separatedness and compactness of objects of a category with respect to a convergence structure.     

关于随机序列加权和的收敛性的注记

通过对数组加权系数的列截尾,研究了B值随机一致有界序列加权和的收敛性,获得了相应的几乎处处收敛和完全收敛到0的结果.最后建立了B值随机适应序列加权和的矩收敛定理.     

Complete Moment Convergence for Arrays of Rowwise Widely Orthant Dependent Random Variables

In this paper, complete moment convergence for widely orthant dependent random variables is investigated under some mild conditions. For arrays of rowwise widely orthant dependent random variables, the main results extend recent results on complete convergence to complete moment convergence. These results on complete moment convergence are shown to yield new results on complete integral convergence.     

Convergence on Categories

We introduce and study a concept of a convergence structure on a concrete category. The concept is based on using certain generalized filters for expressing the convergence. Some basic properties of the convergence structures are discussed. In particular, we study convergence separation and convergence compactness and investigate relationships between the convergence structures and the usual closure operators on categories. Dedicated to Professor E. Giuli and Professor G. Preuss on the occasion of their 65th birthdays.     

Relationships between different types of initial conditions for simultaneous root finding methods

The construction of initial conditions of an iterative method is one of the most important problems in solving nonlinear equations. In this paper, we obtain relationships between different types of initial conditions that guarantee the convergence of iterative methods for simultaneously finding all zeros of a polynomial. In particular, we show that any local convergence theorem for a simultaneous method can be converted into a convergence theorem with computationally verifiable initial conditions which is of practical importance. Thus, we propose a new approach for obtaining semilocal convergence results for simultaneous methods via local convergence results.