基于增广Lagrange函数的RQP方法

Recursive quadratic programming is a family of techniques developd by Bartholomew-Biggs and other authors for solving nonlinear programming problems.This paperdescribes a new method for constrained optimization which obtains its search di-rections from a quadratic programming subproblem based on the well-known aug-mented Lagrangian function.It avoids the penalty parameter to tend to infinity.We employ the Fletcher‘s exact penalty function as a merit function and the use of an approximate directional derivative of the function that avoids the need toevaluate the second order derivatives of the problem functions.We prove that thealgorithm possesses global and superlinear convergence properties.At the sametime, numerical results are reported.     

一类二层优化问题的极大熵方法

文章对一类下层带有公差的特殊的二层优化问题构造出不同于文[1]-[4]的极大熵函数来近似表示下层极值函数,地不可微二层优化问题转化为可微优化问题来处理，从而得到一类二层优化问题的ε－最优解的一种计算方法。     

二阶段随机规划问题基于随机模拟的遗传算法

利用遗传算法不过多依赖目标函数性质．适应于全局搜索的特点．提出了求解二阶段随机规划的基于随机模拟的遗传算法，算法采用随机模拟技术利用样本均值近似代替期望值，使计算得以简化，计算实例表明该算法是有效和可行的。     

再论集体次正规空间的逆极限

首先给出集体次正规空间的一组等价刻画.利用该组刻画证明:设X=lim{Xσ,πρσ,∑}并且每个投影映射πσ:X→Xσ是开满映射, (1)如果X是|∑|-仿紧的且每个Xσ是集体次正规空间,则X是正规集体次正规空间; (2)如果X是遗传|∑|-仿紧的且每个Xσ是遗传集体次正规空间,则X是遗传集体次正规空间.然后,在X=Ⅱα∈AXα是|A|-仿紧的条件下得到结果:X是集体次正规的当且仅当(?)F∈[A]<ω,Ⅱσ∈FXσ是集体次正规的,并且遗传集体次正规也有类似性质.     

遗传中紧空间与散射分解

本文证明了可数仿紧(中紧、亚紧)空间有类似Junnila的刻画，遗传中紧空间不具有类似Junnila的刻画，并给出了每个散射分解有紧有限的开膨胀的充要条件．     

求解二层规划问题的遗传算法

本文求解二层规划问题的遗传算法，给出了算法基本框架并对算法实现进行了研究．算法适用于各类线性和非线性二层规划问题．数值计算结果显示，该方法是可行和有效的．     

凹整数规划的分枝定界解法

凹整数规划是一类重要的非线性整数规划问题，也是在经济和管理中有着广泛应用的最优化问题．本文主要研究用分枝定界方法求解凹整数规划问题，这一方法的基本思想是对目标函数进行线性下逼近，然后用乘子搜索法求解连续松弛问题．数值结果表明，用这种分枝定界方法求解凹整数规划是有效的．     

基于q-理论的家族企业动态模型

本文在不完全市场下,基于托宾q-理论,以最大化企业家的CRRA期望效用为目标,在假设企业资本和生产力均随机的条件下,研究家族企业的动态优化问题,即家族企业在动态经营过程中的生产、消费和投资组合的最优决策问题.利用动态规划原理推导出企业最优决策所满足的HJB方程,并通过企业适用的生产消费均衡理论和风险对冲需求对此HJB方程进行求解,得到家族企业的最优生产、最优消费和最优投资策略.最后选取合适参数对家族企业的最优策略进行数值分析,结果表明了企业的最优策略与其流动性之间的变化关系.     

下层独立的一主多从双层随机线性规划问题研究

Herminia I.Calvete等研究了一主多从双层确定性线性规划问题,证明了这类问题等价于一类常规的双层线性规划问题.本文在此基础上,推广确定型的问题到随机型优化情况,考虑了一类下层优化相互独立的一主多从双层随机优化问题(SLBMFP).在特定的随机变量分布条件下,理论上证明了该类问题可以转化为一主一从双层确定性优化问题.本文的研究对于求解一主多从双层随机优化模型,解决此类模型在实际应用中的问题具有一定的意义.     

Unique factorization of compositive hereditary graph properties

A graph property is any class of graphs that is closed under isomorphisms. A graph property P is hereditary if it is closed under taking subgraphs; it is compositive if for any graphs G1, G2 ∈ P there exists a graph G ∈ P containing both G1 and G2 as subgraphs. Let H be any given graph on vertices v1, . . . , vn, n ≥ 2. A graph property P is H-factorizable over the class of graph properties P if there exist P 1 , . . . , P n ∈ P such that P consists of all graphs whose vertex sets can be partitioned into n parts, possibly empty, satisfying: 1. for each i, the graph induced by the i-th non-empty partition part is in P i , and 2. for each i and j with i = j, there is no edge between the i-th and j-th parts if vi and vj are non-adjacent vertices in H. If a graph property P is H-factorizable over P and we know the graph properties P 1 , . . . , P n , then we write P = H [ P 1 , . . . , P n ]. In such a case, the presentation H[ P 1 , . . . , P n ] is called a factorization of P over P. This concept generalizes graph homomorphisms and (P 1 , . . . , P n )-colorings. In this paper, we investigate all H-factorizations of a graph property P over the class of all hered- itary compositive graph properties for finite graphs H. It is shown that in many cases there is exactly one such factorization.