文化的维度与人力资源管理

人力资源管理因其所处文化维度之不同应采取相应的思路、方式。中国文化区别于任何国家的文化 ,因此 ,只能走一条具有中国特色的人力资源管理之路     

一类新变分问题的解法及其应用

提出了一类新的变分问题，讨论了这类变分问题的解法。给出了这类变分问题在求解最优控制等实际问题中的应用。     

一类集值映射变分不等式及在凸规划中应用

给定弱连续映射 f:X■X~-*,h:X■R 是下半连续正齐次凸泛函.本文在自反空间中给定 f 某种条件下,得出形如 f(x)+(?)h(x)映射变分不等式与补问题解的存在定理,并在数学规划中得到应用.     

具有网络外部性的扩展Hotelling模型

将网络外部性引入线性运输成本下的Hotelling模型,研究了两家网络产品厂商首先选址,然后进行价格竞争的两阶段完全信息动态博弈问题,得到了厂商价格竞争均衡的存在性条件,推广了一般Hotelling模型的结论.由于网络外部性的存在,消费者效用是相互依赖的.网络产品的市场份额既是消费者选择的结果,又是消费者选择的依据.文章将交通流量分配技术和无穷维变分方法相结合,建立了刻画网络产品市场划分的模型——无穷维变分不等式,并研究了市场划分的存在性和唯一性问题.最后假定厂商定位在两端,分析了产品的网络外部性特征及单位运输成本对两个厂商竞争均衡情况的影响.     

Nash均衡、变分不等式和广义均衡问题的关系

主要讨论了Nash均衡问题(NE)与变分不等式(VI)和广义均衡问题(GEP)的关系.给出它们之间解的等价关系,以及与之相应的映射之间单调性的关系.研究结果为进一步研究Nash均衡、广义均衡问题理论及其算法提供了理论依据.     

Exact or approximate inference in graphical models: why the choice is dictated by the treewidth,and how variable elimination can be exploited

Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper, we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so‐called treewidth of the graph characterises this algorithmic complexity: low‐treewidth graphs can be processed efficiently. The first point that we illustrate is therefore the idea that for inference in graphical models, the number of variables is not the limiting factor, and it is worth checking the width of several tree decompositions of the graph before resorting to the approximate method. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a ‘good' elimination order enabling to realise exact inference. The second point is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte‐Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.     

Bayesian parameter estimation via variational methods

We consider a logistic regression model with a Gaussian prior distribution over the parameters. We show that an accurate variational transformation can be used to obtain a closed form approximation to the posterior distribution of the parameters thereby yielding an approximate posterior predictive model. This approach is readily extended to binary graphical model with complete observations. For graphical models with incomplete observations we utilize an additional variational transformation and again obtain a closed form approximation to the posterior. Finally, we show that the dual of the regression problem gives a latent variable density model, the variational formulation of which leads to exactly solvable EM updates.     

非完整可控力学系统的非等时变分方程和积分不变量

给出了非完整可控力学系统的非等时变分方程，并研究了它们的解．证明了在一定条件下可利用第一积分得到非等时变分方程的特解．利用正则方程的变分方程证明了由第一积分可直接构造系统的积分不变量．