Scenario Reduction Algorithms in Stochastic Programming

We consider convex stochastic programs with an (approximate) initial probability distribution P having finite support supp P, i.e., finitely many scenarios. The behaviour of such stochastic programs is stable with respect to perturbations of P measured in terms of a Fortet-Mourier probability metric. The problem of optimal scenario reduction consists in determining a probability measure that is supported by a subset of supp P of prescribed cardinality and is closest to P in terms of such a probability metric. Two new versions of forward and backward type algorithms are presented for computing such optimally reduced probability measures approximately. Compared to earlier versions, the computational performance (accuracy, running time) of the new algorithms has been improved considerably. Numerical experience is reported for different instances of scenario trees with computable optimal lower bounds. The test examples also include a ternary scenario tree representing the weekly electrical load process in a power management model.     

严格渐进伪压缩映象之修正型Mann迭代算法的强收敛性

本文利用CQ方法获得了k-严格渐进伪压缩映象修正型迭代算法的强收敛结果.此结果推广并改进了T.H.Kim和徐洪坤2006年获得的相应的一主要结果.即,从渐进非扩张映象推广到k-严格渐进伪压缩映象,并且去掉了闭凸子集C的有界性假设条件.     

四维时空中场方程表述形式的推导方法

场方程的推导在量子场论中占有很重要的地位。鉴于在大多数理论物理教程中都是仅仅给出场方程的形式,而鲜有得出场方程的具体推导过程。从物质场的作用量S(其中拉格朗日函数具有对称性)和四维平直时空、四维弯曲时空的度规出发,应用变分原理及数学知识,分别给出了四维平直时空和四维弯曲时空中物质场场方程的具体推导过程,供广大理论物理学习者和爱好者参考。     

Finsler几何中Bao-Chern联络的合理性

利用标架场的变换公式证明David Bao与S.S.Cheern确定的射影化芬斯 拉联络的合理性。     

A Model with Exact Inflationary Solution in Finsler Universe

In this paper, using the Gravity’s Rainbow theory, we introduce rainbow metric into rainbow Robertson-Walker metric, and obtain a model which depends on the energy of probe particles. Furthermore, we research on an exact inflationary solution of the model, and it can be consistent with the conclusions of observation. The results of our research show that some details in inflation depend on the energy of particles which are observed by observers.     

基于黎曼度量的最小二乘支持向量机模型选择

针对最小二乘支持向量机(LS-SVM)模型选择效果不稳定、易于过学习的问题,提出了一种基于黎曼度量的模型选择方法.首先,基于信息几何理论,证明了LS-SVM模型泛化能力受样本点二阶协变张量的影响;其次,进一步证明了同时最小化所有样本点的黎曼度量之和与权重向量的L2范数即可提高模型泛化能力;在此基础上,将LS-SVM模型选择转换为一个多目标优化问题,引入多目标粒子群算法选取最优超参数.采用仿真与真实UCI数据集对所提方法进行了对比实验,结果表明,与传统LS-SVM与基于留一法模型选择的LS-SVM相比,所提方法可以取得更小的泛化误差,同时数值稳定性更好.     

芬斯拉度量的一般提升

本文把Matsumoto提出的芬斯拉基本张量g到切丛的α提升推广成一般提升,其N-分解为满足α_(αβ)=α_(βα)。对于加当联络的情形,证明了使芬斯拉型线性联络关于为度量的充要条件是α_(αβ)均为常数。当取α_(11)=α_(22)=0时,所得应变张量场N-分解的8个芬斯拉张量场中有5个不为零,且都是正齐次的,最后还指出,只有这种取法才能获得正齐次的N-分解张量场。     

最优ε策略的性质及线性度量对策的值

本文确定了线性度量对策两个局中人具有特定的最优ε策略类,利用这些策略类的性质,估计了线性度量对策的值.