求解互补约束优化问题的乘子松弛法

利用互补问题的Lagrange函数, 给出了互补约束优化问题\,(MPCC)\,的一种新松弛问题. 在较弱的条件下, 新松弛问题满足线性独立约束规范. 在此基础上, 提出了求解互补约束优化问题的乘子松弛法. 在MPCC-LICQ条件下, 松弛问题稳定点的任何聚点都是MPCC的M-稳定点. 无需二阶必要条件, 只在ULSC条件下, 就可保证聚点是MPCC的B-稳定点. 另外, 给出了算法收敛于B-稳定点的新条件.     

基于改进ICP算法的曲面特征配准研究

随着3D激光扫描技术的发展,点云数据的应用越来越广泛.然而点云配准一直是点云数据预处理过程中的一个关键问题.目前ICP算法是实现点云配准的主流算法.然而面对数据量大、噪声大的点云数据时,ICP算法在执行的配准效率和执行效果上不够理想.本文通过PCA算法,提取了点云数据集的方向向量,根据源数据与目标数据的方向向量,初步设定了旋转矩阵R的值.此外,定义了源数据与目标数据的曲面距离,在此基础上改进了传统的ICP算法.将改进后的ICP算法成功的应用到点云数据配准中来,提高了点云数据的配准效果,并压缩了算法的执行时间.     

自相似测度的联合发散点和填充维数(英文)

近年来,发散点引起了数学界的广泛关注.对单测度的发散点,前人已经作了很完备的研究.对有限多个自相似测度的联合发散点的集合,仅其豪斯多夫维数被L.Olsen研究并给出了,然而,我们对其填充维数仍一无所知.因此,本文主要研究联合发散点集合的填充维数.     

小波检测并估计非参函数变点

研究随机设计下非参函数变点的小波检测与估计问题.将小波方法与设计点转化方法相结合给出变点的检测统计量并研究检测的一致性.给出了变点个数和变点位置的估计量,证明了变点个数估计量的相合性并得到变点位置估计量的收敛速度.     

老师说，这叫“沈雯公式”

学完有理数,我在家里复习,遇到这样一个问题:已知数轴上三点A、B、C分别表示有理数a、1、-1,那么 |a+1 |表示(　)A. A、B两点距离;B. A、C两点距离;C. A、B两点到原点距离之和;D. A、C两点到原点距离之和.从“距离”去试验:我思考了很长时间,可依然想不出,翻开答案,正确答案为B,我百思不得其解,点A与点B的关系如何扯上了点C?无奈下,我勇敢地给老师打了电话. 老师只说了一句话:“用数轴上两个具体点的距离去试试.”我开始仔细地考虑“两个具体点”,可以从 5个角度考虑:(1)两个正数(2)两个负数(3)一正一负(4)零与正数(5)零与负数…     

一种偏微分方程间断点的反演方法

本文研究了一种抛物型方程间断参数的识别问题.利用未知间断点作为反演点和遗传算法优化参数,获得了间断点和反演解.数值实验结果表明反演解和真实解非常接近.     

对称的稳定分布参数变点估计的相合性

假设稳定分布的特征指数α满足1<α<2,关于均值μ对称. 本文讨论了稳定分布中α或刻度参数β的变化导致的变点问题,即是否发生变化及变化时刻.若均值已知,当α或β改变时,密度函数f(x)在μ处的值f(μ)发生变化,我们利用密度函数的核估计来估计该点的值. 若均值未知,利用经验特征函数估计该点的值,并进一步讨论了估计的相合性与收敛速度. 其次讨论了均值变化导致的变点问题,若均值发生变化,相应变点前后特征函数的参数将变化,利用经验特征函数给出了变点的估计, 获得了类似的收敛速度. 最后给出了检测金融市场突变性的应用.     

最大内角不等于120°的三角形性质

<正>费马点以三角形各边为边长向形外作等边三角形,则三个等边三角形的外接圆共点.该点称为三角形的费马点.显然,最大内角小于120°的三角形的费马点在形内,最大内角大于120°的三角形的费马点在形外,最大内角等于120°的三角形的费马点是120°角的顶点.本文对最大内角等于120°的三角形不作介绍了     

BCH-代数的Ω-模糊点理想

引入BCH-代数的Ω-模糊点理想的概念,给出了它的几个实例,研究了它们的一些性质.讨论了Ω-模糊理想与Ω-模糊点理想的关系,获得了Ω-模糊点理想的几个等价描述,研究了Ω-模糊点理想的同态像与同态原像的性质,给出了BCH-代数的Ω-模糊点理想与BCH-代数之积代数的Ω-模糊点理想的关系, 讨论了模糊点理想与Ω-模糊点理想之间的相互构造.     

不等式约束优化全局收敛的滤子线性方程组算法

设计了求解不等式约束非线性规划问题的一种新的滤子序列线性方程组算法,该算法每步迭代由减小约束违反度和目标函数值两部分构成.利用约束函数在某个中介点线性化的方法产生搜索方向.每步迭代仅需求解两个线性方程组,计算量较小.在一般条件下,证明了算法产生的无穷迭代点列所有聚点都是可行点并且所有聚点都是所求解问题的KKT点.     

节点有自环的网络流数学模型

以网络流中节点有自环的情形为对象进行研究,把节点分开为入点和出点,节点自环转换为流量相同,方向相反的两条弧,改进了网络流数学模型.改进后的数学模型在处理原来网络流中节点发生异常情况时,即节点不遵守流量守恒条件时,其节点自环的流能够起特殊的调节作用.在描述网络流异常状态时,给出了网络流状态周期的阶跃性质.通过网络模型对应的邻接矩阵对网络流进行计算和监控,给出一个节点环流的应用实例.     

Two-stage queueing network models for quality control and testing

We study sojourn times in a two-node open queueing network with a processor sharing node and a delay node, with Poisson arrivals at the PS node. Motivated by quality control and blood testing applications, we consider a feedback mechanism in which customers may either leave the system after service at the PS node or move to the delay node; from the delay node, they always return to the PS node for new quality controls or blood tests. We propose various approximations for the distribution of the total sojourn time in the network; each of these approximations yields the exact mean sojourn time, and very accurate results for the variance. The best of the three approximations is used to tackle an optimization problem that is mainly inspired by a blood testing application.     

On the Riesz constants for systems of integer translates

In this paper, one-parameter families of integer translates of the Gaussian and Lorentz functions are studied. For a Lorentz function, we obtain formulas for the coefficients of the series defining node functions and show that the limit value of node functions is given by a sample function. For systems of translates generated by the Gaussian and Lorentz functions as well as by the node functions related to them, we obtain explicit expressions for the Riesz constants and study the parameter-dependent behavior of these constants. While proving some of the results of this paper, we establish the monotonicity of a special ratio of two Jacobi theta functions, a fact which is of interest in itself.     

一类最小支撑树的逆问题及其求解方法

本文针对传统的基于边的最小支撑树逆问题，提出了一类基于点边更新策略的最小支撑树逆问题．更新一个点是指减少与此点相关联的某些边的权值．根据是否含有更新点的费用，考虑了两类模型，它们均可转化为森林上的最小(费用)点覆盖的求解问题，算法的复杂性都是O(mn)，其中m=|E|n=|V|。     

Persistence of centrality in random growing trees

We investigate properties of node centrality in random growing tree models. We focus on a measure of centrality that computes the maximum subtree size of the tree rooted at each node, with the most central node being the tree centroid. For random trees grown according to a preferential attachment model, a uniform attachment model, or a diffusion processes over a regular tree, we prove that a single node persists as the tree centroid after a finite number of steps, with probability 1. Furthermore, this persistence property generalizes to the top K ≥ 1 nodes with respect to the same centrality measure. We also establish necessary and sufficient conditions for the size of an initial seed graph required to ensure persistence of a particular node with probability , as a function of ϵ: In the case of preferential and uniform attachment models, we derive bounds for the size of an initial hub constructed around the special node. In the case of a diffusion process over a regular tree, we derive bounds for the radius of an initial ball centered around the special node. Our necessary and sufficient conditions match up to constant factors for preferential attachment and diffusion tree models.     

Finding the shortest paths by node combination

By repeatedly combining the source node’s nearest neighbor, we propose a node combination (NC) method to implement the Dijkstra’s algorithm. The NC algorithm finds the shortest paths with three simple iterative steps: find the nearest neighbor of the source node, combine that node with the source node, and modify the weights on edges that connect to the nearest neighbor. The NC algorithm is more comprehensible and convenient for programming as there is no need to maintain a set with the nodes’ distances. Experimental evaluations on various networks reveal that the NC algorithm is as efficient as Dijkstra’s algorithm. As the whole process of the NC algorithm can be implemented with vectors, we also show how to find the shortest paths on a weight matrix.     

Truncation and roundoff errors in three-point approximations of first and second derivatives

We use linear combinations of Taylor expansions to develop three-point finite difference expressions for the first and second derivative of a function at a given node. We derive analytical expressions for the truncation and roundoff errors associated with these finite difference formulae. Using these error expressions, we find optimal values for the stepsize and the distribution of the three points, relative to the given node. The latter are obtained assuming that the three points are equispaced. For the first derivative approximation, the distribution of the points relative to the given node is not symmetrical, while it is so for the second derivative approximation. We illustrate these results with a numerical example in which we compute upper bounds on the roundoff error.     

人力资源网络模型及其动力学分析在企业内部升迁和外部招聘中的应用

首先讨论了ICM公司职位分配到职员办公室的原则,并讨论了在这些原则下一个合理分配方案的合作网络和监管网络构成的混合网络模型;然后,利用该模型的节点属性、公司职员属性及两者之间的关系建立了后档案矩阵,讨论了公司职员属性函数,在此基础上研究了包含职员离职算法、内部升迁算法和外部招聘算法的动力学分析;最后,在混合网络模型中利用算法进行了动力学仿真。     

The degree ratio ranking method for directed graphs

One of the most famous ranking methods for digraphs is the ranking by Copeland score. The Copeland score of a node in a digraph is the difference between its outdegree (i.e. its number of outgoing arcs) and its indegree (i.e. its number of ingoing arcs). In the ranking by Copeland score, a node is ranked higher, the higher is its Copeland score. In this paper, we deal with an alternative method to rank nodes according to their out- and indegree, namely ranking the nodes according to their degree ratio, i.e. the outdegree divided by the indegree. To avoid dividing by zero, we add 1 to both the out- as well as indegree of every node. We provide an axiomatization of the ranking by degree ratio using a clone property, which says that the entrance of a clone or a copy (i.e. a node that is in some sense similar to the original node) does not change the ranking among the original nodes. We also provide a new axiomatization of the ranking by Copeland score using the same axioms except that this method satisfies a different clone property. Finally, we modify the ranking by degree ratio by taking only the out- and indegree, but by definition assume nodes with indegree zero to be ranked higher than nodes with positive indegree. We provide an axiomatization of this ranking method using yet another clone property and a maximal property. In this way, we can compare the three ranking methods by their clone property.     

Scheduling for single agile satellite,redundant targets problem using complex networks theory

Scheduling for the Earth observation satellites (EOSs) imaging mission is a complicated combinatorial optimization problem, especially for the agile EOSs (AEOSs). The increasing observation requirements and orbiting satellites have exacerbated the scheduling complexity in recent years. In this paper, the single agile satellite, redundant observation targets scheduling problem is studied. We introduce the theory of complex networks and find similarities between AEOS redundant targets scheduling problem and the node centrality ranking problem. Then we model this problem as a complex network, regarding each node as a possible observation opportunity, and define two factors, node importance factor and target importance factor, to describe the node/target importance. Based on the two factors, we propose a fast approximate scheduling algorithm (FASA) to obtain the effective scheduling results. Simulation results indicate the FASA is quite efficient and with broad suitability. Our work is helpful in the EOSs and AEOSs scheduling problems by using complex network knowledge.