关于一道复数题答案的对白

高中课本代数下册复习参考题八的 2 ( 2 )题 :判断命题“复数集Ｃ与复平面内所有向量的集合一一对应”的真假 ,并说明理由 .有人给出答案 :假 ,因为实际上是复数集Ｃ与复平面内所有以原点Ｏ为起点的向量所成的集合一一一对应 .下面就是教师 (Ｂ)与学生 (Ａ)关于这个答案的讨论 .Ａ :这个答案很有道理 .复数集与平面内以原点为起点的向量集 (记作Ｖ)一一对应 .平面内所有向量的集合 (记作Ｖ′)的元素比集合Ｖ的元素多得多 ,所以集合Ｃ与Ｖ′就不能一一对应 .Ｂ :似是而非 .首先 ,向量 (ｖｅｃｔｏｒ ,亦称矢量 )有且只有两个要素———方向与…     

层次P-集合的属性规律发现

P-集合是把动态特性引入到有限普通集合X内,改进有限普通集合X得到的.层次P-集合是对普通P-集合的扩展,具有层次结构和链式结构.利用层次P-集合的性质,研究层次P-集合属性元素与规律,给出层次结构间属性元素的关系及度量,给出链式结构中属性元素的关系及度量,给出属性规律.     

Banach空间中关于变分不等式组与严格伪压缩映射的粘滞逼近法

本文利用粘滞逼近法建立了一迭代序列来逼近两个集合的公共元素,这两个集合分别是Banach空间中广义变分不等式组的解集与Banach空间中有限个严格伪压缩映射的公共不动点集.本文证明了该迭代序列强收敛到这两个集合的某一公共元素,且该元素为某一变分不等式的解.本文的结果提高与推广了许多相关结论.     

怎样利用集合元素的性质解题

在学习集合概念时,同学们对元素的性质,即元素的确定性、互异性、无序性这些性质记得住、背得过,就是不会用.为了帮助同学们解决这个问题,本文对其进行研究.这个问题往往与两个集合相等相联系,两个集合相等指的是两个集合中元素对应相等.要判断集合中元素相等自然要用到元素的性质.一、直接求解检验法     

内P-反动态信息特征与信息恢复

P-集合(packet sets)是一个动态模型,P-集合是由内P-集合x~F(internal packet set X~F)与外P-集合XF(~Fouter packet set X~F)构成的元素集合对;或者(X~F,X~F)是P-集合.利用内P-集合的结构,给出内P-信息,内P-反动态信息,信息的内P-反动态恢复概念,给出内P-反动态信息的属性合取收缩生成,给出内P-反动态信息与内P-信息同属性定理,给出内P-反动态信息存在与属性合取范式定理,给出信息的内P-反动态恢复属性定理.这些基本理论结果是把内P-集合与一类信息系统故障状态识别交叉,渗透研究得到的.     

巧用向量法求空间距离

向量法是将几何问题代数化,用代数的方法研究和解决几何问题.由于向量法是将空间元素的位置关系转化为数量关系,将形式逻辑证明转化为数值计算,因此用向量解题有时不仅不会增加解题难度,相反在一定程度上还能降低思维强度,增强可操作性.这对于丰富学生的思维结构,消除学生由于学习立体几何而产生的心理压力,培养学生从多角度、多方面思考和探索问题的能力,无疑将有着非常重要的意义.同时这也有利于落实新课改、新理念、新教材的教学实验.     

新题征展(93)

A 题组新编 　　1.(万尔遐)已知集合A = {x1,x2,…,xn},n∈N+且 n≥2. 　　(1)将集合A的第k个元素xk与数2k - 1相对应(1≤k≤n),则元素x5的对应数为__,元素__的对应数为128.……     

抽屉原理

抽屉原理俗称鸽巢原理,又叫狄利克雷原理.简单地说就是:把3个苹果放入两个抽屉中,必有一个抽屉中至少有两个苹果;把3个苹果放入4个抽屉中,必有一个抽屉中没有苹果.1抽屉原理的几种形式1)第一抽屉原理(少的抽屉原理)设有m个元素分属于n个集合(其两两的交集可以非空),且m>kn(m,n,k均为正整数),则必有一个集合中至少有k 1个元素.2)第二抽屉原理(多的抽屉原理)设有m个元素分属于n个两两不相交的集合,且m相似文献   

排列组合中的分组问题

排列组合一章的习题中,常常涉及到对元素进行分组的问题.题目有对相同元素分组和对不同元素分组,有组的位置确定和不确定多种情况,学生弄不清这些题目的区别和联系,解答时很容易重复或者遗漏.本文编拟口诀并举例介绍巧妙解决分组问题的方法.1相同元素的分组问题(口诀:同元分组用挡板)例1将12个相同的小球放入编号为1,2,3,4的盒子中,问每个盒子中至少有一个小球的放法有多少种?解本题是将12个球分成四组,每组必须有球的问题.将12个球排成一排,中间有11个间隔,在这11个间隔中任意选出3个插入挡板,把球分成4组,例如○○○|○○○○|○○|○○○…     

新题征展(79)

A题组新编1.已知函数y=f(x)存在反函数y=f-1(x).(1)若方程f(x)-m 1 x=0的所有实根和方程f-1(x-1)-m x=0的所有实根的集合为k元集合A,则集合A中所有元素的和为.(2)若方程f(x)-mx 1=0(m>0)的所有正根与f-1(x-1)-mx=0(m>0)的所有正根组成一个k元集合A,则集合A中所有元素的积为.(3)     

一种基于Bloom Filter的频繁模式挖掘算法

挖掘最大频繁项集是多种数据挖掘应用中的关键问题.针对频繁模式挖掘的可扩展性问题,基于Bloom Filter的相关理论,提出了一种"挖掘最频繁的K个元素"的Mining Top-K算法.该算法基于推广的Bloom Filter的数据结构,能够较为准确地筛选出数据流中出现最频繁的K个元素,并估算这K个元素的出现频数.实验结果表明:该方法在具有低空间复杂度特性的同时又不会失去准确性.     

Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering,GA and unscented filter

This paper presents a two stage procedure for building optimal fuzzy model from data for nonlinear dynamical systems. Both stages are embedded into Genetic Algorithm (GA) and in the first stage emphasis is placed on structural optimization by assigning a suitable fitness to each individual member of population in a canonical GA. These individuals represent coded information about the structure of the model (number of antecedents and rules). This information is consequently utilized by subtractive clustering to partition the input space and construct a compact fuzzy rule base. In the second stage, Unscented Filter (UF) is employed for optimization of model parameters, that is, parameters of the input–output Membership Functions (MFs).     

Mobile Robot Global Localization using an Evolutionary MAP Filter

A new algorithm based on evolutionary computation concepts is presented in this paper. This algorithm is a non linear evolutive filter known as the Evolutive Localization Filter (ELF) which is able to solve the global localization problem in a robust and efficient way. The proposed algorithm searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information up to date. The population evolves by using the log-likelihood of each candidate pose according to the observation and the motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The algorithm has been tested on a mobile robot equipped with a laser range finder to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.     

Sub-sample swapping for sequential Monte Carlo approximation of high-dimensional densities in the context of complex object tracking

In this paper, we address the problem of complex object tracking using the particle filter framework, which essentially amounts to estimate high-dimensional distributions by a sequential Monte Carlo algorithm. For this purpose, we first exploit Dynamic Bayesian Networks to determine conditionally independent subspaces of the object’s state space, which allows us to independently perform the particle filter’s propagations and corrections over small spaces. Second, we propose a swapping process to transform the weighted particle set provided by the update step of the particle filter into a “new particle set” better focusing on high peaks of the posterior distribution. This new methodology, called Swapping-Based Partitioned Sampling, is proved to be mathematically sound and is successfully tested and validated on synthetic video sequences for single or multiple articulated object tracking.     

Bloom: A stochastic growth-based fast method of community detection in networks

Networks are characterized by a variety of topological features and dynamics. Classifying nodes into communities, community structure, is important when exploring networks. This paper explores the community detection metric called modularity. The theoretical definitions of modularity are connected with intuitive insights into the compositions of communities. Local modularity costs/benefits are explored and an efficient stochastic algorithm, Bloom, is introduced, based on growing communities using local improvement measures. Three extensions of Bloom are presented that build upon the basic version. A numerical analysis compares Bloom with the popular fast-greedy algorithm and demonstrates the successful performance of the three modifications of Bloom.     

Dynamic mortality factor model with conditional heteroskedasticity

In most methods for modeling mortality rates, the idiosyncratic shocks are assumed to be homoskedastic. This study investigates the conditional heteroskedasticity of mortality in terms of statistical time series. We start from testing the conditional heteroskedasticity of the period effect in the naïve Lee-Carter model for some mortality data. Then we introduce the Generalized Dynamic Factor method and the multivariate BEKK GARCH model to describe mortality dynamics and the conditional heteroskedasticity of mortality. After specifying the number of static factors and dynamic factors by several variants of information criterion, we compare our model with other two models, namely, the Lee-Carter model and the state space model. Based on several error-based measures of performance, our results indicate that if the number of static factors and dynamic factors is properly determined, the method proposed dominates other methods. Finally, we use our method combined with Kalman filter to forecast the mortality rates of Iceland and period life expectancies of Denmark, Finland, Italy and Netherlands.     

Analysis of Multi-Unit Variance Components Models with State Space Profiles

We apply the Kalman Filter to the analysis of multi-unit variance components models where each unit's response profile follows a state space model. We use mixed model results to obtain estimates of unit-specific random effects, state disturbance terms and residual noise terms. We use the signal extraction approach to smooth individual profiles. We show how to utilize the Kalman Filter to efficiently compute the restricted loglikelihood of the model. For the important special case where each unit's response profile follows a continuous structural time series model with known transition matrix we derive an EM algorithm for the restricted maximum likelihood (REML) estimation of the variance components. We present details for the case where individual profiles are modeled as local polynomial trends or polynomial smoothing splines.     

Enhanced consistency of the Resampled Convolution Particle Filter

Among the convolution particle filters for discrete-time dynamic systems defined by nonlinear state space models, the Resampled Convolution Filter is one of the most efficient, in terms of estimation of the conditional probability density functions (pdf’s) of the state variables and unknown parameters and in terms of implementation. This nonparametric filter is known for its almost sure L₁-convergence property. But contrarily to the other convolution filters, its almost sure punctual convergence had not yet been established. This paper is devoted to the proof of this property.     

About Tracing Problems in Dynamic Geometry

Dynamic Geometry is the field of interactively performing geometric construction on a computer. In addition to simulating ruler-and-compass constructions we allow a drag mode. This drag mode allows to move geometric objects that have at least one degree of freedom. The remaining part of the construction should adjust automatically. Thus, during the motion, we have to trace the resulting paths of all geometric objects. This path tracking problem is known as the Tracing Problem from Dynamic Geometry. It combines the step-by-step procedure of doing geometric constructions with the continuous concept of motions. This study is based on the model for Dynamic Geometry used in the interactive geometry software Cinderella. We give a numerical solution to the Tracing Problem based on continuation methods and a reliable algorithm based on real and complex interval arithmetic. Degenerate situations like the intersection of two identical lines lead to critical points in the configuration space and are treated separately.     

A limit theorem for products of free unitary operators

This paper establishes necessary and sufficient conditions for the sequence of products of freely independent unitary operators to converge in distribution to the uniform law on the unit circle. The author would like to express his gratitude to Diana Bloom for her help with editing, and to Professor Raghu Varadhan for useful discussions.