对于“集合”,我们应该掌握些什么?

集合是数学的基础知识 ,学习它 ,可以使我们更好地理解数学中出现的集合语言 ,能更简捷地用集合的语言表述数学问题 ,用集合的观点去研究、处理数学问题 .那么 ,对于“集合”这部分内容 ,我们应该掌握些什么呢 ?第一 ,正确理解集合的概念、掌握元素与集合、集合与集合间的基本关系是学好集合乃至学好高中数学的第一步 .以下知识点是必须深刻理解并熟练掌握的 :1 )集合、空集、全集的概念 .集合是一个原始的数学概念 ,要用心体验 ,特别要注意集合的“三性” :①确定性 .指集合中的元素是确定的 .如“很小的数的全体”就不能视为集合 .②互异…     

集合问题应掌握的知识要点

集合是数学中的最基本概念 ,它已渗透到自然科学的各个领域 ,应用十分广泛 .为了帮助同学们正确理解集合的有关概念 ,弄懂集合的各个概念的涵义和相互之间的区别与联系 ,能够准确运用集合的术语、符号和运算解决有关问题 ,为今后的数学学习作好铺垫 ,特归纳知识要点如下 .1)集合是一个原始不能定义的概念 ,集合的元素具有确定性、互异性和无序性 .确定性是对某一集合来说 ,任一对象或者是该集合的元素 ,或者不是该集合的元素 ,二者必具其一 .如“个子较高的学生的全体”就不能构成集合 ,因为“个子较高”并不是一个明确的标准 ,无法作出判断…     

看图筛选 多退少补——例谈构造集合求解排列组合问题

从集合的角度看,从n个不同元素中取出m（m≤n）个元素的排列（组合）,可以组成一个集合,其中每一个排列（组合）是它的一个元素,其排列数（组合数）就是这个集合中的元素的个数．因此在许多排列组合问题中适当构造集合,将问题中的条件关系转化为可用集合图形表示出来的集合间的运算关系,运用看图筛选,多退少补的方法求出符合条件的集合中的元素个数,     

运用子集思想确定参数取值范围

确定参数取值范围问题是高考、竞赛中的热点问题 .关于这类问题的解法 ,有很多作者进行了研究 ,本文就一类与子集有关的参数范围问题作一些探讨 ,供同行们参考 .对于 A、B两个集合 ,如果 A中每一个元素都是 B中的元素 ,则称 A是 B的子集 ,记作A B,利用子集概念 ,可以简明地解决许多数学问题 .例 1　设集合 A ={x| x2 x - 6 <0 },函数 f ( x) =x2 ax - 2x2 - x 1 的值域为 B,求使B A的实数α的取值范围 .分析　这里的集合是一个“非必求量”.若先求 f ( x)的值域 B,再通过数轴 ,由 B A,列出关于α的不等式组 ,然后解不等式组 …     

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     

P-集合的随机特征

在P-集合概念的基础上,利用元素迁移的随机性,蛤出P-集合的随机生成和P-集合的强随机生成,讨论了P-集合的随机特性.     

漫谈“空集”

什么叫做空集?新编高中《数学》第一册第五页上写着“不含任何元素的集合,叫做空集”. 回顾集合的定义“一些确定的元素组成的整体叫做集合”[见本刊1981年第2期第4—6页]。如果我们用这个定义来理解上面所说的空集定义,这就说明在空集定义中已经承认了“整体”中可以“不含任何元素”,而且把“不含任何元素”所组成的“整体”也认为     

分式函数f(x)=t/(d-x)+c的性质

题目已知数集A满足条件:a≠1,若a∈A,则11-a∈A.(1)若2∈A,则集合A中还有哪些元素?(2)请你任意设集合A中的一个元素(实数),再探讨该集合中其他元素;(3)从上面两题的解答过程中,你领悟到什么结论?并加以证明.     

集合学习中的四点注意

一、注意集合中的元素是什么集合中的元素的表现形式是多种多样的,可以是实数x,有序实数对(x,y),三角形等等.弄清集合中的元素是什么,是掌握集合概念的基本要求,是进行集合运算的前提.     

集合的概念与运算

集合是数学中的重要概念之一,在中学数学竞赛中,许多本质上属于代数、几何、数论、组合的问题都可以用集合的观点和方法来解决,局部与整体的观点是其思想实质.一般地,某些指定的对象集中在一起就成为一个集合,集合中的每个对象叫做这个集合的元素.常用描述法表示集合,S={x|x具有性质P}表示所有具有性质P的对象组成的集合S.集合的运算中,除了交、并运算外,还有补运算和差运算.对于A、B两个集合,由所有属于A但不属于B的元素构成的集合称为A关于B的差集,记作A\B,即A\B={x|x∈A,且x B}关于集合的运算满足如下关系式:(1)交换律:A∩B=B∩A…     

The complexity of dissociation set problems in graphs

A subset of vertices in a graph is called a dissociation set if it induces a subgraph with a vertex degree of at most 1. The maximum dissociation set problem, i.e., the problem of finding a dissociation set of maximum size in a given graph is known to be NP-hard for bipartite graphs. We show that the maximum dissociation set problem is NP-hard for planar line graphs of planar bipartite graphs. In addition, we describe several polynomially solvable cases for the problem under consideration. One of them deals with the subclass of the so-called chair-free graphs. Furthermore, the related problem of finding a maximal (by inclusion) dissociation set of minimum size in a given graph is studied, and NP-hardness results for this problem, namely for weakly chordal and bipartite graphs, are derived. Finally, we provide inapproximability results for the dissociation set problems mentioned above.     

Continuous quadratic programming formulations of optimization problems on graphs

Four NP-hard optimization problems on graphs are studied: The vertex separator problem, the edge separator problem, the maximum clique problem, and the maximum independent set problem. We show that the vertex separator problem is equivalent to a continuous bilinear quadratic program. This continuous formulation is compared to known continuous quadratic programming formulations for the edge separator problem, the maximum clique problem, and the maximum independent set problem. All of these formulations, when expressed as maximization problems, are shown to follow from the convexity properties of the objective function along the edges of the feasible set. An algorithm is given which exploits the continuous formulation of the vertex separator problem to quickly compute approximate separators. Computational results are given.     

Range sets for weak efficiency in multiobjective linear programming and a parametric polytopes intersection problem

ABSTRACT

The aim of this paper is to obtain the range set for a given multiobjective linear programming problem and a weakly efficient solution. The range set is the set of all values of a parameter such that a given weakly efficient solution remains efficient when the objective coefficients vary in a given direction. The problem was originally formulated by Benson in 1985 and left to be solved. We formulate an algorithm for determining the range set, based on some hard optimization problems. Due to toughness of these optimization problems, we propose also lower and upper bound approximation techniques. In the second part, we focus on topological properties of the range set. In particular, we prove that a range set is formed by a finite union of intervals and we propose upper bounds on the number of intervals. Our approach to tackle the range set problem is via the intersection problem of parametric polytopes. Thus, our results have much wider area of applicability since the intersection (and separability) problem of convex polyhedra is important in many fields of optimization.     

An efficient algorithm for the complete set partitioning problem

The complete set partitioning problem is the well known set partitioning problem with all possible nonzero binary columns in the constraint matrix. A highly specialized enumerative algorithm, which never requires the explicit maintenance of the constraint matrix, is presented. Computational results, with data reflecting a particular corporate tax payment scenario that can be modelled as a complete set partitioning problem, is also given.     

On the collection depots location problem

In this paper we investigate the problem of locating a new facility servicing a set of demand points. A given set of collection depots is also given. When service is required by a demand point, the server travels from the facility to the demand point, then from the demand point to one of the collection depots (which provides the shortest route back to the facility), and back to the facility. The problem is analyzed and properties of the solution point are formulated and proved. Computational results on randomly generated problems are reported.     

Partial inverse maximum spanning tree in which weight can only be decreased under $$l_p$$-norm

The maximum or minimum spanning tree problem is a classical combinatorial optimization problem. In this paper, we consider the partial inverse maximum spanning tree problem in which the weight function can only be decreased. Given a graph, an acyclic edge set, and an edge weight function, the goal of this problem is to decrease weights as little as possible such that there exists with respect to function containing the given edge set. If the given edge set has at least two edges, we show that this problem is APX-Hard. If the given edge set contains only one edge, we present a polynomial time algorithm.     

Active Constraint Set Invariancy Sensitivity Analysis in Linear Optimization

Active constraint set invariancy sensitivity analysis is concerned with finding the range of parameter variation so that the perturbed problem has still an optimal solution with the same support set that the given optimal solution of the unperturbed problem has. However, in an optimization problem with inequality constraints, active constraint set invariancy sensitivity analysis aims to find the range of parameter variation, where the active constraints in a given optimal solution remains invariant.For the sake of simplicity, we consider the primal problem in standard form and consequently its dual may have an optimal solution with some active constraints. In this paper, the following question is answered: “what is the range of the parameter, where for each parameter value in this range, a dual optimal solution exists with exactly the same set of positive slack variables as for the current dual optimal solution?”. The differences of the results between the linear and convex quadratic optimization problems are highlighted too.     

求多目标优化问题Pareto最优解集的方法

主要讨论了无约束多目标优化问题Pareto最优解集的求解方法,其中问题的目标函数是C1连续函数.给出了Pareto最优解集的一个充要条件,定义了α强有效解,并结合区间分析的方法,建立了求解无约束多目标优化问题Pareto最优解集的区间算法,理论分析和数值结果均表明该算法是可靠和有效的.     

The Multi-Handler Knapsack Problem under Uncertainty

The Multi-Handler Knapsack Problem under Uncertainty is a new stochastic knapsack problem where, given a set of items, characterized by volume and random profit, and a set of potential handlers, we want to find a subset of items which maximizes the expected total profit. The item profit is given by the sum of a deterministic profit plus a stochastic profit due to the random handling costs of the handlers. On the contrary of other stochastic problems in the literature, the probability distribution of the stochastic profit is unknown. By using the asymptotic theory of extreme values, a deterministic approximation for the stochastic problem is derived. The accuracy of such a deterministic approximation is tested against the two-stage with fixed recourse formulation of the problem. Very promising results are obtained on a large set of instances in negligible computing time.