放缩法引领下数列不等式的证明

数列不等式的证明集知识、方法、能力于一体,能综合反映学生分析问题和解决问题的能力,能全面考查学生的数学意识,因而是高考的一个重要考点,也是一大难点.这类问题极具选拔功能,对学生来说具有很大的挑战性.下面针对2012年广东高考(理)19题的分析,介绍     

容器约束的吉普问题

研究无容器约束和容器约束的吉普问题.对无容器约束吉普问题,给出单向及往返的最优行驶策略.对容器约束吉普问题,给出单向及往返行驶策略并证明吉普有能力行至无穷远处.在容器约束下,得到吉普最优行驶策略是一个有挑战性的问题.     

解析2013年高考立体几何题的创新性

2013年高考中的一些构思精巧、新颖别致、极富思考性和挑战性的立体几何创新题频频出现,它们充当着“把关题”的重要角色,具有很好的区分和选拔功能,是考查学生数学能力和素养的极好素材,值得认真研究.下面精选几例创新题加以剖析,旨在探索题型规律,揭示解题方法.     

解析2013年高考立体几何题的创新性

<正>2013年高考中的一些构思精巧、新颖别致、极富思考性和挑战性的立体几何创新题频频出现,它们充当着"把关题"的重要角色,具有很好的区分和选拔功能,是考查学生数学能力和素养的极好素材,值得认真研究.下面精选几例创新题加以剖析,旨在探索题型规律,揭示解题方法.1.你能想到直观图吗?例1(2013年福建     

一道教材习题引发的深度学习

<正>1 深度学习深度学习是课程改革以来对课程理解和课堂实践的深化,它既是一种理念也是一种实践指导策略.深度学习是指在教师引领下,学生围绕着具有挑战性的学习主题,全身心积极参与,体验成功,获得发展的有意义的学习过程.数学学习过程是学生围绕学习内容展开的活动过程,初中数学深度学习的特点是学生能够全身心投入具有挑战性的富有思维含量的学习活动.笔者在一节习题课中设计了三个具有挑战性的学习活动：一是发现习题的多个不同的证明方法;     

挑战性问题：激发有效学习的动力

<正>1 问题提出挑战性学习内容是基于学生已有的知识经验，立足于学生的“最近发展区”,设计的具有一定难度的学习内容.挑战性学习内容能够帮助学生拓展视野，锻炼思维能力，促进对知识的理解，引导他们逐渐形成探寻真理的科学精神.那么，在教学中应该如何合理、科学地设计挑战性学习任务呢?倘若课程内容难度较低，就无法激发学生的学习兴趣，难以调动学生的思维和引发学生的深层次思考，不利于促进学生产生内在的学习动力.但是，如果课程内容难度过大，     

部分线性分位回归模型估计的MM算法

近年来,关于部分线性分位回归模型的估计方法的研究得到了较多的关注.但由于目标函数的非光滑性,估计程序的实现是比较具有挑战性的.文章将采用MM(Majorization Minimization)算法计算部分线性分位数回归模型的估计.其基本原理是首先找到目标函数的优化函数,然后借助优化函数的最小化过程,逐步迭代至目标函数的解.数值模拟和实证研究表明该算法具有较好的稳定性和较强的数值计算能力.     

多元函数最值的求解策略

在近几年的高考及各种测试试题中,多元函数的最值及其衍生问题频频出现,因为变量多、解析式复杂、方法技巧性强、题目灵活多变而具有较强的挑战性,成为最值问题中的一个难点,也是考查学生的数学素养和能力的一个热点.根据课程标准的要求,求多元函数的最值,总的策略是转化为一元函数或二元函数最值问题,转化的具体策略多种多样,本文对此进行了归纳和梳理.     

用放缩法证明数列不等式技巧“揭秘”

数列型不等式的证明题,常常需要用放缩的方法来解决,但放缩的技巧让人目不暇接,极具思考性和挑战性,能全面而综合地考查学生的潜能与后继学习能力,因此常常成为高考压轴题及各级各类竞赛试题命题的素材.学生感觉这就像魔术师在玩魔术,感觉到忽有忽无、变化不测、奇幻莫测,很精彩但不知道怎么玩,一直无法抓住其中的关键处.笔者就此进行一些探究,试图发现这些放缩变形的本质.     

非对称信息下基于梯度效应的4PL外包激励机制

借鉴报酬激励的梯度效应,运用委托代理理论,研究了信息不对称条件第四方物流外包时对第三方物流的激励问题.研究结果表明:第三方物流获得高业绩的可能性越小即任务挑战性越强,第四方物流应该给予其的激励越多;激励梯度使激励契约与任务属性、第三方物流能力与效益以及第四方物流付出的代价密切关联;激励梯度的存在不影响第四方物流努力水平.     

2010年研究生数学建模竞赛A题综述

第七届全国研究生数学建模竞赛A题是生物信息学中的一个急需解决的问题.虽然有关问题的研究已经经历了十多年,但由于问题的复杂性,人们的认识还很局限,基本的结论大多还以定性的为主,定量的探讨正方兴未艾.对参赛队员来讲解决该问题是一个极大的挑战.研究生们在讨论该问题时,大多直接进行分类.然而对于一个小样本的学习问题,显然这样做是行不通的.所以问题的关键是从数学和生物学角度减少用于分类的特征数目.同时,对于获取的基因标签,需要从临床上或生物学角度找到验证.该问题的求解过程引导研究生们从数学建模走向解决实际问题.     

Mathematical programming and the sensitivity of multi-criteria decisions

We report on the current state of a project whose aim is to implement a framework for sensitivity analysis in Multi-Criteria Decision Making (MCDM). The framework is largely based on mathematical programming. Due to the potentially large number and nature of the mathematical programmes, it is far from trivial to provide solutions to all of them in acceptable computing times. The challenge is even greater when we recognize that much decision analysis is performed in the context of decision conferences where any sensitivity analysis needs to be conducted in near real time (preferably) on a PC. We present a parallel processing approach to this challenge and point to some of the difficulties still to be resolved. Preliminary results obtained on a network of transputers are discussed.     

On residuals in generalized Johnson SB regressions

An important challenge in the class of generalized Johnson S_B regression models is to define residuals which are capable of identifying departures from the model assumptions, as well as to assess the overall goodness-of-fit of the model. On this regard, we propose a new residual for this class of models, and numerically evaluate its behaviour relative to the deviance residual initially proposed for this class of regression models. Monte Carlo simulation experiments and empirical applications using real and simulated data are provided. Overall, the results favour the residual we propose.     

Sure feature screening for high-dimensional dichotomous classification

The curse of high-dimensionality has emerged in the statistical fields more and more frequently. Many techniques have been developed to address this challenge for classification problems. We propose a novel feature screening procedure for dichotomous response data. This new method can be implemented as easily as t-test marginal screening approach, and the proposed procedure is free of any subexponential tail probability conditions and moment requirement and not restricted in a specific model structure. We prove that our method possesses the sure screening property and also illustrate the effect of screening by Monte Carlo simulation and apply it to a real data example.     

Facilitating an Elementary Engineering Design Process Module

STEM education in elementary school is guided by the understanding that engineering represents the application of science and math concepts to make life better for people. The Engineering Design Process (EDP) guides the application of creative solutions to problems. Helping teachers understand how to apply the EDP to create lessons develops a classroom where students are engaged in solving real world problems by applying the concepts they learn about science and mathematics. This article outlines a framework for developing such lessons and units, and discusses the underlying theory of systems thinking. A model lesson that uses this framework is discussed. Misconceptions regarding the EDP that children have displayed through this lesson and other design challenge lessons are highlighted. Through understanding these misconceptions, teachers can do a better job of helping students understand the system of ideas that helps engineers attack problems in the real world. Getting children ready for the 21st century requires a different outlook. Children need to tackle problems with a plan and not shrivel when at first, they fail. Seeing themselves as engineers will help more underrepresented students see engineering and other STEM fields as viable career options, which is our ultimate goal.     

Transient Modeling and Simulation of Gas Pipe Networks with Characteristic Diagram Models for Compressors

One challenge for the simulation and optimization of real gas pipe networks is the treatment of compressors. Their behavior is usually described by characteristic diagrams reflecting the connection of the volumetric flow and the enthalpy change or shaft torque. Such models are commonly used for an optimal control of compressors and compressor stations [4,7] using stationary models for the gas flow through the pipes. For transient simulations of gas networks, simplified compressor models have been studied in [1–3]. Here, we present a transient simulation of gas pipe networks with characteristic diagram models of compressors using a stable network formulation as (partial) differential-algebraic system. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Using Swap-Based Search Trees to obtain Solutions for Resource Constrained Project Scheduling Problems

The high level of complexity in modern construction projects causes the problem of scheduling the involved tasks under precedence and resource constraints to be a great challenge. Additionally applications on real construction sites require non linear and non continuous constraints and objective functions. In order to optimize large scale construction sites in practice, this paper proposes a new optimization approach based on creating search trees from swapping and delaying tasks. The convergence of the proposed algorithm is analyzed and its complexity is compared to other available algorithms. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Robust placement of sensors in dynamic water distribution systems

Designing a robust sensor network to detect accidental contaminants in water distribution systems is a challenge given the uncertain nature of the contamination events (what, how much, when, where and for how long) and the dynamic nature of water distribution systems (driven by the random consumption of consumers). We formulate a set of scenario-based minimax and minimax regret models in order to provide robust sensor-placement schemes that perform well under all realizable contamination scenarios, and thus protect water consumers. Single-and multi-objective versions of these models are then applied to a real water distribution system. A heuristic solution method is applied to solve the robust models. The concept of “sensitivity region” is used to visualize trade-offs between multiple objectives.     

Real‐time road traffic forecasting using regime‐switching space‐time models and adaptive LASSO

Smart transportation technologies require real‐time traffic prediction to be both fast and scalable to full urban networks. We discuss a method that is able to meet this challenge while accounting for nonlinear traffic dynamics and space‐time dependencies of traffic variables. Nonlinearity is taken into account by a union of non‐overlapping linear regimes characterized by a sequence of temporal thresholds. In each regime, for each measurement location, a penalized estimation scheme, namely the adaptive absolute shrinkage and selection operator (LASSO), is implemented to perform model selection and coefficient estimation simultaneously. Both the robust to outliers least absolute deviation estimates and conventional LASSO estimates are considered. The methodology is illustrated on 5‐minute average speed data from three highway networks. Copyright © 2012 John Wiley & Sons, Ltd.     

Hazard rate estimation for left truncated and right censored data

Left truncation and right censoring (LTRC) presents a unique challenge for nonparametric estimation of the hazard rate of a continuous lifetime because consistent estimation over the support of the lifetime is impossible. To understand the problem and make practical recommendations, the paper explores how the LTRC affects a minimal (called sharp) constant of a minimax MISE convergence over a fixed interval. The corresponding theory of sharp minimax estimation of the hazard rate is presented, and it shows how right censoring, left truncation and interval of estimation affect the MISE. Obtained results are also new for classical cases of censoring or truncation and some even for the case of direct observations of the lifetime of interest. The theory allows us to propose a relatively simple data-driven estimator for small samples as well as the methodology of choosing an interval of estimation. The estimation methodology is tested numerically and on real data.