管理科学代写|决策论代写Management Science Models for Decision Making代考|MN2032

Doug I. Jones

Doug I. Jones

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如果你也在 怎样代写决策论Management Science Models for Decision Making这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。

管理科学的特点是使用数学模型为管理者提供指导方针,以便在当前信息状态下做出有效的决策,或者在当前知识不足以达成适当决策的情况下寻求进一步的信息。

couryes-lab™ 为您的留学生涯保驾护航 在代写决策论Management Science Models for Decision Making方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写决策论Management Science Models for Decision Making代写方面经验极为丰富,各种代写决策论Management Science Models for Decision Making相关的作业也就用不着说。

我们提供的决策论Management Science Models for Decision Making及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
管理科学代写|决策论代写Management Science Models for Decision Making代考|MN2032

管理科学代写|决策论代写Management Science Models for Decision Making代考|Interior Point Methods for LP

In the early 1980s, Narendra Karmarkar pioneered a new method for LP, an interior point method (Karmarkar 1984). Claims were made that this method would be many times faster than the simplex method for solving large-scale sparse LPs, and these claims helped focus researchers attention on it. His work attracted worldwide attention not only from operations researchers, but also from scientists in other areas.
Let me relate a personal experience. When news of his work broke out in world press, I was returning from Asia. The person sitting next to me on the flight was a petroleum geologist. When he learned that I was on the OR faculty at Michigan, he asked me excitedly “I understand that an OR scientist from India at Bell Labs made a discovery that is going to revolutionize petroleum exploration. Do you know him?!”
In talks on his algorithm that he gave at that time, Karmarker repeatedly emphasized the following points:

  1. The boundary of a convex polyhedron with its faces of varying dimensions has a highly complex combinatorial structure. Any method that operates on the boundary or close to the boundary will get caught up in this combinatorial complexity, and there is a limit on improvements we can make to its efficiency.
  2. Methods that operate in the central portion of the feasible region in the direction of descent of the objective function have the ability to take longer steps towards the optimum before being stopped by the boundary, and hence have the potential of being more efficient than boundary methods for larger problems.
  3. From an interior point, one can move in any direction locally without violating feasibility; hence powerful methods of unconstrained optimization can be brought to bear on the problem.

Researchers saw the validity of these arguments, hence Karmarkar’s talks stimulated a lot of work on these methods that stay “away” from the boundary. In the tidal wave of research that ensued, many different classes of interior point methods have been developed for LP, and extended to wider classes of problems including convex quadratic programming, monotone linear complementarity problem, and semi definite programming problems. We will discuss some popular interior point methods in a later chapter. Among them, the first is in fact the first interior point method discussed in the literature, the primal affine scaling method (Dikin 1967), which predates Karmarkar’s work, but did not attract much attention until after Karmarkar popularized the study of interior point methods (IPMs). We will also discuss another IPM known as the primal-dual IPM, which is the most popular IPM for solving I.Ps.

管理科学代写|决策论代写Management Science Models for Decision Making代考|How to Be a Successful Decision Maker?

The aim of this book is to discuss some techniques for reaching optimum decisions in problems that can be modeled using deterministic linear and quadratic models. Successful decision making is a very complex task with many dimensions to it. Reaching an optimum decision is one aspect of it. Another important aspect not in the scope of this book is implementing the decision reached, which often requires a lot of tact. I illustrate with a story:

“A 20-year-old lady started dating a 25-year-old man. He kept on giving her expensive gifts until one day she agreed to marry him.

Two days after the wedding she realized that he had been giving her these expensive gifts mainly to trap her into marriage, but in reality he was a miser. She felt very depressed at the prospect of a possible divorce so soon after her marriage.

A month passed by during which time she got a chance to observe him closely. He was hard working, made good money, and was very nice in every respect, except that he tried to save all this money. She thought “If I can learn how to manage him, I can still have a wonderful life. Let me give it a try.”
Forty years rolled by. Then her husband became sick, and on his death bed, she was serving him obediently. He said “I am going to die soon. You know very well that I love my money dearly. I want you to withdraw all my money and put it in my casket with me. I want to take it with me to my afterlife. I hope you will take the decision to sincerely fulfill this last request of mine.”

With his hands in hers, she told him, “You have my solemn promise that your wish will be implemented.”

Moments later he was dead. The undertaker came and the man’s body was stretched out in the casket. His wife, dressed in black, and her best friend were sitting by its side. The ceremony was over, and the undertaker got ready to close the casket. Then the wife said “Wait just a moment.” She went in and came out with a metal box and put it inside the casket. Then the underlaker closed it and rolled it away.
Then her friend said “I hope you were not foolish enough to put all your family’s money in your husband’s casket.”
The loyal wife replied, “Listen, I loved my husband, and we had a long and happy married life. I made a promise that his final request would be implemented. My husband worked very hard to earn his money, and I know how much pleasure it gave him to know he would have it with him. I could not go back on my word.”
The friend said, “You mean to tell me that you kept your promise?!!”
The tactful wife said, “I sure did. I got all the money together, put it in my account, and wrote him a check, and I put that cheque in the casket!”
Experience is the best teacher of “being tactful.” So, I encourage all the readers to get involved in using the techniques discussed in this book in practice.

管理科学代写|决策论代写Management Science Models for Decision Making代考|MN2032

决策论代写

管理科学代写|决策论代写管理科学决策模型代考| LP的内部点方法


在20世纪80年代初,Narendra Karmarkar开创了LP的一种新方法——内点法(Karmarkar 1984)。有人声称,在求解大规模稀疏LPs时,这种方法比单纯形方法快很多倍,这些声明帮助研究人员关注它。他的工作不仅引起了运筹学研究人员的关注,也引起了其他领域科学家的关注。让我讲一个个人的经历。当他的工作被世界媒体报道时,我正从亚洲回来。飞机上坐在我旁边的是一位石油地质学家。当他知道我在密歇根大学的手术室工作时,他兴奋地问我:“我听说贝尔实验室的一位来自印度的手术室科学家做出了一项将彻底改变石油勘探的发现。你认识他吗?!在他当时发表的关于他的算法的演讲中,Karmarker反复强调了以下几点


具有不同尺寸面的凸多面体的边界具有高度复杂的组合结构。任何在边界上或边界附近操作的方法都会陷入这种组合复杂性中,我们对其效率的改进是有限的。在可行区域的中心部分,在目标函数下降的方向上运行的方法,在被边界停止之前,有能力采取更长的步骤走向最优,因此,对于更大的问题,有可能比边界方法更有效。从内部点出发,可以向局部的任何方向移动而不违背可行性;因此,可以采用强大的无约束优化方法来解决这个问题。


研究人员看到了这些论点的有效性,因此Karmarkar的演讲激发了许多关于这些“远离”边界的方法的工作。在随后的研究浪潮中,针对LP开发了许多不同类别的内点方法,并扩展到更广泛的一类问题,包括凸二次规划、单调线性互补问题和半定规划问题。我们将在后面的章节中讨论一些流行的内点方法。其中,第一种方法实际上是文献中讨论的第一个内点方法,即原始仿射尺度法(Dikin 1967),该方法早于Karmarkar的工作,但直到Karmarkar普及内点方法(IPMs)的研究后才引起人们的重视。我们还将讨论另一种称为原-对偶IPM的IPM,它是求解ip的最流行的IPM。

管理科学代写|决策论代写管理科学决策模型代考|如何成为一个成功的决策者?


本书的目的是讨论在可以使用确定性线性模型和二次模型建模的问题中达到最优决策的一些技术。成功的决策是一个非常复杂的任务,有很多方面。做出最优决定是其中的一个方面。另一个不在本书范围内的重要方面是执行所达成的决策,这通常需要很多技巧。我用一个故事来说明:


“一个20岁的女人开始和一个25岁的男人约会。他不断送她昂贵的礼物,直到有一天她同意嫁给他


结婚两天后她才明白,他送她这些贵重礼物主要是为了诱骗她结婚,但实际上他是个守财奴。想到结婚这么快就要离婚,她感到非常沮丧


一个月过去了,在这期间她有机会仔细观察他。他工作努力,赚很多钱,在各方面都很好,除了他想把所有的钱都存起来。她想:“如果我能学会如何管理他,我仍然可以有一个美好的生活。让我试试。四十年过去了。后来她的丈夫病了,躺在病床上,她乖乖地伺候着他。他说:“我马上就要死了。你很清楚,我非常爱我的钱。我要你把我所有的钱都取出来放在我的棺材里。我想把它带到来世。我希望您能做出决定,真诚地满足我最后的请求。


她握着他的手,对他说:“我郑重地答应你,你的愿望一定会实现。”


片刻之后,他就死了。殡仪馆的人来了,那人的尸体躺在棺材里。他的妻子,穿着黑衣,和她最好的朋友坐在它的旁边。仪式结束了,殡仪馆的人准备合上棺材。然后妻子说:“等一下。”她走进去,拿着一个金属盒子出来,把它放进棺材里。然后下拉员把它合上,把它卷走了。然后她的朋友说:“我希望你没有愚蠢到把你家里所有的钱都放进你丈夫的棺材里。”忠诚的妻子回答说:“听着,我爱我的丈夫,我们的婚姻生活长久而幸福。我保证他最后的请求一定会得到执行。我丈夫非常努力地工作挣钱,我知道,他知道他将拥有这些钱,这给了他多大的快乐。我不能食言。朋友说:“你是说你遵守了你的诺言?!!”圆滑的妻子说:“当然。”我把所有的钱都凑在一起,存入我的账户,给他写了一张支票,然后把那张支票放进了棺材!经验是“要圆滑”的最好老师。因此,我鼓励所有读者在实践中使用本书中讨论的技术

管理科学代写|决策论代写Management Science Models for Decision Making代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

tatistics-lab作为专业的留学生服务机构,多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务,包括但不限于Essay代写,Assignment代写,Dissertation代写,Report代写,小组作业代写,Proposal代写,Paper代写,Presentation代写,计算机作业代写,论文修改和润色,网课代做,exam代考等等。写作范围涵盖高中,本科,研究生等海外留学全阶段,辐射金融,经济学,会计学,审计学,管理学等全球99%专业科目。写作团队既有专业英语母语作者,也有海外名校硕博留学生,每位写作老师都拥有过硬的语言能力,专业的学科背景和学术写作经验。我们承诺100%原创,100%专业,100%准时,100%满意。

随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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