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

#### Doug I. Jones

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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。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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
couryes™为您提供可以保分的包课服务

## 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.

# 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|如何成为一个成功的决策者?

“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

## 有限元方法代写

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## 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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