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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写算法设计Algorithm Design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。我们的专家在代写算法设计Algorithm Design代写方面经验极为丰富，各种代写算法设计Algorithm Design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

## 统计代写|算法设计代写Algorithm Design代考|Modeling the Problem

Modeling is the art of formulating your application in terms of precisely described, well-understood problems. Proper modeling is the key to applying algorithmic design techniques to real-world problems. Indeed, proper modeling can eliminate the need to design or even implement algorithms, by relating your application to what has been done before. Proper modeling is the key to effectively using the “Hitchhiker’s Guide” in Part II of this book.

Real-world applications involve real-world objects. You might be working on a system to route traffic in a network, to find the best way to schedule classrooms in a university, or to search for patterns in a corporate database. Most algorithms, however, are designed to work on rigorously defined abstract structures such as permutations, graphs, and sets. To exploit the algorithms literature, you must learn to describe your problem abstractly, in terms of procedures on such fundamental structures.

Odds are very good that others have probably stumbled upon any algorithmic problem you care about, perhaps in substantially different contexts. But to find out what is known about your particular “widget optimization problem,” you can’t hope to find it in a book under widget. You must first formulate widget optimization in terms of computing properties of common structures such as those described below:

• Permutations are arrangements, or orderings, of items. For example, ${1,4,3,2}$ and ${4,3,2,1}$ are two distinct permutations of the same set of four integers. We have already seen permutations in the robot optimization problem, and in sorting. Permutations are likely the object in question whenever your problem seeks an “arrangement,” “tour,” “ordering,” or “sequence.”

## 统计代写|算法设计代写Algorithm Design代考|Recursive Objects

Learning to think recursively is learning to look for big things that are made from smaller things of exactly the same type as the big thing. If you think of houses as sets of rooms, then adding or deleting a room still leaves a house behind.Recursive structures occur everywhere in the algorithmic world. Indeed, each of the abstract structures described above can be thought about recursively. You just have to see how you can break them down, as shown in Figure 1.10:

• Permutations – Delete the first element of a permutation of $n$ things ${1, \ldots, n}$ and you get a permutation of the remaining $n-1$ things. This may require renumbering to keep the object a permutation of consecutive integers. For example, removing the first element of ${4,1,5,2,3}$ and

renumbering gives ${1,4,2,3}$, a permutation of ${1,2,3,4}$. Permutations are recursive objects.

• Subsets – Every subset of ${1, \ldots, n}$ contains a subset of ${1, \ldots, n-1}$ obtained by deleting element $n$, if it is present. Subsets are recursive objects.
• Trees – Delete the root of a tree and what do you get? A collection of smaller trees. Delete any leaf of a tree and what do you get? A slightly smaller tree. Trees are recursive objects.
• Graphs – Delete any vertex from a graph, and you get a smaller graph. Now divide the vertices of a graph into two groups, left and right. Cut through all edges that span from left to right, and what do you get? Two smaller graphs, and a bunch of broken edges. Graphs are recursive objects.
• Points – ‘lake a cloud of points, and separate them into two groups by drawing a line. Now you have two smaller clouds of points. Point sets are recursive objects.
• Polygons – Inserting any internal chord between two non-adjacent vertices of a simple polygon cuts it into two smaller polygons. Polygons are recursive objects.
• Strings – Delete the first character from a string, and what do you get? A shorter string. Strings are recursive objects. ${ }^1$

# 算法设计代考

## 统计代写|算法设计代写Algorithm Design代考|Modeling the Problem

• 排列是项目的排列或排序。例如，1,4,3,2和4,3,2,1是同一组四个整数的两个不同排列。我们已经看到了机器人优化问题和排序中的排列。每当您的问题寻求“安排”、“游览”、“排序”或“顺序”时，排列很可能是有问题的对象。

## 统计代写|算法设计代写Algorithm Design代考|Recursive Objects

• 排列 – 删除排列的第一个元素n事物1,…,n你得到剩余的排列n−1事物。这可能需要重新编号以使对象保持连续整数的排列。例如，删除第一个元素4,1,5,2,3和

• 子集——每个子集1,…,n包含一个子集1,…,n−1通过删除元素获得n，如果存在的话。子集是递归对象。
• 树——删除树的根，你会得到什么？小树的集合。删除一棵树的任何叶子，你会得到什么？一棵稍小的树。树是递归对象。
• 图表——从图表中删除任何顶点，你会得到一个更小的图表。现在将图的顶点分成左右两组。切开从左到右的所有边缘，你会得到什么？两个较小的图和一堆断边。图是递归对象。
• 点 – ‘湖点云，并通过绘制一条线将它们分成两组。现在你有两个更小的点云。点集是递归对象。
• 多边形 – 在简单多边形的两个不相邻顶点之间插入任何内部弦将其切割成两个较小的多边形。多边形是递归对象。
• 字符串——从字符串中删除第一个字符，你会得到什么？较短的字符串。字符串是递归对象。

## 有限元方法代写

assignmentutor™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。

## 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。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师