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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|Psychic Modeling

The call came for me out of the blue as I sat in my office.
“Professor Skiena, I hope you can help me. I’m the President of Lotto Systems Group Inc., and we need an algorithm for a problem arising in our latest product.”

“Sure,” I replied. After all, the dean of my engineering school is always encouraging our faculty to interact more with industry.

“At Lotto Systems Group, we market a program designed to improve our customers’ psychic ability to predict winning lottery numbers. ${ }^2$ In a standard lottery, each ticket consists of six numbers selected from, say, 1 to 44 . However, after proper training, our clients can visualize (say) fifteen numbers out of the 44 and be certain that at least four of them will be on the winning ticket. Are you with me so far?”

“Probably not,” I replied. But then I recalled how my dean encourages us to interact with industry.

“Our problem is this. After the psychic has narrowed the choices down to fifteen numbers and is certain that at least four of them will be on the winning ticket, we must find the most efficient way to exploit this information. Suppose a cash prize is awarded whenever you pick at least three of the correct numbers on your ticket. We need an algorithm to construct the smallest set of tickets that we must buy in order to guarantee that we win at least one prize.”

“Assuming the psychic is correct?”
“Yes, assuming the psychic is correct. We necd a program that prints out a list of all the tickets that the psychic should buy in order to minimize their investment. Can you help us?”

Maybe they did have psychic ability, for they had come to the right place. Identifying the best subset of tickets to buy was very much a combinatorial algorithm problem. It was going to be some type of covering problem, where each ticket bought would “cover” some of the possible 4-element subsets of the psychic’s set. Finding the absolute smallest set of tickets to cover everything was a special instance of the NP-complete problem set cover (discussed in Section $21.1$ (page 678)), and presumably computationally intractable.

It was indeed a special instance of set cover, completely specified by only four numbers: the size $n$ of the candidate set $S$ (typically $n \approx 15$ ), the number of slots $k$ for numbers on each ticket (typically $k \approx 6$ ), the number of psychicallypromised correct numbers $j$ from $S$ (say $j=4$ ), and finally, the number of matching numbers $l$ necessary to win a prize (say $l=3$ ). Figure $1.11$ illustrates a covering of a smaller instance, where $n=5, k=3$, and $l=2$, and no psychic contribution (meaning $j=5$ ).

## 统计代写|算法设计代写Algorithm Design代考|Estimation

When you don’t know the right answer, the best thing to do is guess. Principled guessing is called estimation. The ability to make back-of-the-envelope estimates of diverse quantities such as the running time of a program is a valuable skill in algorithm design, as it is in any technical enterprise.

Estimation problems are best solved through some kind of logical reasoning process, typically a mix of principled calculations and analogies. Principled calculations give the answer as a function of quantities that either you already know, can look up on Google, or feel confident enough to guess. Analogies reference your past experiences, recalling those that seem similar to some aspect of the problem at hand.

I once asked my class to estimate the number of pennies in a hefty glass jar, and got answers ranging from 250 to 15,000 . Both answers will seem pretty silly if you make the right analogies:

• A penny roll holds 50 coins in a tube roughly the length and width of your biggest finger. So five such rolls can easily be held in your hand, with no need for a hefty jar.
• 15,000 pennies means $\$ 150$in value. I have never managed to accumulate that much value in coins even in a hefty pile of change – and here I am only using pennies! But the class average estimate proved very close to the right answer, which turned out to be 1879 . There are at least three principled ways I can think of to estimate the number of coins in the jar: • Volume – The jar was a cylinder with about 5 inches diameter, and the coins probably reached a level equal to about ten pennies tall stacked end to end. Figure a penny is ten times longer than it is thick. The bottom layer of the jar was a circle of radius about five pennies. So $$(10 \times 10) \times\left(\pi \times 2.5^2\right) \approx 1962.5$$ • Weight- Lugging the jar felt like carrying around a bowling ball. Multiplying the number of US pennies in a pound (181 when I looked it up) times a$10 \mathrm{lb}$. ball gave a frighteningly accurate estimate of 1810 . • Analogy – The coins had a height of about 8 inches in the jar, or twice that of a penny roll. Figure I could stack about two layers of ten rolls per layer in the jar, or a total estimate of 1,000 coins. A best practice in estimation is to try to solve the problem in different ways and see if the answers generally agree in magnitude. All of these are within a factor of two of each other, giving me confidence that my answer is about right. Try some of the estimation exercises at the end of this chapter, and see how many different ways you can approach them. If you do things right, the ratio between your high and low estimates should be somewhere within a factor of two to ten, depending upon the nature of the problem. A sound reasoning process matters a lot more here than the actual numbers you get. # 算法设计代考 ## 统计代写|算法设计代写Algorithm Design代考|Psychic Modeling 当我坐在办公室里时，突然来了一个电话。 “Skiena 教授，我希望你能帮助我。我是 Lotto Systems Group Inc. 的总裁，我们需要一种算法来解决我们最新产品中出现的问题。” “当然，”我回答。毕竟，我工程学院的院长总是鼓励我们的教师与行业进行更多互动。 “在 Lotto Systems Group，我们推出了一项旨在提高客户预测中奖彩票号码的心理能力的计划。2在标准彩票中，每张彩票由从 1 到 44 中选择的六个数字组成。然而，经过适当的培训，我们的客户可以想象（比如说）44 个数字中的 15 个数字，并确定其中至少有四个会出现在中奖彩票上。到目前为止，你和我在一起吗？” “可能不会，”我回答。但后来我想起了我的院长是如何鼓励我们与行业互动的。 “我们的问题是这样的。在通灵者将选择范围缩小到十五个数字并确定其中至少有四个将出现在中奖彩票上之后，我们必须找到利用这些信息的最有效方法。假设只要您在彩票上选择了至少三个正确号码，就会获得现金奖励。我们需要一种算法来构建我们必须购买的最小门票集，以确保我们至少赢得一个奖项。” “假设通灵是正确的？” “是的，假设通灵者是正确的。我们需要一个程序，打印出通灵者应该购买的所有门票的清单，以尽量减少他们的投资。你能帮助我们吗？” 也许他们确实有通灵能力，因为他们来对了地方。确定要购买的最佳门票子集在很大程度上是一个组合算法问题。这将是某种类型的覆盖问题，其中每张购买的票都将“覆盖”通灵者集合中一些可能的 4 元素子集。找到覆盖所有事物的绝对最小票证集是 NP 完全问题集覆盖的一个特殊实例（在第 1 节中讨论）21.1（第 678 页）），并且可能在计算上难以处理。 这确实是一个特例，完全由四个数字指定：大小n候选集的小号（通常n≈15), 槽数ķ每张票上的号码（通常ķ≈6)，心理承诺的正确数字的数量j从小号（说j=4)，最后是匹配数字的数量l赢得奖品所必需的（比如说l=3）。数字1.11说明了一个较小实例的覆盖，其中n=5,ķ=3， 和l=2，并且没有精神贡献（意思是j=5 ). ## 统计代写|算法设计代写Algorithm Design代考|Estimation 当你不知道正确答案时，最好的办法就是猜测。有原则的猜测称为估计。对不同数量（例如程序的运行时间）进行粗略估计的能力是算法设计中的一项宝贵技能，就像在任何技术企业中一样。 估计问题最好通过某种逻辑推理过程来解决，通常是原则性计算和类比的混合。有原则的计算将答案作为您已经知道的数量的函数，可以在 Google 上查找，或者有足够的信心猜测。类比参考你过去的经历，回忆那些看起来与手头问题的某些方面相似的经历。 我曾经让班上的同学估计一个大玻璃罐子里的硬币数量，得到的答案从 250 到 15,000 不等。如果你做出正确的类比，这两个答案都会显得很愚蠢： • 一便士卷在一个管子里装着 50 个硬币，大约是你最大手指的长度和宽度。所以五个这样的面包卷可以很容易地拿在手里，不需要一个大罐子。 • 15,000 便士意味着$150在价值上。即使在一大堆零钱中，我也从未设法在硬币中积累如此多的价值——而我在这里只使用便士！

• 体积 – 罐子是一个直径约 5 英寸的圆柱体，硬币的高度可能相当于首尾相连的十便士高。图一便士比它的厚度长十倍。罐子的底层是一个半径约五便士的圆。所以
(10×10)×(圆周率×2.52)≈1962.5
• 重量- 拖着罐子感觉就像带着一个保龄球。将一磅的美国便士的数量（我查时是 181）乘以 a10磅. 鲍尔给出了 1810 年的准确得吓人的估计。
• 类比——硬币在罐子里的高度约为 8 英寸，是一分钱硬币的两倍。图我可以在罐子里堆放大约两层，每层十卷，或者总共估计 1,000 个硬币。

## 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
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