标签: Math475

数学代写|组合学代写Combinatorics代考|Math475

如果你也在 怎样代写组合学Combinatorics 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。组合学Combinatorics和离散数学,在这一代人中蓬勃发展。在理论方面,各种各样的工具、概念和见解已经发展起来,使我们能够解决以前难以解决的问题,制定新的问题,并将以前不相关的主题联系起来。

组合学Combinatorics在应用方面,从物理学家到生物学家的科学家都发现组合学在他们的研究中至关重要。在所有这一切中,计算机科学和数学之间的相互作用作为理论发展和组合学应用的主要推动力而脱颖而出。本文介绍了这种相互作用的数学基础及其一些结果。

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数学代写|组合学代写Combinatorics代考|Math475

数学代写|组合学代写Combinatorics代考|Bipartite Multigraphs

Let $G=(V, E)$ be a multigraph. Then $G$ is called bipartite, provided that the vertex set $V$ may be partitioned into two subsets $X$ and $Y$ so that each edge of $G$ has one vertex in $X$ and one vertex in $Y$. A pair $X, Y$ with this property is called a bipartition of $G$ (and of its vertex set $V$ ). Two vertices in the same part of the bipartition are not adjacent. As we did in Chapter 9 for bipartite graphs, we usually picture a bipartite multigraph so that the vertices in $X$ are on the left (thus called left vertices) and the vertices in $Y$ are on the right (thus called right vertices). ${ }^{33}$ Note that a bipartite multigraph does not have any loops. A multigraph that is isomorphic to a bipartite multigraph is also bipartite.

Example. A bipartite multigraph with bipartition $X, Y$, where $X=$ ${a, b, c, d}$ and $Y={u, v, w}$, is shown in Figure 11.20.

Example. Consider the graph $G$ shown in Figure 11.21. Although it is not apparent from the drawing, $G$ is a bipartite graph. This is because we may also draw $G$ as in Figure 11.22, which reveals that $G$ has a bipartition $X={a, c, g, h, j, k}, Y={b, d, e, f, i}$.

The previous example demonstrates that a drawing of a bipartite graph or a listing of its edges may not directly reveal the bipartite property. A description of the edges of a graph may reveal a bipartition of its vertices.

数学代写|组合学代写Combinatorics代考|The first sequence of play

Player $N$ goes first and puts a – on some edge $\beta$. We consider two cases:

Case 1: $\beta$ is an edge of one of the trees $T_1^{(0)}$ and $T_2^{(0)}$, say, the tree $T_1^{(0)}$.

Since $T_1^{(0)}$ and $T_2^{(0)}$ are spanning trees of $G_U$, it follows from Theorem 11.5.9 that there is an edge $\alpha$ of $T_2^{(0)}$ such that the graph obtained from $T_1^{(0)}$ by inserting $\alpha$ and deleting $\beta$ is a spanning tree $T_1^{(1)}$ of $G_U$. Our instructions to $P$ are to put $\mathrm{a}+$ on the edge $\alpha$. We let $T_2^{(1)}=T_2^{(0)}$. The trees $T_1^{(1)}$ and $T_2^{(1)}$ have exactly one edge in common, namely, the edge $\alpha$ with a + on it.

Case 2: $\beta$ is neither an edge of $T_1^{(0)}$ nor an edge of $T_2^{(0)}$.
Our instructions to $P$ are now to place a + on any edge $\alpha$ of $T_1^{(0)}$ or of $T_2^{(0)}$, say, an edge $\alpha$ of $T_1^{(0)}{ }^{47}$ Since $T_2^{(0)}$ is a spanning tree of $G_U$ and $\alpha$ is an edge of $G_U$, it follows from Theorem 11.5.9 that there is an edge $\gamma$ of $T_2^{(0)}$ such that the graph obtained from $T_2^{(0)}$ by inserting $\alpha$ and deleting $\gamma$ is a spanning tree $T_2^{(1)}$ of $G_U$. We let $T_1^{(1)}=T_1^{(0)}$. The trees $T_1^{(1)}$ and $T_2^{(1)}$ have only the edge $\alpha$ with a + in common.
We conclude that, at the end of the first sequence of play, there are two spanning trees, $T_1^{(1)}$ and $T_2^{(1)}$, of $G_U$ that have exactly one edge in common, namely, the edge with a + on it that was played by $P$.

数学代写|组合学代写Combinatorics代考|Math475

组合学代考

数学代写|组合学代写Combinatorics代考|Bipartite Multigraphs

假设$G=(V, E)$是一个多图。如果顶点集$V$可以划分为两个子集$X$和$Y$,使得$G$的每条边在$X$中有一个顶点,在$Y$中有一个顶点,那么$G$被称为二部的。具有此属性的对$X, Y$称为$G$(及其顶点集$V$)的双分割。在二分划的同一部分中的两个顶点不相邻。正如我们在第9章中对二部图所做的那样,我们通常描绘一个二部多图,这样$X$中的顶点在左边(因此称为左顶点),$Y$中的顶点在右边(因此称为右顶点)。${ }^{33}$注意,二部多图没有任何循环。与二部多图同构的多图也是二部的。

示例:具有两分区$X, Y$的二部多图,其中$X=$${a, b, c, d}$和$Y={u, v, w}$如图11.20所示。

示例:考虑图11.21中所示的$G$图。虽然从图中看不出来,但$G$是一个二部图。这是因为我们也可以绘制$G$,如图11.22所示,其中显示$G$有一个双分区$X={a, c, g, h, j, k}, Y={b, d, e, f, i}$。

前面的例子表明,绘制二部图或其边的列表可能不会直接揭示二部性质。图的边的描述可以揭示其顶点的二分。

数学代写|组合学代写Combinatorics代考|The first sequence of play

玩家$N$先去,把一个-放在一些边$\beta$。我们考虑两种情况:

情形1:$\beta$是一棵树$T_1^{(0)}$和一棵树$T_2^{(0)}$的一条边,比如一棵树$T_1^{(0)}$。

由于$T_1^{(0)}$和$T_2^{(0)}$是$G_U$的生成树,由定理11.5.9可知$T_2^{(0)}$存在一条边$\alpha$,因此插入$\alpha$,删除$\beta$,从$T_1^{(0)}$得到的图就是$G_U$的生成树$T_1^{(1)}$。我们对$P$的指示是将$\mathrm{a}+$放在边缘$\alpha$上。我们让$T_2^{(1)}=T_2^{(0)}$。树$T_1^{(1)}$和$T_2^{(1)}$只有一条共同的边,即带+的边$\alpha$。

情形2:$\beta$既不是$T_1^{(0)}$的边,也不是$T_2^{(0)}$的边。
我们对$P$的指令现在是在$T_1^{(0)}$或$T_2^{(0)}$的任意边$\alpha$上放一个+,比如$T_1^{(0)}{ }^{47}$的边$\alpha$因为$T_2^{(0)}$是$G_U$的生成树而$\alpha$是$G_U$的一条边,由定理11.5.9可知,$T_2^{(0)}$存在一条边$\gamma$,使得通过插入$\alpha$和删除$\gamma$从$T_2^{(0)}$得到的图是$G_U$的生成树$T_2^{(1)}$。我们让$T_1^{(1)}=T_1^{(0)}$。树$T_1^{(1)}$和$T_2^{(1)}$只有一条边$\alpha$的a +是相同的。
我们得出结论,在第一个游戏序列结束时,$G_U$有两棵生成树$T_1^{(1)}$和$T_2^{(1)}$,它们恰好有一条共同的边,即$P$玩过的带a +的边。

数学代写|组合学代写Combinatorics代考 请认准statistics-lab™

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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。

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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。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

数学代写|MATH334 Graph Theory

Statistics-lab™可以为您提供williams.edu MATH334 Graph Theory图论课程的代写代考辅导服务!

MATH334 Graph Theory课程简介

The content of this course includes general enumeration methods, which involves counting techniques used to solve combinatorial problems. The course also covers difference equations, which are used to model and solve recurrence relations. Another topic covered is generating functions, which are used to convert combinatorial problems into algebraic problems that can be solved using calculus and algebra.

In addition, the course includes elements of graph theory, which is the study of graphs and their properties. This includes matrix representations of graphs, which are used to analyze the structure of graphs. The course also covers applications of graph theory to transport networks, where graphs are used to model and analyze transportation systems.

Matching theory and graphical algorithms are also covered in this course. Matching theory is the study of matching problems, where we try to match elements from one set to another according to certain criteria. Graphical algorithms are algorithms that operate on graphs, and are used to solve problems such as shortest path and maximum flow problems.

PREREQUISITES 

Sample Textbooks
First Course in Graph Theory, by Gary Chartrand
Introduction to Enumerative Combinatorics, by Miklos Bona
Applications
Computer science, physics, economics, biology, chemistry
If you like this course, you might also consider the following courses
MATH 401, MATH 405, MATH416, Study abroad program Budapest Semesters of Mathematics
Additional Notes
Students interested in grad school in STAT or computer science should consider this course. A large element of the course involves puzzles that are very easy to understand, but requiring thinking outside the box.

MATH334 Graph Theory HELP(EXAM HELP, ONLINE TUTOR)

问题 1.

Theorem 2.26 If $G$ is disconnected then $\bar{G}$ is connected and $\operatorname{diam}(\bar{G}) \leq 2$.

Proof: Assume $G$ is disconnected. To prove $\bar{G}$ is connected, we must show there is an $x-y$ path for any pair of vertices $x, y$. If $x$ and $y$ are not adjacent in $G$ then $x y \in E(\bar{G})$. Thus the edge $x y$ is itself a $x-y$ path. Otherwise, $x y \in E(G)$ and so $x$ and $y$ are in the same component of $G$ and not adjacent in $\bar{G}$. Since $G$ is not connected, there must exist some vertex $z$ in a different component from $x$ and $y$, and so $z$ cannot be adjacent to either of $x$ or $y$. This implies $x z, y z \in E(\bar{G})$, and so $x z y$ is a $x-y$ path in $\bar{G}$. Note that every pair of vertices in $\bar{G}$ fall into one of these two cases and so satisfy $d(x, y) \leq 2$. Thus $\bar{G}$ is connected and $\operatorname{diam}(\bar{G}) \leq 2$

问题 2.

Theorem 2.27 For a simple graph $G$ if $\operatorname{rad}(G) \geq 3$ then $\operatorname{rad}(\bar{G}) \leq 2$.

Proof: Since $G$ is simple and $\operatorname{rad}(G)=r \geq 3$, we know that $\epsilon(v) \geq r$ for all vertices in $G$. In particular, there exists some path wxyz such that $w$ is not adjacent to $y$ and $z$, and $x$ is not adjacent to $z$. Since $r \geq 3$ we know $\epsilon(x) \geq 3$ and so there must exist another vertex $v$ (not one of $w, y, z$ ) such that $d(x, v) \geq 3$. Thus $v$ is not adjacent to either of $x$ or $y$. Moreover, $v$ cannot be adjacent to both $w$ and $z$ since otherwise $d(w, z)<3$. Thus at least one of the edges $v w$ or $v z$, but possibly both, cannot exist in $G$.

In either case, we have that the distance in $\bar{G}$ between any two of these vertices is at most 2 , and since this holds for any collection of vertices in $G$, we see that $\operatorname{rad}(\bar{G}) \leq 2$

Since the radius measures the shortest distance between two vertices and the diameter the longest, we should expect that these quantities cannot be too far away from one another. In geometry when we study circles, we define the diameter to be twice the radius. But is the same true in graph theory? As we have already seen above, the diameter need not be equal to twice the radius, but as the result below proves it cannot be larger than twice the radius.

Textbooks


• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.

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数学代写|MATH334 Graph Theory

Statistics-lab™可以为您提供williams.edu MATH334 Graph Theory图论课程的代写代考辅导服务! 请认准Statistics-lab™. Statistics-lab™为您的留学生涯保驾护航。