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数学代写|离散数学代写Discrete Mathematics代考|Search Algorithms

如果你也在 怎样代写离散数学Discrete Mathematics MA210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。离散数学Discrete Mathematics是数学的一个分支,研究一般代数环境中的同源性。它是一门相对年轻的学科,其起源可以追溯到19世纪末的组合拓扑学(代数拓扑学的前身)和抽象代数(模块和共轭理论)的研究,主要是由亨利-庞加莱和大卫-希尔伯特提出。

离散数学Discrete Mathematics是研究同源漏斗和它们所带来的复杂的代数结构;它的发展与范畴理论的出现紧密地联系在一起。一个核心概念是链复合体,可以通过其同调和同调来研究。它在代数拓扑学中发挥了巨大的作用。它的影响逐渐扩大,目前包括换元代数、代数几何、代数理论、表示理论、数学物理学、算子矩阵、复分析和偏微分方程理论。K理论是一门独立的学科,它借鉴了同调代数的方法,正如阿兰-康尼斯的非交换几何一样。

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数学代写|离散数学代写Discrete Mathematics代考|Search Algorithms

数学代写|离散数学代写Discrete Mathematics代考|Search Algorithms

Searching is the process of locating an element in a list. A search algorithm is an algorithm that involves a search problem. Searching a database employs a systematic procedure to find an entry with a key designated as the objective of the search. A search algorithm locates an element $x$ in a list of distinct elements or determines that it is not in the list. The solution to the search is either the location of the element $x$ in the list or 0 if $x$ is not on the list. We now briefly introduce two well-known search algorithms whose worst-case and average time complexities are presented in Table 12.2.

The linear search, also known as the sequential search, is the simplest search algorithm. It is an algorithm, based on the brute-force algorithmic paradigm, that scans the elements of a list in sequence in search of $x$, the element that needs to be located. A comparison is made between $x$ and the first element in the list, if they are the same, then the solution is 1. Otherwise, a comparison is made between $x$ and the second element in the list, if they are the same, then the solution is 2 . This process continues until a match is found and the solution is the location of the element sought. If no match is found, then the solution is 0 . Linear search is applied on unsorted or unordered lists consisting of a small number of elements. Because $n$ comparisons are required to find $x$, the linear search has a time complexity of $O(n)$, which means the time is linearly dependent on the number of elements in the list.

The list of data in a binary search must be in a sorted order for it to work, such as ascending order. This search algorithm, which is quite effective in large sorted array, is based on the divide-and-conquer algorithmic paradigm. A binary search works by comparing the element to be searched with the element in the middle of the array of elements. If we get a match, the position of the middle element is returned. If the target element is less than the middle element, the search continues in the upper half of the array (i.e., the target element is compared to the element in the middle of the upper subarray), and the process repeats itself. If the target element is greater than the middle element, the search continues in the lower half of the array (i.e., the target element is compared to the element in the middle of the lower subarray), and the process repeats itself. By doing this, the algorithm eliminates the half in which the target element cannot lie in each iteration. Assuming the number of elements is $n=2^k$ (i.e., $k=\log _2 n$ ), at most $2 k+2=2 \log _2 n+2$ comparisons are required to perform a binary search. Binary search is thus more efficient than linear search, as it has a time complexity of $O(\log n)$. The worst case occurs when $x$ is not in the list.

数学代写|离散数学代写Discrete Mathematics代考|Deductive Reasoning and Inductive Reasoning

Deductive reasoning, which is top-down logic, contrasts with inductive reasoning, which is bottom-up logic. While the conclusion of a deductive argument is certain, based on the facts provided, the truth of the conclusion of an inductive argument may be probable based upon the evidence given.

Deductive reasoning refers to the process of concluding that something must be true because it is a specific case of a general principle that is already known to be true. Deductive reasoning is the process of reasoning from premises to reach a logically certain conclusion; it is logically valid and is the fundamental method in which mathematical facts are shown to be true. Deductive reasoning provides a guarantee of the truth of the conclusion if the premises (assumptions) are true. In other words, in a deductive argument, the premises are intended to provide such a strong support for the conclusion that, if the premises are true, then it would be impossible for the conclusion to be false. For example, a general principle in plane geometry states that the sum of the angles in any triangle is 180 degrees, then one can conclude that the sum of the angles in an isosceles right triangle is also 180 degrees. Another example is that the colonial powers systematically colonized countries and oppressed their people, then one can conclude that the British Empire, as it was a major colonial power, also colonized countries and oppressed people in a systematic manner. In summary, deductive reasoning requires one to start with a few general ideas, called premises, and apply them to a specific situation. Recognized rules, laws, theories, and other widely accepted truths are used to prove that a conclusion is right.

Inductive reasoning is the process of reasoning that a general principle is true because the special cases are true. Inductive reasoning makes broad generalizations from specific observations. Basically, there is data, and then conclusions are drawn from the data. Inductive reasoning is a process of reasoning in which the premises are viewed as supplying some evidence for the truth of the conclusion. It is also described as a method where one’s experiences and observations, including what are learned from others, are synthesized to come up with a general truth. For example, if all the people one has ever met from a particular country have been racist, one might then conclude all the citizens of that country are racist. Inductive reasoning is not logically valid. Just because all the people one happens to have met from a country were racist is no guarantee at all that all the people from that country are racist. Therefore this form of reasoning has no part in a mathematical proof. Even if all of the premises are true in a statement, inductive reasoning allows for the conclusion to be false. For instance, my neighbor is a grandfather. My neighbor is bald. Therefore all grandfathers are bald. The conclusion does not follow logically from the statements. In summary, inductive reasoning uses a set of specific observations to reach an overarching conclusion. Therefore a few particular premises create a pattern that gives way to a broad idea that is possibly true.

Inductive reasoning is part of the discovery process whereby the observation of special cases leads one to suspect very strongly (though not know with absolute logical certainty) that some general principle is true. Deductive reasoning, on the other hand, is the method you would use to demonstrate with logical certainty that the special case is true. In other words, inductive reasoning is used to formulate hypotheses and theories, and deductive reasoning is employed when applying them to specific situations. The difference between the two kinds of reasoning lies in the relationship between the premises and the conclusion. If the truth of the premises definitely establishes the truth of the conclusion (due to definition, logical structure, or mathematical necessity), then it is deductive reasoning. If the truth of the premises does not definitely establish the truth of the conclusion but nonetheless provides a reason to believe the conclusion may be true, then the argument is inductive.

数学代写|离散数学代写Discrete Mathematics代考|Search Algorithms


数学代写|离散数学代写Discrete Mathematics代考|Search Algorithms

搜索是在列表中定位元素的过程。搜索算法是涉及搜索问题的算法。搜索数据库采用系 统程序来查找具有指定为搜索目标的关键字的条目。搜索算法定位一个元素 $x$ 在不同元 素的列表中或确定它不在列表中。搜索的解决方案是元素的位置 $x$ 在列表中或 0 如果 $x$ 不在列表中。我们现在简要介绍两种著名的搜索算法,它们的最坏情况和平均时间复杂 度如表 12.2 所示。
线性搜索,也称为顺序搜索,是最简单的搜索算法。它是一种基于蛮力算法范式的算 法,它按顺序扫描列表的元素以搜索 $x$ ,需要定位的元素。之间进行了比较 $x$ 和列表中 的第一个元素,如果它们相同,则解为 1 。否则,进行比较 $x$ 和列表中的第二个元素, 如果它们相同,则解为 2 。这个过程一直持续到找到匹配并且解决方案是所寻找元素的 位置。如果末找到匹配项,则解决方案为 0 。线性搜索适用于由少量元素组成的末排序 或无序列表。因为 $n$ 需要比较才能发现 $x$ ,线性搜索的时间复杂度为 $O(n)$ ,这意味着时 间与列表中的元素数量线性相关。
二进制搜索中的数据列表必须按排序顺序才能工作,例如升序。这种在大型排序数组中 非常有效的搜索算法基于分而治之算法范式。二分查找的工作原理是将要查找的元素与 元素数组中间的元素进行比较。如果我们匹配到,则返回中间元素的位置。如果目标元 素小于中间元素,则在数组的上半部分继续搜索 (即,将目标元素与上部子数组中间的 元素进行比较),并重复该过程。如果目标元素大于中间元素,则在数组的下半部分继 续搜索 (即,将目标元素与较低子数组中间的元素进行比较),这个过程会重复。通过 这样做,该算法在每次迭代中消除了目标元素不能位于其中的一半。假设元素的数量是 $n=2^k\left(\right.$ IE, $\left.k=\log _2 n\right)$ , 最多 $2 k+2=2 \log _2 n+2$ 需要进行比较才能执行二 分查找。因此,二分搜索比线性搜索更有效,因为它的时间复杂度为 $O(\log n)$. 最坏的 情况发生在 $x$ 不在列表中。

数学代写|离散数学代写Discrete Mathematics代考|Deductive Reasoning and Inductive Reasoning


演绎推理是指得出某事一定为真的结论的过程,因为它是已知为真的一般原则的特定案例。演绎推理是从前提进行推理以得出逻辑上确定的结论的过程;它在逻辑上是有效的,是证明数学事实为真的基本方法。如果前提(假设)为真,演绎推理可以保证结论的真实性。换句话说,在演绎论证中,前提旨在为结论提供强有力的支持,如果前提为真,则结论不可能为假。例如,平面几何中的一般原理指出任何三角形的内角和为 180 度,那么可以得出等腰直角三角形的内角和也是180度。又如殖民列强有计划地殖民国家、压迫人民,那么大英帝国作为殖民大国,也有计划地殖民国家、压迫人民。总而言之,演绎推理需要一个人从一些一般性的想法(称为前提)开始,并将它们应用于特定情况。公认的规则、定律、理论和其他被广泛接受的真理被用来证明一个结论是正确的。还系统地殖民国家和压迫人民。总而言之,演绎推理需要一个人从一些一般性的想法(称为前提)开始,并将它们应用于特定情况。公认的规则、定律、理论和其他被广泛接受的真理被用来证明一个结论是正确的。还系统地殖民国家和压迫人民。总而言之,演绎推理需要一个人从一些一般性的想法(称为前提)开始,并将它们应用于特定情况。公认的规则、定律、理论和其他被广泛接受的真理被用来证明一个结论是正确的。



数学代写|离散数学代写Discrete Mathematics代考

数学代写|离散数学代写Discrete Mathematics代考 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。


微观经济学是主流经济学的一个分支,研究个人和企业在做出有关稀缺资源分配的决策时的行为以及这些个人和企业之间的相互作用。my-assignmentexpert™ 为您的留学生涯保驾护航 在数学Mathematics作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的数学Mathematics代写服务。我们的专家在图论代写Graph Theory代写方面经验极为丰富,各种图论代写Graph Theory相关的作业也就用不着 说。




现代博弈论始于约翰-冯-诺伊曼(John von Neumann)提出的两人零和博弈中的混合策略均衡的观点及其证明。冯-诺依曼的原始证明使用了关于连续映射到紧凑凸集的布劳威尔定点定理,这成为博弈论和数学经济学的标准方法。在他的论文之后,1944年,他与奥斯卡-莫根斯特恩(Oskar Morgenstern)共同撰写了《游戏和经济行为理论》一书,该书考虑了几个参与者的合作游戏。这本书的第二版提供了预期效用的公理理论,使数理统计学家和经济学家能够处理不确定性下的决策。


微积分,最初被称为无穷小微积分或 “无穷小的微积分”,是对连续变化的数学研究,就像几何学是对形状的研究,而代数是对算术运算的概括研究一样。

它有两个主要分支,微分和积分;微分涉及瞬时变化率和曲线的斜率,而积分涉及数量的累积,以及曲线下或曲线之间的面积。这两个分支通过微积分的基本定理相互联系,它们利用了无限序列和无限级数收敛到一个明确定义的极限的基本概念 。





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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