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# 数学代写|最优化作业代写optimization theory代考|CS586/IE519 Complexity of Real Computation Processes

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## 数学代写|最优化作业代写optimization theory代考|On the Computer Constructing Technology of T-Efficient Computing Processes

Scheme of constructing (choice) of $T$-effective computational algorithm depends on many factors (class problems, input data, dimension and characteristics of the problems, computational resources that are available to the user, constrains (2.1), (2.2), and (2.3)); therefore, in the class problem $F$, it is advisable to distinguish multitude (subclasses) of problems that have common features in the context of computing [14]:

• One-off problems with a small amount of computing and moderate constraints on process time
• Problems (or series of problems) that are needed to be solved in real time
• Problems with a very large amount of computations that are needed to be solved in a practically reasonable amount of time (that cannot be achieved on traditional computing machines)

The performance of the conditions (2.1), (2.2), and (2.3) depending upon the statement of the problem can be achieved by choosing one of the following combinations of computing resources: $X,\left(X, I_n\right),(X, Y),\left(X, Y, I_n\right)$. In the first two situations, the possibilities of the computer are fixed. In the first situation, the information $I_n$ is also fixed; conditions (2.1), (2.2), and (2.3) are satisfied by the choice of the algorithm and its parameters; in the second one, it is still possible to select the set $I_n$ for this type of information operator. In the third situation, the information is fixed, and the parameters of the computer can be chosen besides the algorithm. In the fourth situation, all computing resources are used.

## 数学代写|最优化作业代写optimization theory代考|Specificity of Using Characteristic Estimates

In constructing real computational processes of computations, $\varepsilon$-solution is often used by some estimates of global error, its component and process time. Herewith, they distinguish estimates in the following way: a priori and a posteriori, majorizing and asymptotic, and determinate and stochastic. The possibility and advisability of these estimates using and the methods of their construction depend on the type, structure, and accuracy of a priori data, the problem, and the CA from that why the estimate is computed, and it also depends on the computational resources [114, 238].

Majorizing a priori estimate guarantees the upper bound of the estimated derivatives, and they are performed through known derivatives. Their computation does not require some significant computational expenses, but the value of estimates are often overrated; therefore, the conclusions based on them as for the possibility of computing of the solution under the conditions (2.1) and (2.2) may be false.

Asymptotic estimates approximate the estimated derivative. The variability of the parameter can be achieved by the desirable estimate proximity to the estimated derivative, but the computation of such estimates is related to significant computational expenses, and these estimates are usually a posteriori.

In the algorithmic support of solving problems under the conditions (2.1) and (2.2), given the properties of the estimates, it must be expected the possibility of computing of the various types of estimates of characteristics $E\left(E_{\mathrm{H}}, E_\mu, E_\tau\right)$ [238]. By the relaxed constraints (2.1) and (2.2), less precise and less complex (computational) estimates may be sufficient. By the tighten constrains (2.1) and (2.2), asymptotic (a posteriori) estimates are used. For example, the condition (2.2) may apply strict requirements to the accuracy of estimates of computational process parameters that are computed on the basis of errors estimate of the solution.

## 数学代写|最优化作业代写优化理论代考|论t -高效计算过程的计算机构造技术

$T$ -有效计算算法的构造(选择)方案取决于许多因素(类问题、输入数据、问题的维度和特征、用户可用的计算资源、约束(2.1)、(2.2)和(2.3));因此，在类问题$F$中，建议在计算[14]的上下文中区分具有共同特征的众多(子类)问题:

• 一次性问题，计算量少，处理时间限制适中
• 需要实时解决的问题(或一系列问题)

## 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。