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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写凸优化Convex Optimization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。我们的专家在代写凸优化Convex Optimization代写方面经验极为丰富，各种代写凸优化Convex Optimization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

## 数学代写|凸优化作业代写Convex Optimization代考|General Assumptions

The development of single-objective global optimization algorithms for some subclasses of non-convex functions is facilitated by the exploitation of analytical properties of objectives [87]. However, in some applications there occur optimization problems where objectives are available either as a complicated computational model or as a closed code software. Such types of problems usually are named black-box optimization problems. According to this concept, the assumption on the uncertainty in properties of $f(\mathbf{x})$ seems quite natural. Nevertheless, normally that assumption can be softened postulating some relevant properties of the problem in question. At least, the assumption that $f(\cdot)$ is a continuous function normally is acceptable as well as $\mathbf{A}$ is a compact set. Besides the continuity, other analytical properties of $f(\mathbf{x})$ can be difficult to substantiate. Such unfavorable, from the optimization point of view, properties of $f(\mathbf{x})$ as non-differentiability, non-convexity, and multimodality cannot be excluded. To justify the search strategy in the described situation it is important to choose a model of uncertainty corresponding to the relevant information on the problem. A “rational optimizer” can choose a statistical model of uncertainty as it is justified in the expected utility theory [58], although other models, such as fuzzy logic and rough sets, are also applicable.

The focus of statistical models-based global optimization is on the black-box problems where objective functions are expensive; expensiveness here means a long-lasting computation of a value of the objective function normally implied by the complexity of the underlying computational model of the considered problem. The black-box optimization of expensive functions in many respects is noticeably different from the optimization of objective functions defined by analytical formulae. The expensiveness of objective functions imply limitations in both exploration and exploitation during the search. Therefore, the rationality of the search strategy is strongly required, i.e., the choice of points where to compute the objective function values should be well substantiated. The algorithms, founded on the principles of rational decision theory, here are of special interest. To construct such algorithms in the single-objective optimization case, statistical models of multimodal functions have proved very helpful $[139,208,216,239]$.

## 数学代写|凸优化作业代写Convex Optimization代考|Statistical Models for Global Optimization

Black-box optimization is aimed at the optimization of objective functions the properties of which are rather uncertain. The models of functions under uncertainty developed in probability theory are stochastic functions: random processes in the case of functions of one variable, and random fields in the case of functions of many variables. Assuming that the objective function is a sample function of a random process/field it would be attractive to construct a method of the best average performance with respect to the chosen statistical model. Stochastic models of objective functions are also helpful for the theoretical research on average complexity of global optimization problems; see, e.g., [28, 29].

For the successful implementation of the algorithms, by the considered approach, the selection/construction of an appropriate statistical model is crucial. We discuss here the relevant properties of the statistical models in detail since they are equally important for the case of multi-objective optimization.

## 有限元方法代写

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## 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