机器学习和数据科学数学：Optimization与Mathematica应用
Mathematics for Machine Learning and Data Science: Optimization with Mathematica Applications
摘要：
近年来，由于计算机技术的重大进步，优化领域引起了人们的极大兴趣。由于最优化方法和算法的快速发展，机器学习、经济学、金融学、生物物理学、分子建模、计算系统生物学、运筹学和所有工程领域中的许多问题现在都得到了解决。本专著介绍了线性代数、凸集、凸函数、单变量优化、多变量优化的主要定理及其相应的算法。本文还简要地讨论了约束非线性最优化问题。我们发现Wolfram语言非常适合以人类可读的形式指定算法。为了最小化非线性目标函数，我们创建了27个Mathematica函数，遵循18个算法的原则。为了说明算法的有效性，设计了相应的代码实例.每个算法的代码将按原样运行，除了安装Mathematica之外，不需要来自先前算法或第三方的代码。
英文摘要：
The field of optimization has gotten a lot of interest in recent years owing to significant advances in computer technology. Numerous issues in machine learning, economics, finance, geophysics, molecular modeling, computational systems biology, operations research, and all areas of engineering are now being resolved owing to the rapid growth of optimization methods and algorithms. This monograph presents the main theorems in linear algebra, convex sets, convex functions, single variable optimization, multivariable optimization, and their corresponding algorithms. We also briefly touch upon the constrained nonlinear optimization. We have found the Wolfram language to be ideal for specifying algorithms in human readable form. To minimize nonlinear objective functions, we have created 27 Mathematica functions that follow the principles of 18 algorithms. The code examples were carefully designed to demonstrate the purpose of given algorithm. The code for each algorithm will run as is with no code from prior algorithms or third parties required beyond the installation of Mathematica.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
