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所在平台: Udemy |
课程主页: https://www.udemy.com/course/optimization-for-engineering-students/
课程评论：没有评论
Coursera 课程《面向 STEM 学生的最优化导论》是一门为科学和工程专业的学生设计的入门课程，通常作为本科数值方法课程的一部分。 本课程涵盖了多种优化方法，包括： * **一维无约束问题**：牛顿法、割线法、黄金分割法。 * **多维无约束问题**：单变量搜索法、最速下降法、牛顿法。 * **多维约束问题**：拉格朗日乘数法（包括等式约束和不等式约束）。 课程还提供了实际算例，并附带 Fortran95 和 Python 编写的计算机代码，方便学生下载和修改使用。 本课程适合大二或大三的 STEM 专业学生，要求具备微积分基础以及至少一种编程语言（如 Fortran90, C, C++, Python, MatLab 等）的编程能力。
This course provides a basic introduction to optimization methods for science and engineering students which is often taught as part of an undergraduate-level numerical methods class. The material covered here is at that level, and includes:· Newton and Secant methods for one dimensional unconstrained problems.· Golden search bracketed method for one-dimensional unconstrained problems.· Univariate search for multi-dimensional unconstrained problems.· Steepest Ascent Method for multi-dimensional unconstrained problems.· Newton's Method for a multi-dimensional unconstrained problem.· Lagrange multiplier method for multi-dimensional equality constraint problems.· Lagrange multiplier method for multi-dimensional inequality constraint problems.· Example problems using the above methods.Course notes are available for download. Computer codes used to solve these problems, written in both Fortran95 and Python, are also available for download and may be easily modified for your own use.The material presented is suitable for students in a sophomore or junior level science, technology, engineering and/or mathematics numerical methods class. A background in calculus is necessary, as is the ability to program in a computer language such as Fortran90, C, C++, Python, MatLab, etc.x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
