Optimization Basics

本课程向学生介绍优化技术。本课程让学生了解有关数值优化技术实施的基本概念，假设学生对这些主题有或没有任何想法。教授这门优化课程的方法是基于学生对优化问题公式、各种优化算法的重要方面以及如何使用编程解决优化问题的知识有基本的了解。本课程的讲座涵盖优化问题的图形化方法、优化的符号和分类、无约束优化和约束优化。各种算法，如黄金分割法、梯度下降法、牛顿法、增广拉格朗日法、和顺序二次规划（SQP）。

Published 05/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 43 lectures (7h 10m) | Size: 9.1 GB

Course introduces students to optimization techniques

What you’ll learn
Defines and introduces students to optimization techniques
This course introduces students to optimization techniques
optimization problem formulations
important aspects of various optimization algorithms
to use programming to solve optimization problems

Requirements
No prerequisites required.

Description
This course introduces students to optimization techniques. The course exposes students to basic concepts about the implementation of numerical optimization techniques, assuming that the student does or does not have any kind of idea on these topics. The approach used for teaching this optimization course is based on students having a basic understanding of optimization problem formulations, the important aspects of various optimization algorithms, also about the knowledge of how to use programming to solve optimization problems. The lectures in this course cover Graphical Approaches for Optimization Problems, Notations and Classification of Optimization, Unconstrained Optimization, and Constrained Optimization. Various algorithms such as Golden Section, Gradient Descent, Newton’s Methods, Augmented Lagrangian, and Sequential Quadratic Programming (SQP). This course will be beneficial to students who are interested in learning about the basics of optimization methods. Operation researchers, engineers, and data science and machine learning students will find this course useful. This course is taught by professor Rahul Rai who joined the Department of Automotive Engineering in 2020 as Dean’s Distinguished Professor in the Clemson University International Centre for Automotive Research (CU-ICAR). Previously, he served on the Mechanical and Aerospace Engineering faculty at the University at Buffalo-SUNY (2012-2020) and has experience in industrial research center experiences at United Technology Research Centre (UTRC) and Palo Alto Research Centre called as (PARC).

Who this course is for
Operations researcher, data science and machine learning, optimization