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

Homepage

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