Introduction to Optimization

Optimization technology provides an extremely powerful tool for data-based decision making under complex constraints. This course will teach engineers how to use widely available software tools (such as Excel) to make sophisticated decisions in various settings. The topics in the course will be covered using real-world examples and reinforced through homework problems. The emphasis will be on ‘learning by doing’. The focus will be on teaching students the skills to solve real-world problems, and not on the theory of optimization.

IE59000

Credit Hours:

3

Objective

The objective of this course is to provide hands-on training in identifying and solving various types of real-world optimization problems.  Students will be trained to identify, formulate, and subsequently use available software–specifically Excel–to solve common types of optimization problems. Optimization is an extremely powerful and economically important tool for decision-making in the modern world, and this course will train students to use the tool in various contexts.

Course description

Optimization technology provides an extremely powerful tool for data-based decision making under complex constraints.   This course will teach engineers how to use widely available software tools (such as Excel) to make sophisticated decisions in various settings.  The topics in the course will be covered using real-world examples and reinforced through homework problems. The emphasis will be on ‘learning by doing’.  The focus will be on teaching students the skills to solve real-world problems, and not on the theory of optimization.

Syllabus

  1. Introduction to optimization
  2. Linear optimization
  3. Integer linear optimization
  4. Nonlinear optimization
  5. Multi-objective optimization
  6. Variational optimization
  7. Heuristic optimization

Prerequisites

The course will be self-contained.  There are no pre-requisites for the course.

Assessment

Grades in the course will be based on six homework assignments.  Students will be given about 15 days to complete each assignment.  The course does not have any exams, quizzes or projects.

Textbook

The course does not have a textbook. The material to be learned will be provided as ten sets of lecture notes, which will provide detailed self-contained discussion of the topics. References to helpful books and articles will be provided as appropriate.

Office hours and interactions

The office hours will be held over zoom.  In addition, discussion boards will be set up to facilitate interactions among fellow students.

Instructor’s bio

Professor Prabhu is an award-winning educator who has taught the material covered in this course over a span of 32 years at various levels—from undergraduate courses to doctoral courses. He has advised several doctoral theses in optimization, has authored research articles on various topics in optimization and has published two books.  He holds a B.Tech. in Computer Science and Engineering from Indian Institute of Technology, Mumbai, a Ph.D. in Computer Science from NYU and a Ph.D. in theoretical physics from MIT.