Basic Information

• Course Code: IE 609
• Course Name: Mathematical Optimisation Techniques
• Course Offered In: 2023-‘24
• Semester Season: Autumn
• Instructors: Prof. Vishnu Narayana
• Prerequisites: None
• Difficulty (1 being easy and 5 being tough): 2

Course Content

The course content mainly cover : 1) Basics of linear optimization : Linear programs and duality 2) Basics of constrained optimization : Lagrangian theory and sensitivity analysis 3) Basics of non-linear optimization : Multivariable calculus and other methods

A good amount of time is devoted to developing and analyzing the simplex method. The instructor also discusses several examples to drive the concepts home.

Feedback on Lectures

The lectures are very slow paced and easy to follow. The instructor takes sufficient time to explain the concepts before moving onto the next one. However, this also gives less time to solve doubts, and the instructor often tends to leave the thinking part up to the students. A lot of concepts are not covered, and a lot of self study is required if one really wants to understand the concepts holistically, and connect the various tracks covered in the course.

Feedback on Evaluations

The evaluations only consisted of three short assignments, to be done in groups of 4, and one endsem. Both the assignments and endsem are relatively easy, and can be solved with very little preparation. The grading was lenient, with nearly 9.5% getting AA, and almost 30% getting a grade point greater than or equal to 9.

Study Material and Resources

Introduction to Linear Optimization by Bertsimas and Tsitsiklis

Final Takeaway

This is a very chill course, however, the instructor does not allow students from third year or below to credit the course.