40.002 Optimisation

No. of Credits: 12 Subject Credits


Pre-requisites (for Exchange Students): Multivariable Calculus and Linear Algebra

Course Description

The course covers a broad range of optimisation algorithms and models.

The course will cover the following topics: linear programming, simplex algorithm, duality, sensitivity analysis, two player zero-sum games, network optimisation, minimum cost flow, network simplex algorithm, integer programming, branch and bound methods, cutting plane methods, dynamic programming.

Throughout the course, a number of applications from various areas will be discussed.

Learning Objectives

At the end of the term, students will be able to:

  • Formulate a linear optimisation model and determine the appropriate algorithm to solve
  • Understand and appreciate the relative computational difficulty of different types of optimisation models
  • Understand duality and sensitivity analysis for linear optimisation
  • Identify potential applications of optimisation to engineering systems problems

Measurable Outcomes

  • Be able to formulate a linear optimisation model and know how to solve it
  • Be able to interpret the output from the solution of an optimisation problem and provide intuition on why it is the optimum
  • Be able to interpret and apply the sensitivity analysis reports from linear optimisation
  • Demonstrate a working knowledge of software for solving optimisation problems

Course Notes

This set of course notes was graciously shared by Tong Hui Kang , updated as of 02 February 2020.

Follow him on GitHub and give him your messages of appreciation!

Download Midterm Notes

Download Finals Notes

View Source Code

Author's Note

This contains my revision notes midterms and finals. I have obtained 90% for the first three of the four exams/quiz.

The images are not my work, and most of them are taken from the slides.

The source code is available in my repository.