# 40.002 Optimisation¶

**No. of Credits:** 12 Subject Credits

**Pre-requisites:**

**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.

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### 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.