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

Pre-Requisite Subject(s)

• 10.018 Modelling Space and Systems

Prerequisites (for Exchange Students):  Multivariable Calculus and Linear Algebra

12 Credits

Image Credit (http://graphics.stanford.edu/projects/lgl/papers/szggm-icqgdgdsn-08/image.gif)