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Last modified: 25 May 2018 11:16

Course Overview

Linear optimisation is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. It is widely used in business and economics, and is also utilised for some engineering problems. Industries that use linear programming models include transportation, energy, telecommunications, and manufacturing. It has proved useful in modeling diverse types routingschedulingassignment, and design.

Course Details

Study Type Undergraduate Level 4
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Old Aberdeen Sustained Study No
  • Professor Vassili Gorbunov

Qualification Prerequisites

  • Either Programme Level 3 or Programme Level 4

What courses & programmes must have been taken before this course?

  • MA1005 Calculus 1 (Passed)
  • Either Mathematics (MA) or Mathematical Sciences (MX)
  • Any Undergraduate Programme (Studied)
  • Either MA2008 Linear Algebra I (Passed) or MA2506 Linear Algebra (Passed)

What other courses must be taken with this course?


What courses cannot be taken with this course?

  • MX3022 Optimisation and Numerical Analysis (Studied)

Are there a limited number of places available?


Course Description

In many eal life problems one is required to find optimal solutions, namely a solution which, generally speaking, either minimises cost or maximises gain. To do so, one models the problem mathematically, and then applies the appropriate mathematical techniques to find the optimal solution. In this course students will learn how to formulate optimisation problems mathematically and study the relevant techniques from analysis and algebra which are useful in solving them, for example the Simplex Algorithm developed by Dantzing in 1947. Applications to “real life” problems and the use of computer software to solve them will also be discussed.

Further Information & Notes


  • to formulate optimisation problems and study them in a general setup; 
  • to use results from analysis and algebra to study specific types of optimisation problems;
  • to get a glimpse of numerical analysis methods and their applications; and 
  • to use optimisation and numerical analysis techniques in solving applicable problems. 


Intended Learning Outcomes
1.Understand the concept of "optimization problem"
2.Be able to formulate a concrete optimization problem in mathematical terms.
3.Be familiar with the technique of Lagrange Multipliers nd be able to apply it.
4.Understand the Simplex algorithm and be to apply it.
5.Understand and be able to apply the techniques of Linear Programming.


This course alternates with MX4087 Financial Mathematics.

Degree Programmes for which this Course is Prescribed


Contact Teaching Time

Sorry, we don't have that information available.

Teaching Breakdown



Formative Assessment




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