Optimisation (Linear Programming)
Undergraduate: Level 5
Monday 13 January 2020
Friday 20 March 2020
04 October 2019
Requisites for this module
BSC L1G1 Economics with Mathematics,
BSC L1G3 Economics with Mathematics (Including Placement Year),
BSC L1G8 Economics with Mathematics (Including Foundation Year),
BSC L1GC Economics with Mathematics (Including Year Abroad),
BSC G100 Mathematics,
BSC G102 Mathematics (Including Year Abroad),
BSC G103 Mathematics (Including Placement Year),
BSC G104 Mathematics (Including Foundation Year),
BSC 5B43 Statistics (Including Year Abroad),
BSC 9K12 Statistics,
BSC 9K13 Statistics (Including Placement Year),
BSC 9K18 Statistics (Including Foundation Year),
BSC G1G4 Mathematics with Computing (Including Year Abroad),
BSC G1G8 Mathematics with Computing (Including Foundation Year),
BSC G1GK Mathematics with Computing,
BSC G1IK Mathematics with Computing (Including Placement Year),
BSC I1G3 Data Science and Analytics,
BSC I1G3CE Data Science and Analytics,
BSC I1GB Data Science and Analytics (Including Placement Year),
BSC I1GBCE Data Science and Analytics (Including Placement Year),
BSC I1GC Data Science and Analytics (Including Year Abroad),
BSC I1GF Data Science and Analytics (Including Foundation Year)
An introduction to the methods of linear programming, including both theoretical and computational aspects.
Formulation of linear programming models
The Simplex Algorithm, Two-Phase Simplex and Revised Simplex
Duality, Complementary Slackness and Dual Simplex
Implementation of some of these ideas using MATLAB.
On completion of the course students will be able to:
- formulate an appropriate linear programming model, from a written description of a problem environment, whose solution would actually solve the problem;
- recognise the scope and limitations of linear programming modelling and appreciate its position within the Operational Research discipline;
- solve any (small) linear programming problem using an appropriate version of the Simplex Algorithm;
- perform sensitivity analysis on an optimal solution;
- use Duality Theory to prove basic theorems of Linear Programming and apply Duality Theory to recognize optimality, infeasibility or unboundedness in a linear program;
- apply the Transportation Simplex Algorithm under a variety of scenarios.
-make use of the MATLAB computer package to solve linear programming problems.
No additional information available.
This module has two one hour lectures each week, an additional lecture in even-numbered weeks and a class in odd-numbered weeks.
In the Summer term 3 hours of revision lectures are given.
This module does not appear to have a published bibliography.
Assessment items, weightings and deadlines
|Coursework / exam
||120 minutes during Summer (Main Period) (Main)
Module supervisor and teaching staff
Prof Abdel Salhi, email email@example.com, Dr Georgios Papamikos (firstname.lastname@example.org)
Professor Abdel Salhi (email@example.com), Dr Georgios Papamikos (firstname.lastname@example.org)
Prof Fionn Murtagh
Professor of Data Science
Available via Moodle
Of 33 hours, 33 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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