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EEEN40580

Academic Year 2023/2024

Optimisation Techniques for Engineers (EEEN40580)

Subject:
Electronic & Electrical Eng
College:
Engineering & Architecture
School:
Electrical & Electronic Eng
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Declan Delaney
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Mathematical optimization is concerned with finding the best available value of an objective function through appropriately choosing controllable variables/designer parameters within a set of given constraints. A broad variety of problems arising from diverse applications including production optimization in manufacturing, crew scheduling and aircraft assignment for airlines, optimal production and distribution of electricity or portfolio management in finance, can be formulated as mathematical optimization problems. This module presents the mathematical concepts and algorithmic tools for modeling and solving practical optimization problems.

1. Linear programming (the simplex algorithm, manufacturing, multiperiod models)

2. Integer programming (Assignment problems, optimization problems on graphs, shortest path problems, branch & bound)

3. Duality (Lagrange dual, weak & strong duality)

4. Convex optimization (convex functions, optimality conditions, duality, quadratic optimization)

5. Non-convex optimization (local vs. global optimization, optimality conditions, convex relaxations, )

6. Nonlinear optimization algorithms (gradient descent methods, quasi-Newton methods, interior-point methods, sub-gradients)

7. Optimization under uncertainty (uncertainty modeling, robust optimization, )


The coding language will be Python in this module

About this Module

Learning Outcomes:

1. Be able to formulate real world optimization problems and incorporate uncertainty.

2. Be capable of implementing optimization solvers.

3. Convert problems to their dual formulation.

4. Be able to prove basic results on convex optimization.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Specified Learning Activities

12
Autonomous Student Learning

80
Online Learning

11
Total

127

Approaches to Teaching and Learning:
1. Lectures first introduce the ideas behind the theory that is going to be developed;
2. The formalisation of the idea leads to the development of the theory;
3. Whenever possible, software is used to develop experiments illustrating the main features of the theory;
4. During each lecture, the links with the lecture before and after are outlined so as to gain the logical links between the topics

Requirements, Exclusions and Recommendations
Learning Recommendations:

A good facility with computer scripting, as well as a solid grasp on numerical methods will ground a student well for this module. A good familiarity with MATLAB or C++ or Python, Julia or GAMS will be very helpful.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Examination: Online exam 2 hour End of Trimester Exam Yes Standard conversion grade scale 40% No
40
 No
Assignment: Solving some optimisation problems , interpreting the results, coding Varies over the Trimester n/a Graded No
30
 No
Multiple Choice Questionnaire: Online exam, Mix of theoretical and practical questions Week 7 n/a Standard conversion grade scale 40% No
30
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

The coding language in this module will be Python

Suggested books:
Theory:
A gentle introduction to optimization, by B. Guenin, J. Konemann, J. Tuncel. Publisher: Cambridge University Press
Boyd, Stephen, Stephen P. Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.

Practical examples and tools:
Hart, William E., et al. Pyomo-optimization modeling in python. Vol. 67. Berlin: Springer, 2017.
Soroudi, Alireza. Power system optimization modeling in GAMS. Vol. 78. Switzerland: Springer, 2017.

Name Role
Cliodhna Gartland Tutor
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