Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: EEE319
Module Title: Optimization
Module Level: Level 3
Module Credits: 2.50
Academic Year: 2017/18
Semester: SEM2
Originating Department: Electrical and Electronic Engineering
Pre-requisites: EEE116
   
Aims
To provide students with the ability to:

Understand various engineering optimisation problems.

Select appropriate optimisation methods to deal with a specific engineering optimisation problem.

Develop software packages using MATLAB to resolve an optimisation problem.

Gain their own knowledge of optimization subjects for further development.
Learning outcomes 
Knowledge and Understanding

On successful completion of this module the student should have:

An appreciation of the use of optimisation methods for system analysis and modelling;

An understanding of how Linear programming, Non-linear programming and Dynamic programming can be used to solve system optimisation problems.

An appreciation of how computer-aided design and simulation tools operate;

An understanding of how the optimisation methods are applied to industrial and engineering optimisation problems.

An understanding of optimisation algorithm development.


Intellectual Abilities

On successful completion of this module the student should be able to pursue the further study by themsleves in this subject and relevant areas.


Practical Skills

On successful completion of this module the student should be able to understand the basic engineering optimisation problems and resolve them using MATLAB.


General Transferable Skills On successful completion of this module the student should be able to show experience and enhancement of the following key skills:

Independent learning

Problem solving and design skills
Method of teaching and learning 
This module will be delivered through a combination of formal lectures, tutorials and supervised laboratory sessions.
Syllabus 
Lecture 1-4 System optimisation methods Concepts of system optimisation Constrained optimisation; Unconstrained optimisation. Lagrange multiplier; Slack variables; Gradient search direction; Numerical computation.

Lecture 5-10 Gradient-based Optimisation Methods Gradient methods; Steepest descent method; Newton method; Quasi-Newton methods; Conjugate gradient method; Feasible direction method.

Lecture 11-12 Optimisation Algorithms and Computation Tools Optimisation algorithms and computation tools Applications to industrial and engineering optimisation problems
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 14     0  14    47  75 

Assessment

Sequence Method % of Final Mark
1 Final Exam 85.00
2 Continuous Assessments 15.00

Module Catalogue generated from SITS CUT-OFF: 10/22/2017 10:30:26 AM