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OPTIMIZATION THEORY

Module code: EE616
Credits: 7.5
Semester: 2
Department: ELECTRONIC ENGINEERING
International:

	Overview
	The aim of this module is to introduce the student to unconstrained and constrained optimisation Indicative Syllabus: Unconstrained Optimisation: Fundamental principles of optimisation Line searches for functions with one variable Direct search methods for functions of more than one variable Gradient methods for functions of more than one variable Hessian-based gradient methods for functions of more than one variable The sum of Squares Problem Constrained Optimisation: First Order Kuhn-Tucker Theorem The Augmented Lagrangian Penalty methods Barrier Methods Advanced Optimisation: Genetic Algorithms Particle Swarm Optimisation For more detailed information on this module please use the following web address: https://www.maynoothuniversity.ie/electronic-engineering/current-students/module-descriptors

Overview

The aim of this module is to introduce the student to unconstrained and constrained optimisation
Indicative Syllabus:
Unconstrained Optimisation:
*Fundamental principles of optimisation
*Line searches for functions with one variable
*Direct search methods for functions of more than one variable
*Gradient methods for functions of more than one variable
*Hessian-based gradient methods for functions of more than one variable
*The sum of Squares Problem

Constrained Optimisation:
*First Order Kuhn-Tucker Theorem
*The Augmented Lagrangian
*Penalty methods
*Barrier Methods

Advanced Optimisation:
*Genetic Algorithms
*Particle Swarm Optimisation

For more detailed information on this module please use the following web address:
https://www.maynoothuniversity.ie/electronic-engineering/current-students/module-descriptors

	Learning Outcomes
	On successful completion of the module, students should be able to: Labs 15% End of Module test 60% Assignments (3) 25% Labs 15% End of Module test 60% recognize optimisation problems carry out line searches for functions with one variables apply gradient methods to functions of more than one variable solve simple unconstrained and constrained optimisation problems model optimisation problems arising from basic applications classify optimisation problems and choose appropriate solvers for them.

Learning Outcomes

Teaching & Learning methods

Assessment

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? Yes 1 * 2hour written exam in the Autumn. The Continuous Assessment mark is carried forward as there is no facility for repeating the continuous assessment elements of the course. University scheduled written examination (Autumn): 120 minutes Is Supplemental Continuous Assessment registration permitted? No Is Supplemental Continuous Assessment capped? No

Autumn Supplementals/Resits

	Pre-Requisites
	A basic knowledge of Matrices and a working knowledge of MatLab

	Co-Requisites
	None

	Additional Reading
	Reference Reading: Singiresu, S. Rao, Engineering Optimization, Theory and Practice. Brian D Bunday, Basic Optimization Methods. E.M.L. Beale, Introduction to Optimization. R.Fletcher, Practical Methods of Optimization. Rangarajan K. Sundaram, A First Course in Optimization Theory. J. J. McKeown, D. Meegan, D. Sprevak, An Introduction to Unconstrained Optimization. *S. Boyd, L. Vandenberghe, Convex Optimization

Additional Reading

	Timetable
	This timetable is not currently available, please contact the Electronic Engineering Department.

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OPTIMIZATION THEORY