310503 (v.1) Numerical Optimisation 302

Area:	Department of Mathematics and Statistics
Credits:	25.0
Contact Hours:	4.0
The tuition pattern below provides details of the types of classes and their duration. This is to be used as a guide only. For more precise information please check your unit outline.
Lecture:	1 x 3 Hours Weekly
Tutorial:	1 x 1 Hours Weekly
Prerequisite(s):	8127 (v.6) Advanced Calculus 201 or any previous version OR 8648 (v.3) Mathematical Methods 201 or any previous version
Syllabus:	Optimisation models. One-dimensional search techniques. Unconstrained optimisation techniques for functions with several variables, including search methods using function values only, steepest descent method, Newton's method; quasi-Newton's methods, conjugate gradient methods, accurate and inaccurate line searches, convergence and rate of convergence. Constrained optimisation techniques, including Lagrangian multipliers, Kuhn-Tucker optimality conditions, penalty function methods, quadratic programming techniques, sequential quadratic programming technique. Dynamic programming. Branch and bound methods.
To ensure that the most up-to-date information about unit references, texts and outcomes appears, they will be provided in your unit outline prior to commencement.
Field of Education:	010101 Mathematics
SOLT (Online) Definitions*:	Not Online *Extent to which this unit or thesis utilises online information
Result Type:	Grade/Mark

Availability

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