310503 (v.2) Numerical Optimisation 301
Note
Tutition Patterns
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.
Unit references, texts and outcomes
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.
| Area: | Department of Mathematics and Statistics |
|---|---|
| Credits: | 25.0 |
| Contact Hours: | 4.0 |
| 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. |
| 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
Availability Information has not been provided by the respective School or Area. Prospective students should contact the School or Area listed above for further information.
