Courses Handbook 2008 - [ Archived ]

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

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.

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