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MATH412-12S2 (C) Semester Two 2012
Unconstrained Optimization

0.1250 EFTS
09 Jul 2012 - 11 Nov 2012 		↓Other occurrences

Description

Unconstrained Optimization

This course looks at the minimization of smooth functions of several variables.  The first part of the course examines gradient based methods using line searches, including Newton, quasi-Newton, and conjugate gradient methods.  

A selection of other topics is then introduced, including trust region methods and methods for constrained optimization.  

Demonstration software is used to illustrate aspects of various algorithms in practice.

Topics:
• Gradient based methods: steepest descent, conjugate gradients, Newton's method and quasi-Newton methods. Line searches and trust regions.
• Constrained optimization: Karush-Kuhn-Tucker conditions, quadratic penalty functions, augmented Lagrangians.
• Derivative free methods: positive bases, Clarke's generalized derivative, frames.

Pre-requisites

Subject to approval of the Head of Department.
Lectures
Streams Day Time Where Notes
Stream 01 Tuesday 4:00pm-5:00pm Erskine 441 9 Jul - 19 Aug,
3 Sep - 14 Oct
Wednesday 9:00am-10:00am Erskine 441 9 Jul - 19 Aug,
3 Sep - 14 Oct

Course Coordinator / Lecturer

Chris Price (MATH)

Assessment

Assessment Due Date Percentage 
Internal Assessment - TBA 30%
Final Examination 70%

Examination and Formal Tests

Exam Thursday 08 Nov 2012 2:30pm-4:30pm  

Textbooks

Recommended Texts:

• Numerical Optimization, Nocedal and Wright (2006).
• Practical Methods of Optimisation, Fletcher (1987).
• Practical Optimization, Gill, Murray, and Wright (1981).

Fees

Domestic fee $788.00
International fee $3,588.00


For further information see Mathematics and Statistics.

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