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Access Grid - Semester 2, 2008

This page contains information on the subjects offered by the nGAME partner Universities via the Access Grid in semester 2, 2008.


For a timetable of all courses available via the Access Grid click here


For information on all the subjects offered via the Access Grid, including those from Universities outside this project, click here


Offered by the University of Sydney


MATH3966 Modules & Group Representations (Advanced)

Description: This unit deals first with generalized linear algebra, in which the field of scalars is replaced by an integral domain. In particular we investigate the structure of modules, which are the analogues of vector spaces in this setting, and which are of fundamental importance in modern pure mathematics. Applications of the theory include the solution over the integers of simultaneous equations with integer coefficients and analysis of the structure of finite abelian groups.

In the second half of this unit we focus on linear representations of groups. A group occurs naturally in many contexts as a symmetry group of a set or space. Representation theory provides techniques for analysing these symmetries. The component will deals with the decomposition of representation into simple constituents, the remarkable theory of characters, and orthogonality relations which these characters satisfy.

Pre-requistes: 12 credit point of intermediate maths. MATH3962 Rings, Fields and Galois Theory (Advanced)(assumed knowledge)

Prohibitions: MATH3907, MATH390

Description: This unit of study investigates the modern mathematical theory that was originally developed for the purpose of studying polynomial equations. The philosophy is that it should be possible to factorize any polynomial into a product of linear factors by working over a "large enough" field (such as the field of all complex numbers). Viewed like this, the problem of solving polynomial equations leads naturally to the problem of understanding extensions of fields. This in turn leads into the area of mathematics known as Galois theory. The basic theoretical tool needed for this program is the concept of a ring, which generalizes the concept of a field. The course begins with examples of rings, and associated concepts such as subrings, ring homomorphisms, ideals and quotient rings. These tools are then applied to study quotient rings of polynomial rings. The final part of the course deals with the basics of Galois theory, which gives a way of understanding field extensions.


Lecturer/coordinator: Andrew Henderson. Email - A.Henderson@maths.usyd.edu.au


Timetable:

Monday 10am-11am
Wednesday 10am-11pm and 1pm-2pm
Thursday 10am-11am

Enroll here


Offered by the University of South Australia


MATH1005 Advanced Optimization (offered as part Honours Mathematical Studies 2)


Description: Overview of subject content: We study optimization problems and methods in finite dimensional spaces. The course consists of two parts: (I) Theory and (II) Methods.
Part (I) includes: (i) classification of optimization problems, (ii) existence and uniqueness of solutions, and (iii) optimality conditions for unconstrained and constrained optimization: differentiable case and convex case (differentiable and non-differentiable). Part (II) includes the definition and convergence analysis of: (i) Methods for unconstrained problems: Newton's method, Steepest descent method (Cauchy and Armijo variants), Quasi-Newton methods, and (ii) Methods for constrained problems: Penalty and Barrier Methods.

The course may also include some mathematical background on topology of Rn and convex analysis, as well as some classical topological results involving continuous functions.


Pre-requisites: Available to honours students only.


Lecturer/coordinator: Regina S. Burachik. Email - regina.burachik@unisa.edu.au\


Timetable:
Tuesdays 9-11 am

Enroll here

MATH 1004 Regular and singular perturbations of optimization problems

Description: We will be first studying perturbations theory for problems of mathematical programming and then extend some of the results to problems of optimal control. The main attention will be paid to so called singularly perturbed problems, in which the dependence of the optimal value on the perturbation parameter is not continuous. Various applications of theoretical developments will be discussed

- Detailed syllabus:
- Regular and singular perturbations in linear programming problems
- Regular and singular perturbations in nonlinear programming problems
- Occupational measures formulations of optimal control problems and their equivalence
to infinite dimensional linear programming (IDLP) problems. Regularly and singularly
perturbed IDLP problems.
- Numerical analysis of IDLP problems and construction of near optimal feedback controls
in the corresponding optimal control problems

Basic knowledge of Linear Algebra, Calculus and Linear programming is required.

Assessment
(i) Exam/assignment/class work breakdown
Exam 50%
Project 50%
Class work N/A
(ii) Project due date: Week 11
Compiled by Geoff Prince 13 December 2006
(iii) Approximate exam date
20/11/2008

Required student resources
- Text/printed notes
N/A
- Software (local access)
ILOG CPLEX or other LP solver

Lecturer/Co-ordinator: Vlad. Gaitsgory. Phone: 8 302 3427 Email: v.gaitsgory@unisa.edu.au

Timetable: Wednesdays 9-11am (SA time)

Enroll here


Offered by the University of Wollongong


STAT904 Statistical Consulting


Description: Project management; Client liaison; Problem identification; Consulting ethics and principles; Sources of data; Choosing design and analysis procedures; Common problems in statistical consulting; Setting sample size - power calculations; Consulting case studies; Report writing.


A student who successfully completes this subject should be able to: (i) conduct efficiently a consulting session with a client; (ii) find information on statistical methodology using the resources of the Library and the World Wide Web ; (iii) explain the important principles behind designing and conducting an experiment or sample survey; (iv) determine appropriate statistical procedures to use on a wide variety of data sets; (v) apply and interpret procedures from a statistical package


Pre-requisites: Honours Students only, available to those that have done a statistics major.

Lecturer/coordinator: Professor David Steel. Email - dsteel@uow.edu.au


Timetable:

Tuesdays 11.30-1.30 starting 22 July

Enroll here


MATH942 Financial Mathematics

Description: The aim of the course is to give a practical understanding of the modelling, mathematical and numerical issues involved in building and implementing a mathematical model of a financial derivative. Abstract probabilistic analysis is not an aim of the course and there will be no theorems. The course will focus mainly on the partial differential equation formulation of the underlying problems. The interpretation of results in terms of risk-neutral processes and the connection with Monte Carlo will be achieved using the Kolmogorov
equations for the transition density function of a continuous random process (which will be explained but not proved).
Early in the 2nd half of the course a suitable exotic option is introduced (last year we did an Asian-rate put); the focus of the course is then to develop the theory and techniques to construct and implement a model of it. The model may be student dependent, tempered as necessary by the lecturer.

- Syllabus, preferably week by week
Revision of the basic Black-Scholes framework: (1-2 weeks)
What is a Brownian motion?
Ito's lemma in a simple form and the reason for Ito-integrals;
Delta-hedging and self-financing replication derivations of the Black-Scholes
equation (with one risky-asset);
Solution the Black-Scholes equation for a European call option;
Useful mathematical tricks for the Black-Scholes equation: (2 weeks)
Continuous dividend yields;
Digital options, power options and other options that can be priced by changes of
variables or simple systematic transformations of the call formulae;
Reflection principle and barrier options;
Pricing simple options by Monte Carlo: (3 weeks)
Basic Monte Carlo (in Matlab);
Variance reduction using antithetic variables and martingale variance reduction
Computing the hedge ratios in Monte Carlo;
Asian options: options which depend on averages: (3 weeks)
Modifications necessary to obtain the pricing equations;
Similarity reductions (or "change of numeraire");
Monte Carlo estimation methods for prices and hedge ratio;
Finite-difference methods: (2 weeks)
Simple explicit, fully-implicit and theta-methods for the diffusion and the Black-
Scholes equations;
Vanilla options (calls, puts, digitals);
Asian options (by solving the similarity-reduced pricing equations).


Breakdown of assumed prerequisite knowledge, including host prerequisite subject URLs:

Basic calculus and statistics (important results are the chain rule, Taylor series in one and two variables, normal distributions, properties of sums of random variables); Partial differential equations and the heat/diffusion equation in particular; Experience programming in a procedural language such as C, Pascal, (visual) Basic or Fortran. All numerical work will be demonstrated in Matlab, although student work will be accepted in most common languages (including R, S+ and Python) -- the criteria here is that the lecturer is able to understand and run it.
Host institution prerequisite: MATH317


Assessment
This is usually determined by negotiation with the class. Last year it was two
assignments worth 40% and 60% of total marks.
(i) Exam/assignment/class work breakdown
Exam 0 %
Assignment 100 %
Class work 0 %
(ii) Assignment due dates
End of September, middle of November


Required student resources
- Text/printed notes
These will be provided in class, on-line or via email as appropriate in the
form of pdf files for note and Matlab (ascii) files for computer code
- Software (local access)
Matlab and a pdf-viewer for the notes


Timetable: Thurs 3:30 from 24 July.

Lecturer: Jeff Dewynne. Email: dewynne@uow.edu.au

Enroll here


Offered by Macquarie University


STAT821 Multivariate Analysis


Description: Introduces methodologies and techniques for the exploration and analysis of multivariate data. Topics include graphical displays, discriminant analysis, principle
components analysis, multivariate normal distribution, multivariate linear models and cluster analysis.


Week/Topic
1 Introduction to multivariate analysis; Overview of matrix algebra
2 Matrix algebra (cont.); Basic concepts of multivariate distributions; Sample
statistics
3 Sample statistics (cont.); Some useful multivariate distributions
4 Multivariate distributions (cont.); Initial data analysis
5 Inferences: Estimation and hypothesis testing
6 Inference (cont.); MANOVA
7 MANOVA (cont.); Multivariate regression
8 Regression (cont.); Principal component analysis (PCA)
9 PCA (cont.)
10 Factor analysis
11 Factor analysis (cont.); Discriminant analysis and classification
12 Discriminant analysis (cont.); Logistic regression and its application in
classification
13 Cluster analysis


Assumed prerequisite knowledge:

probability distributions,regression, inference,hypothesis testing
Home Unit pre-req: STAT371 http://www.stat.mq.edu.au/units/stat371

Assessment
(i) Exam/assignment/class work breakdown
Exam 75 %
Assignment 25 %
(ii) Assignment due dates
Assignment 1: 10 September
Assignment 2: 15 October
Assignment 3: 5 November
(iii) Approximate exam date 21 November

Required student resources
- Text/printed notes
"Applied Multivariate Statistical Analysis" by Richard A. Johnson, Dean W.
Wichern (5th edition).
- Software (local access)
We are using S-PLUS in teaching this unit. However, as most S-PLUS codes
are also working in R, you may use R if you wish; please remember that R is
free. More information about R can be found at http://www.r-project.org/.


Lecturer/Coordinator: Dr Jun Ma. Phone: (+61-2) 9850-8548. Email: jma@efs.mq.edu.au

Enroll here




Updated on Oct 15, 2010 by Scott Spence (Version 18)


Subpages (1): Subject Descriptions

Attachments (1)

2008_Subject_Offerings.pdf - on Oct 15, 2010 by Scott Spence (Version 2)