| Semester 1, 2023 Online | |
| Units : | 1 |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Course Coordinator:
Requisites
Pre-requisite: (CSC2410 or CSC1401) and (MAT1102 or ENM1600) or Students must be enrolled in one of the following Programs: MPIT or MCOT or MCTE
Overview
Many areas of computing in engineering, science, technology, and games require programmers to have insight and skills in the implementation of common numerical computations. Programming high-performance computers to rapidly perform large scale tasks requires considerable skill. Modern vector and super-scalar computers are very fast - but to achieve anything remotely like the peak speed requires special programming styles sympathetic to the computer architecture. Using fundamental algorithmic tasks of science, this course develops the ability to design good algorithms for modern computer architectures.
This course develops skills in programming modern high-performance computers. It examines some of the typical hardware architectures and how they affect performance and programming. Algorithms to illustrate the principles are chosen from a range of scientific tasks. The course includes the study of numerical solutions of linear and non-linear equations, numerical interpolation and curve fitting, the numerical solution of ordinary differential equations, and Monte Carlo simulation. Interaction utilising modern graphics is exploited.
Course learning outcomes
Completion of this course will enable students to:
- discern the relationship between computer architecture and program performance
- understand the principles of high-performance programming using vector operations
- demonstrate an understanding of a variety of computer-based numerical methods and their errors, used in the solution of numerical problems
- document, analyse and describe complex numerical code
- choose and implement appropriate numerical techniques (including graphics) for a range of real-world problems
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. |
Basics of numerical computation Performance Measures and Computational Error Analysis |
20.00 |
| 2. |
Solving Linear and Nonlinear Equations Newton's method and other fixed-point iteration; linear systems; condition numbers; Jacobi's iterative solution of linear systems; Newton's method for nonlinear systems. |
20.00 |
| 3. |
Numerical Interpolation and Curve Fitting Interpolation with polynomials, derivatives, and integrals of interpolants; least squares approximations. |
20.00 |
| 4. |
Solution of ordinary differential equations Difference approximations; Euler's method; modified Euler's method; the Runge-Kutta RK4 method, systems of ODES, introduction to shooting and finite difference methods. |
20.00 |
| 5. |
Simulation and Monte Carlo methods Introduction to process simulation (e.g., random walks), Monte Carlo integration, Random Numbers |
20.00 |
Text and materials required to be purchased or accessed
(Available on the 精东传媒appDesk in Electronic Form).
Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Problem Solving 1 | No | 30 | 1,2,3,4,5 |
| Problem Solving 2 | No | 35 | 1,2,3,4,5 |
| Problem Solving 3 | No | 35 | 1,2,3,4,5 |