Course Information
COMP3407 Scientific Computing

COMP3407 Scientific Computing

2019-20
Instructor(s):Wang W
(Class A) No. of credit(s):6
Recommended Learning Hours:
Lecture: 26.0
Tutorial: 13.0
Pre-requisite(s):COMP1117 or ENGG1111 or ENGG1112 or ENGG1330; and COMP2121
Co-requisite(s):  
Mutually exclusive with:  
Remarks:

Course Learning Outcomes

1. [Apply]
computational arithmetic in real-world scenarios: students can handle floating point numbers, rounding errors, relative and absolute errors, cancellation, and numerical instability
2. [Solve]
linear and non-linear equation systems, using Gaussian elimination, LU, QR and Cholesky decomposition, pivoting, norms, condition numbers iterative methods, and the bisection method, Newton’s method, the secant method respectively
3. [Recognise]
and apply various optimization techniques, such as the Golden section search, the steepest descent method, and the Newton method
4. [Interpolate]
data; students will learn data interpolation for approximation through the application of Lagrange interpolation, Newton interpolation, Hermite interpolation, and piecewise polynomial interpolation
5. [Integrate]
and differentiate numerical functions using forward difference approximation, Richardson extrapolation, the composite rule, Newton-Cotes quadrature, and Gaussian quadrature
Mapping from Course Learning Outcomes to Programme Learning Outcomes
 PLO aPLO bPLO cPLO dPLO ePLO fPLO gPLO hPLO iPLO j
CLO 1T,PT,PT,P
CLO 2T,PT,PT,PT,P
CLO 3T,PT,PT,PT
CLO 4T,PT,PT,PT
CLO 5T,PT,PT,PT

T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to here.

Syllabus

Calendar Entry:
This course provides an overview and covers the fundamentals of scientific and numerical computing. Topics include numerical analysis and computation, symbolic computation, scientific visualization, architectures for scientific computing, and applications of scientific computing.

Detailed Description:

Computer Arithmetic Mapped to CLOs
floating point numbers, rounding errors, relative and absolute errors, cancellation, and numerical instability.1
Solving linear system of equation and optimization Mapped to CLOs
Solve linear and non-linear equation systems, using Gaussian elimination, LU, QR and Cholesky decomposition, pivoting, norms, condition numbers iterative methods, and the bisection method, Newton’s method, the secant method respectively2
Optimization techniques, such as the Golden section search, the steepest descent method, and the Newton method3
Data interpolation and fitting Mapped to CLOs
Interpolate data; students will be proficient in data interpolation for approximation through the application of Lagrange interpolation, Newton interpolation, Hermite interpolation, and piecewise polynomial interpolation4
Least squares data fitting, Normal equation, QR decomposition4
Integration and differentiation Mapped to CLOs
Integrate and differentiate numerical functions using forward difference approximation, Richardson extrapolation, the composite rule, Newton-Cotes quadrature, and Gaussian quadrature.5

Assessment:
Continuous Assessment: 50%
Written Examination: 50%

Teaching Plan

Please refer to the corresponding Moodle course.

Moodle Course(s)