
Academic Year:  2017/8 
Owning Department/School:  Department of Mathematical Sciences 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Intermediate (FHEQ level 5) 
Period: 

Assessment Summary:  CW 25%, EX 75% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  Before taking this unit you must take XX10190 or CM10228 and before or while taking this unit you must take MA20218. Students who have not taken XX10190 must confirm they are aware of the need to program in MATLAB. 
Description:  Aims: To give an introduction to numerical analysis, including the role of numerical analysis as the foundation for scientific computing. To develop general mathematical skills and to enable students to take final year courses on numerical analysis. Learning Outcomes: After taking this unit, students should be able to: * Demonstrate knowledge of computational methods for the approximation of functions, integrals, and solutions to systems of equations (e.g., linear equations and ordinary differential equations). * Understand the approximation theory of some computational methods. * Implement and use these methods in Matlab. * Write the relevant mathematical arguments in a precise and lucid fashion. Skills: Numeracy T/F A Problem Solving T/F A Computation skills T/F A Written and Spoken Communication F (in tutorials). Content: Introduction: What is numerical analysis? Floatingpoint numbers and rounding error. Concepts of convergence and accuracy (e.g., absolute and relative errors, order of convergence). Nonlinear systems of equations: The fixedpoint theorem and rootfinding problem. Examples including Newton's method. Approximation of functions: Polynomial interpolation and error analysis. Applications to numerical integration (e.g., NewtonCotes formulae, Gauss quadrature, composite rules) and the numerical solution of initialvalue problems for ODEs (e.g., the Euler and thetamethods; stability, consistency, and convergence). Linear systems of equations: Matrix norms and condition numbers. Iterative methods (e.g., Jacobi and GaussSeidel) vs direct methods (e.g., rowreduction methods and Gaussian elimination). 
Programme availability: 
MA20222 is Compulsory on the following programmes:Department of Mathematical Sciences
MA20222 is Optional on the following programmes:Department of Computer Science

Notes:
