Module Identifier MX35110  
Module Title INTRODUCTION TO NUMERICAL ANALYSIS  
Academic Year 2006/2007  
Co-ordinator Dr Simon J Cox  
Semester Semester 2  
Other staff Dr Simon J Cox  
Pre-Requisite MA11210  
Mutually Exclusive MA25110  
Course delivery Lecture   19 Hours. (19 x 1 hour lectures)  
  Seminars / Tutorials   3 Hours. (3 x 1 hour example classes)  
Assessment
Assessment TypeAssessment Length/DetailsProportion
Semester Exam2 Hours (written examination)  100%
Supplementary Assessment2 Hours (written examination)  100%

Learning outcomes

On completion of this module, students should be able to:
1. construct an interpolating polynomial using either the Lagrange or Newton formula, and describe their relative advantages and disadvantages;
2. prove the error formula for Lagrange interpolation;
3. construct divided and forward difference tables for prescribed data;
4. construct a cubic spline interpolant and discuss the advantage of this approach over the use of the Lagrange interpolant;
5. derive the trapezoidal and Simpson's rules for approximating an integral;
6. derive the error term for the trapezoidal rule;
7. derive the formula for Romberg integration;
8. derive Gauss-type integration rules;
9. determine the root(s) of a nonlinear equation using the bisection method, functional iteration and Newton's method;
10. state and prove the conditions under which the sequence x_{r+1}=g(x_[r}) converges to a unique root of the equation x=g(x);
11. determine the order of an iterative process for computing the root of an equation;
12. give a geometrical interpretation of Newton's method;
13. state the conditions under which an initial value problem possesses a unique solution;
14. define the concepts of consistency, convergence and stability for one-step methods for solving initial value problems;
15. compute numerical approximations to the solution of initial value problems using one-step methods including predictor-corrector methods;
16. determine the consistency, convergence and stability of given one-step methods.

Brief description

It is often impossible to find the exact solution of a mathematical problem using standard techniques. In these situations one has to resort to numerical techniques. Numerical analysis is concerned with the development and analysis of methods for the numerical solution of practical problems. This course will provide an introduction to the subject.

Aims

To introduce students to techniques for the numerical approximation of mathematical problems, and to the analysis of these techniques.

Content

1. POLYNOMIAL INTERPOLATION: Lagrange' formula. Newton's formula and divided differences. The forward difference formula. Interpolation error. Cubic spline interpolation.
2. NUMERICAL INTEGRATION: Trapezoidal rule, Simpson's rule. Composite integration rules. Quadrature errors. Romberg integration. Gaussian quadrature rules.
3. SOLUTION OF NONLINEAR EQUATION IN A SINGLE VARIABLE: Bisection method. Fixed point methods and contraction mappings. Newton's method. Order of convergence.
4. INITIAL VALUE PROBLEMS: Existence and uniqueness of solutions. Euler's method. Local truncation error.
Consistency. Convergence. Stability. General one-step methods. Trapezoidal method. Predictor-corrector methods.

Reading Lists

Books
** Supplementary Text
C F Gerald and P O Wheatley (1999) Applied Numerical Analysis 6th Ed. Addison-Wesley 0201474352
A S Wood (1999) Introduction to Numerical Analysis Addison Wesley 020134291X
J F Epperson (2001) An Introduction to Numerical Methods and Analysis Wiley 0471316474

Notes

This module is at CQFW Level 6