Module Information

Module Identifier
Module Title
Academic Year
Semester 1

Course Delivery

Delivery Type Delivery length / details
Seminars / Tutorials 3 Hours. (3 x 1 hour example classes)
Lecture 19 Hours. (19 x 1 hour lectures)


Assessment Type Assessment length / details Proportion
Semester Exam 2 Hours   (written examination)  100%
Supplementary Assessment 2 Hours   (written examination)  100%

Learning Outcomes

On completion of this module, a student should be able to:
1. decide whether given formulae are norms and decide whether two norms are equivalent;
2. define norms by means of inner products;
3. compute norms on finite dimensional spaces and explain why all such norms are equivalent;
4. compute the L_1, L_2 and L_{infinity} norms on C[0,1] and prove that not all norms on this space are equivalent;
5. define norms on C^{1}[0,1];
6. describe the concept of continuity and determine whether given linear maps are continuous;
7. define the norm of a continuous linear map and compute it in simple cases;
8. describe the idea of completeness with reference to R^{n} and C[0,1];
9. prove the contraction mapping theorem;
10. use the contraction mapping theorem to derive results on the existence and uniqueness of solutions to integral and differential equations;
11. state Picard's Theorem, and calculate Picard iterates.

Brief description

The development of Mathematical Analysis and its applications requires a concept of distance to be defined on a vector space. This can be achieved by introducing the idea of a norm. This module is concerned with the development of the theory of normed spaces leading to the proof of the contraction mapping theorem and an introduction to the fundamental ideas of the theory of differential equations.


To introduce the idea of a normed space and to familiarise students with the use of norms; to prove the contraction mapping theorem and to provide an introduction to the theory of differential equations.


1. Normed spaces: definition, examples; equivalent norms.
2. Inner product spaces: definition, the Cauchy-Schwarz inequality, the norm corresponding to an inner product.
3. Finite dimensional spaces: the l_{1}, l_{2}, l_{infinity} norms; the equivalence of all norms on a finite-dimensional space.
4. Infinite dimensional spaces: the L_{1}, L_{2}, L_{infinity} norms on C[0,1]; norms on C^{1}[0,1].
5. Continuity of functions from one normed space to another. Continuous linear maps.
6. The norm of a continuous linear map and its calculation in simple cases.
7. The idea of completeness with reference to R^n and C[0,1] with the L_{infinity} norm.
8. Contraction mappings; the contraction mapping theorem.
9. Integral equations: the existence and uniqueness of solutions using the contraction mapping theorem.
10. Picard's Theorem and Picard iteration.

Reading List

Recommended Text
W A Light (1990) An Introduction to Abstract Analysis Chapman & Hall Primo search
Supplementary Text
E Kreyszig (1978) Introductory Functional Analysis with Applications Wiley Primo search J R Giles (1987) Introduction to the Analysis of Metric Spaces Cambridge University Press Primo search N Young (1988) An Introduction to Hilbert space Cambridge University Press Primo search


This module is at CQFW Level 6