## MAS331 Metric Spaces

 Semester 1, 2017/18 10 Credits Lecturer: Prof David Applebaum Home page Timetable Reading List Aims Outcomes Teaching Methods Assessment Full Syllabus

This unit explores ideas of convergence of iterative processes in the more general framework of metric spaces. A metric space is a set with a "distance function" which is governed by just three simple rules, from which the entire analysis follows. The course follows on from MAS221 `Analysis', and adapts some of the ideas from that course to the more general setting. The course includes the Contraction Mapping Theorem, which guarantees the convergence of quite general processes; there are applications to many other areas of mathematics, such as to the solubility of differential equations.

Prerequisites: MAS221 (Analysis)

The following modules have this module as a prerequisite:

 MAS435 Algebraic Topology MAS436 Functional Analysis

## Outline syllabus

• Metric spaces: definition, properties and examples
• Convergence of sequences
• Open and closed subsets
• Continuity
• Cauchy sequences, completeness
• Iteration and the Contraction Mapping Theorem
• Compactness

## Office hours

Tuesday 11am
Friday 11am
Feel free to come at other times if this doesn't work for you, but please e-mail first to check that I'll be there!

## Aims

• To point out that iterative processes and convergence of sequences occur in many areas of mathematics, and to develop a general context in which to study these processes
• To provide a basic course in analysis in this setting
• To reinforce ideas of proof
• To illustrate the power of abstraction and show why it is worthwhile
• To provide a foundation for later analysis courses

## Learning outcomes

• decide whether sequences converge, and find the limits in a number of concrete and abstract spaces;
• decide whether given functions are or are not metrics;
• determine, in a variety of contexts, whether a given subset of a metric space is closed;
• discover whether examples of metric spaces are complete using Cauchy sequences;
• understand iterative processes on general spaces;
• prove and apply the Contraction Mapping Theorem;
• understand compactness.

## Teaching methods

Lectures, problem solving

20 lectures, no tutorials

## Assessment

One formal 2.5 hour written examination. You should do 4 questions out of 5.

## Full syllabus

1. Metric spaces
(4 lectures)

Distance functions. Definition of metric space. Review of suprema and infima. Examples including Rn and function spaces. Some basic properties of metrics. Subspaces. Closed balls and open balls.
2. Convergence of sequences
(3 lectures)
Definition in terms of N and ϵ. Examples. Basic properties: uniqueness of limit, equivalence of xn→ x with d(xn,x)→0 in R. Convergence in R2 means convergence componentwise. Convergence in function spaces and pointwise convergence.
3. Closed and open sets
(2 lectures)
Definition of closed sets and open sets. Behaviour under intersections and unions. Examples.
4. Continuity
(2 lectures)
Continuity in terms of sequences and in terms of ϵ and δ. Relation with closed sets and open sets. Examples.
5. Cauchy sequences and completeness
(2 lectures)
`Internal' tests for convergence, without knowledge of the limit. Cauchy sequences. Completeness. Bolzano-Weierstrass. Examples including Rn and function spaces.
6. Iteration and Contraction
(4 lectures)
Iteration as a method to solve problems. Examples in R, R2. Fixed points of iterations. Discussion of what should be required to guarantee convergence of iterative procedures to fixed points. Contractions. Examples. The Contraction Mapping Principle. An application to linear algebra. A differential criterion for a function to be a contraction. Functions with the property that repeated application gives a contraction. Application to existence of solution of differential equations. Examples.
7. Compactness
(3 lectures)
Definition using subsequences. Compactness and continuity. Examples. Compact subsets of Euclidean spaces. Equivalent formulations in terms of total boundedness and completeness, and in terms of the Heineâ€“Borel property.

Type Author(s) Title Library Blackwells Amazon
B Bryant Metric spaces: iteration and application 512.811 (B) Blackwells Amazon
B Carothers Real Analysis 517.51 (C) Blackwells Amazon
B Haaser and Sullivan Real Analysis 517.51(H) Blackwells Amazon
C Kreyszic Introductory Functional Analysis with applications 517.5 (S) Blackwells Amazon

(A = essential, B = recommended, C = background.)

Most books on reading lists should also be available from the Blackwells shop at Jessop West.

## Timetable

 Mon 09:00 - 09:50 lecture Hicks Lecture Theatre 4 Tue 14:00 - 14:50 lecture Hicks Lecture Theatre 10