MAS331 Metric Spaces
|Semester 1, 2017/18||10 Credits|
|Lecturer:||Prof David Applebaum||Home page||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:
- Metric spaces: definition, properties and examples
- Convergence of sequences
- Open and closed subsets
- Cauchy sequences, completeness
- Iteration and the Contraction Mapping Theorem
- 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
- 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.
Lectures, problem solving
20 lectures, no tutorials
One formal 2.5 hour written examination. You should do 4 questions out of 5.
1. Metric spaces
(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 Heine–Borel property.
|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||Kreyszig||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.