Module manager: Dr Vladimir Kisil
Email: V.Kisil@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2024/25
MATH2016, or MATH2017, or equivalent.
| MATH3210 | Metric Spaces |
| MATH5211M | Metric Spaces and Functional Analysis |
MATH3210
This module is not approved as an Elective
The notion of a metric space is a fundamental and extremely important one in mathematics. A metric space is a space with a notion of a distance between points defined on it. This includes for example subsets of real or complex Euclidean space, but also spaces of functions or more general sets can be made into metric spaces. The notion of a distance allows one to talk about convergence and completeness of a space. It also makes it possible to talk about continuity for functions in a more general context. For example, to view the space of continuous functions on an interval of the real line as a metric space turns out to be surprisingly useful. To illustrate this, we will use a simple result on metric spaces to establish one of the major theorems in the theory of ordinary differential equations. We will also derive and discuss two major theorems in real Analysis: the inverse and implicit function theorems. This module develops the theory of metric spaces with focus on applications in real Analysis.
On completion of this module, students should be able to:
- Verify the axioms of a metric space for a range of examples and identify open sets and closed sets
- Handle convergent sequences and continuous functions in an abstract context and apply them to specific function spaces
- Use the contraction mapping theorem to find approximate solutions of equations and differential equations
- Rewrite equations such as inverse functions as fixed point problems and solve them using the contraction mapping theorem
- Work with the notions of connectedness and compactness in abstract and concrete contexts
- Demonstrate a broad understanding of the concepts, information, practical competencies and techniques of the theory of metric spaces.
- Appreciate the coherence, logical structure and broad applicability of the theory of complete metric spaces.
- Use metric spaces and fixed point theory to initiate and undertake problem solving.
(1) Definition and fundamental properties of a metric space. Open sets, closed sets, closure and interior. Convergence of sequences. Continuity of mappings.
(2) Cauchy sequences, completeness of R with the standard metric; uniform convergence and completeness of C[a,b] with the uniform metric, convergence in Ck[a,b].
(3) Completeness of Rn with the standard metric. For a compact set K in Rn uniform convergence and completeness of C(K) with the uniform metric, convergence in Ck(K).
(4) The contraction mapping theorem, with applications: the Picard-Lindelöf theorem, the inverse and implicit function theorems in higher dimension.
(5) Connectedness and path-connectedness. Introduction to compactness and sequential compactness, including subsets of Rn.
(6) Banach spaces, the Baire category theorem and applications to function spaces
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 33 | 1 | 33 |
| Private study hours | 117 | ||
| Total Contact hours | 33 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Studying and revising of course material. Completing of assignments and assessments.
Regular exercise sheets
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 2.0 Hrs 30 Mins | 100 |
| Total percentage (Assessment Exams) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
There is no reading list for this module
Last updated: 29/04/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team