# MATRIX ANALYSIS - 2020/1

Module code: MAT3045

Module Overview

This module continues to develop the theory of matrices initiated in MAT1034 Linear Algebra, and introduces concepts of convergence and distance to the study of matrices. The module then progresses through several advanced topics in linear algebra such as the Perron-Frobenius theorem, tensors and exterior powers of matrices, and singular value decompositions. As an application we will investigate the PageRank algorithm used by Google to rank web pages.

Module provider

Mathematics

Module Leader

MORRIS Ian (Maths)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

JACs code: G100

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

Linear Algebra, Real Analysis 2.

Module content

Indicative content includes:

The Jordan normal form

Norms; equivalence of norms in finite dimensions; convergence of sequences and series of matrices; operator norms

The singular value decomposition

The spectral radius formula and Yamamoto's theorem

Positivity and the Perron-Frobenius theorem

The PageRank algorithm

Tensors and exterior powers of matrices

Assessment pattern

Assessment type | Unit of assessment | Weighting |
---|---|---|

Examination | EXAMINATION | 80 |

School-timetabled exam/test | CLASS TEST (50 MINS) | 20 |

Alternative Assessment

N/A

Assessment Strategy

The __assessment strategy__ is designed to provide students with the opportunity to demonstrate:

Understanding of and ability to interpret and manipulate mathematical statements.

Subject knowledge through the recall of key definitions, theorems and their proofs.

Analytical ability through the solution of unseen problems in the test and exam.

Thus, the

__summative assessment__for this module consists of:

One two hour examination at the end of Semester 2; worth 80% module mark.

One 50 minute class test; worth 20% module mark

__Formative assessment and feedback__

Students receive written feedback via a number of marked coursework assignments over an 11 week period. In addition, verbal feedback is provided by lecturer at biweekly tutorial lectures.

Module aims

- Extend and deepen students' understanding of linear maps on finite-dimensional spaces
- Familiarise students with important matrix decompositions, such as the Jordan form and singular value decomposition
- Familiarise students with core topics in the analysis of matrices such as convergence, norms and the Perron-Frobenius theorem
- Introduce students to tensor and exterior algebra of matrices

Learning outcomes

Attributes Developed | ||
---|---|---|

001 | Rigorously interpret convergence and infinite summation of matrices, including matrix exponentials | KC |

002 | Define and calculate singular values, Kronecker products and exterior powers, at least in simple cases | KC |

003 | Understand the interaction between the norm/singular values of a matrix or matrices and the tensor / exterior product | KC |

004 | Understand the significance of positivity and the Perron-Frobenius theorem. | |

005 | Define the determinant using exterior algebra and derive its essential properties | KC |

Attributes Developed

**C** - Cognitive/analytical

**K** - Subject knowledge

**T** - Transferable skills

**P** - Professional/Practical skills

Overall student workload

Independent Study Hours: 117

Lecture Hours: 33

Methods of Teaching / Learning

The __learning and teaching__ strategy is designed to provide:

An introduction to analytic methods in linear algebra and their applications.

The

__learning and teaching__methods include:

3 x 1 hour lectures per week x 11 weeks, with additional notes on white board to supplement the module handbook and Q + A opportunities for students.

(every second week) 1 x 1 hour tutorial replaces one of the lectures for guided discussion of solutions to problem sheets provided to and worked on by students during the tutorial.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Programmes this module appears in

Programme | Semester | Classification | Qualifying conditions |
---|---|---|---|

Mathematics MSc | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Mathematics with Statistics BSc (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Mathematics BSc (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Mathematics MMath | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.