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MATH40550

Academic Year 2024/2025

Applied Matrix Theory (MATH40550)

Subject:
Mathematics
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Helena Smigoc
Trimester:
Autumn
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module is designed to develop an understanding of selected topics from matrix theory, that are particularly relevant in applications. The students are introduced to matrix theoretical concepts such as matrix norms, singular values, and various matrix factorisations. They learn about different classes of matrices such as symmetric, orthogonal, positive semidefinite, sparse, and how different matrix properties can be exploited in applications.

About this Module

Learning Outcomes:

On completion of this course, the students should have a firm grasp of various matrix theoretical methods and matrix properties. They should be able to perform calculations for small matrices on paper and for large matrices with the aid of a computer. They should be able to understand how different concepts are derived (proofs of theorems), and how they are relevant in applications.

Indicative Module Content:

Provisional Module Structure

1 - Basic Concepts: Eigenvalues, Eigenvectors, and Similarity

2 - Norms for Vectors and Matrices

3- Schur Factorisation and Singular Value Decomposition

4 -Conditioning and perturbation theorems

5- Positive Definite Matrices

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

24
Autonomous Student Learning

46
Lectures

18
Tutorial

12
Total

100

Approaches to Teaching and Learning:
Lectures,
Flipped classroom (some lecture hours might be transformed in Q&A sessions)
Enquiry and problem-based learning.

Requirements, Exclusions and Recommendations
Learning Requirements:

The students are expected to have an intermediate level of linear algebra. They should have a good grasp of linear algebraic concepts such as vector space, linear independence and basis. They should be competent in solving linear systems, and preforming matrix operations such as computing determinants, inverses and eigenvalues.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): End of semester exam. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
70
 No
Assignment(Including Essay): Homework assignments during the semester. Week 3, Week 6, Week 9, Week 12 Graded No
30
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Björn Johannesson Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Mon 12:00 - 12:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Mon 13:00 - 13:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 09:00 - 09:50
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