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Curricular information is subject to change
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 Type | Hours |
---|---|
Lectures | 18 |
Tutorial | 12 |
Specified Learning Activities | 24 |
Autonomous Student Learning | 46 |
Total | 100 |
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.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Not yet recorded. |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 2 Hour |
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Mr Andrew Fulcher | Tutor |
Priyanka Joshi | Tutor |
Mr Piotr Kedziora | Tutor |
Koyel Majumdar | Tutor |