BSc Computer Science and Mathematics / Course details
Year of entry: 2022
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Course unit details:
|Unit level||Level 3|
|Teaching period(s)||Semester 1|
|Offered by||Department of Mathematics|
|Available as a free choice unit?||No|
This course unit is an introduction to matrix analysis, covering both classical and more recent results that are useful in applying matrix algebra to practical problems. In particular it treats eigenvalues and singular values, matrix factorizations, function of matrices, and structured matrices. It builds on the first year linear algebra course. Apart from being used in many areas of mathematics, Matrix Analysis has broad applications in fields such as engineering, physics, statistics, econometrics and in modern application areas such as data mining and pattern recognition. Examples from some of these areas will be used to illustrate and motivate some of the theorems developed in the course.
|Unit title||Unit code||Requirement type||Description|
|Linear Algebra A||MATH10202||Pre-Requisite||Compulsory|
|Linear Algebra B||MATH10212||Pre-Requisite||Compulsory|
Students must have taken MATH10202 or MATH10212 (Linar Algebra). Familiarity with Matlab is helpful but not essential.
To introduce students to matrix analysis through the development of essential tools such as the Jordan canonical form, Perron-Frobenius theory, the singular value decomposition, and matrix functions.
On successful completion of this course unit students will be able to:
- construct some key matrix decompositions such as the Jordan canonical form of a square matrix and the singular value decomposition of a rectangular matrix,
- localize the eigenvalues of matrix using matrix norms and Gershgorin's theorem,
- check the solvability of a linear system and when solvable, provide the set of solutions in terms of the pseudoinverse or the singular value decomposition of the matrix of the linear system,
- solve least squares problems and construct low-rank approximations to a matrix,
- compute the matrix exponential and use it for solving differential and algebraic equations,
- apply Perron's theorem to positive matrices and the Perron-Frobenius theorem to nonnegative matrices.
- Basics: Summary/recap of basic concepts from linear algebra including matrices and vectors, determinants, singularity of matrices, rank. [2 lectures]
- Theory of eigensystems: Eigenvalues, eigenvectors, and invariant subspaces; reduction of square matrices to simpler form including the Schur decomposition, spectral decomposition for normal matrices and the Jordan canonical decomposition; minimal and characteristic polynomials, Cayley-Hamilton Theorem; Sylvester's inertia theorem. 
- Norms: Vector norms and matrix norms, bounds for eigenvalues, Gershgorin theorem. 
- Singular value decomposition (SVD): Projectors; pseudo-inverses; application to linear least squares; polar decomposition. 
- Nonnegative matrices and related results: Irreducible matrices; Perron-Frobenius theorem; diagonally dominant matrices. 
- Matrix functions: Definitions; the matrix exponential function and application to the solution of differential equations and higher order equations; difference equations and matrix powers. 
- Kronecker product. Definition, properties and application to the solution of Sylvester's equation (if time). 
- Mid-semester test: weighting 20%
- End of semester examination: weighting 80%
Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding. Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback. Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.
- Roger A. Horn and Charles R. Johnson. Matrix Analysis. Cambridge University Press, 1985.
- Peter Lancaster and Miron Tismenetsky. The Theory of Matrices. Academic Press, London, second edition, 1985.
- Alan J. Laub. Matrix Analysis for Scientists and Engineers. SIAM, Philadelphia, PA, 2005.
- Carl D. Meyer. Matrix Analysis and Applied Linear Algebra. SIAM, Philadelphia, PA, 2000.
- James M. Ortega. Matrix Theory: A Second Course. Plenum Press, New York, 1987.
|Scheduled activity hours|
|Independent study hours|
|Francoise Tisseur||Unit coordinator|
The independent study hours will normally comprise the following. During each week of the taught part of the semester:
• You will normally have approximately 60-75 minutes of video content. Normally you would spend approximately 2-2.5 hrs per week studying this content independently
• You will normally have exercise or problem sheets, on which you might spend approximately 1.5hrs per week
• There may be other tasks assigned to you on Blackboard, for example short quizzes or short-answer formative exercises
• In some weeks you may be preparing coursework or revising for mid-semester tests
Together with the timetabled classes, you should be spending approximately 6 hours per week on this course unit.
The remaining independent study time comprises revision for and taking the end-of-semester assessment.
The above times are indicative only and may vary depending on the week and the course unit. More information can be found on the course unit’s Blackboard page.