BSc Actuarial Science and Mathematics
Year of entry: 2022
- View tabs
- View full page
Course unit details:
|Unit level||Level 3|
|Teaching period(s)||Semester 2|
|Offered by||Department of Mathematics|
|Available as a free choice unit?||No|
Optimization is the art of optimal decision making under constraints. Convex optimization refers to a set of problems and methods that can be formulated using convex functions and sets; countless problems from science, engineering and statistics can be cast as convex optimization problems and solved using efficient algorithms. The course is intended as an introduction to convex optimization, focussing on the theory, the modelling techniques, and the algorithm analysis and design. Recent developments such as convex regularization and compressed sensing will be discussed. The problem sessions will be used to present applications from machine learning, signal processing, and finance.
|Unit title||Unit code||Requirement type||Description|
|Linear Algebra A||MATH10202||Pre-Requisite||Compulsory|
|Linear Algebra B||MATH10212||Pre-Requisite||Compulsory|
The course aims to introduce students to modern convex optimization and its applications in fields such as machine learning. The course is designed to cover practical modelling aspects, algorithm analysis and design, and the theoretical foundations of the subject.
The lectures and problem sessions will cover the following topics:
(1) Overview and examples of optimization problems;
(2) Least-squares, gradient descent, Newton’s method;
(3) Introduction to CVX;
(4) Fundamentals of convex analysis and geometry: convex sets and functions, subdifferential calculus;
(5) Linear and quadratic programming, semidefinite programming, conic optimization;
(6) Optimality conditions, duality theory, theorems of alternative;
(7) Interior-point methods, augmented Lagrangians, alternating direction method of multipliers;
(8) Applications in machine learning: convex regularization, compressed sensing and matrix completion.
Coursework: Weighting within unit 20%.
End of semester examination: Weighting within unit 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.
The main reference is the book
- Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
The book is available online at http://www.stanford.edu/~boyd/cvxbook/. The lecture will also make use of the CVX software, which is based on MATLAB.
Other useful references include:
- J. Nocedal and S.J.Wright. Numerical Optimization. Springer, 2006.
- A. Ben-Tal and A. Nemirovski. Lectures on Modern Convex Optimization. 2013.
CONVEX OPTIMIZATION 3
- Y. Nesterov. Introductory lectures on convex optimization: A basic course. Springer, 2004.
|Scheduled activity hours|
|Independent study hours|
|Matthew Thorpe||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.