MA427 Half Unit
Mathematical Optimisation
This information is for the 2021/22 session.
Teacher responsible
Dr Giacomo Zambelli
Availability
This course is available on the Global MSc in Management, Global MSc in Management (CEMS MiM), Global MSc in Management (MBA Exchange), MSc in Applicable Mathematics, MSc in Operations Research & Analytics, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available as an outside option to students on other programmes where regulations permit.
Pre-requisites
Students must have sufficient knowledge of linear algebra (linear independence, determinants, matrix inversion and manipulation) and of basic multivariate calculus (derivatives and gradients).
Course content
Introduction to the theory and solution methods of linear and nonlinear programming problems, including: linear programming duality, Lagrangian duality, convex programming and Karush-Kuhn-Tucker conditions, algorithms for linear and convex optimisation problems, theory of good formulations for integer linear programming models, integer linear programming methods (branch and bound and cutting planes).
Teaching
This course is delivered through a combination of classes and lectures totalling a minimum of 30 hours across Lent Term. This year, some or all of this teaching will be delivered through a combination of virtual classes and lectures delivered as online videos.
Formative coursework
Weekly exercises will be given that will be solved and discussed during the seminars. Three of those exercises will be handed in as formative coursework and the students will be given feedback on their submissions.
Indicative reading
Extensive lecture notes covering all parts of the course will be provided. Students interested in further readings can look at the books below.
- D Bertsimas and J N Tsitsiklis, Introduction to Linear Optimization (1997)
- S Boyd and L Vandenberghe, Convex Optimization (2004)
- M Conforti, G Cornuejols, G Zambelli, Integer Programming (2014)
Assessment
Exam (100%, duration: 3 hours) in the summer exam period.
Course selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Important information in response to COVID-19
Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Key facts
Department: Mathematics
Total students 2020/21: 32
Average class size 2020/21: 17
Controlled access 2020/21: Yes
Value: Half Unit
Personal development skills
- Problem solving
- Application of numeracy skills
- Specialist skills
