Dr Giacomo Zambelli

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.

Students must have sufficient knowledge of linear algebra (linear independence, determinants, matrix inversion and manipulation) and of basic multivariate calculus (derivatives and gradients).

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).

20 hours of lectures and 15 hours of seminars in the LT. 2 hours of lectures in the ST.

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.

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)

Exam (100%, duration: 3 hours) in the summer exam period.

Department: Mathematics

Total students 2018/19: 41

Average class size 2018/19: 20

Controlled access 2018/19: No

Value: Half Unit