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    Why this course?

    Mathematical optimisation is one of the cornerstones of fields such as Machine Learning, Artificial Intelligence, and Operations Research. Most decision support methods have, at some level, a mathematical optimisation method at its core, and it is precisely these methods that we will learn in this course.

    Linear Optimization is a powerful framework in which one seeks to represent systems by means of linear objective functions and constraints. Using the analogy that variables represent decisions or parameters to be defined, constraints represent rules that a valid configuration or a plan of action for the system, and the function is a measure of performance, one can use that framework to support decision-making in a wide range of applications, from planning industrial chemical plants to training models that learn from data. 

    In this course, the students will learn the basic linear optimisation theory as well as advanced algorithms available and how they can be applied to solve challenging real-world inspired optimisation problems. Throughout the course, we will also look into practical and research applications of linear optimisation. 

    Practical matters

    Lecturer: Fabricio Oliveira 
    Teaching Assistants: Olli Herrala (head), Eljas Toepfer, Tran Thang

    Teaching method

    • Lectures (2h per week) - Thursdays 14.15h-16.00h
    • Exercises sessions (2h per week)
      • Tuesday 12.15h-14.00h (H1)
      • Monday 10.15h-12.00h (H2)
      • Monday 16.15h-18.00h (H3)
      • Wednesday 16.15h-18.00h (H4) -> if you choose this session, you must have your own computer.


    Lecture notes and forum for content questions:
    https://github.com/gamma-opt/optimisation-notes


    "Administratrivia"


    If you have feedback at any point during the course, we have set up a virtual feedback box in Presemo (link). The feedback you submit there is anonymous and invisible to everyone except the lecturer and the head TA.