Topic outline

  • Course home page

    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), Helmi Hankimaa, Tuukka Mattlar.

    Teaching method

    • Recorded video lectures and exercises to be watched beforehand (approx 2h per week).
    • Workshop-like sessions (Q/A + live exercises and discussion; 2h per week) via Zoom (participation compensated).

    Lecture videos (new videos released every week. First video available from 10.01.2022)
    https://youtube.com/playlist?list=PLxSrejNn6xBDgW5fFrhig8n09veeuUBZr

    Join Zoom Meeting (you must log in with your Aalto account)
    https://aalto.zoom.us/j/64782548735?pwd=WXpVUHVVVU1mcFVCbHc3K1BJNnlVZz09

    You shouldn't need a meeting id or passcode if you use the above link, but here it is just in case:
    - Meeting Id: 647 8254 8735
    - Passcode: E2121.2022

    Zulip workspace invitation (used for communication with TAs and chatting during the Online sessions)

    https://ms-e2121.zulip.aalto.fi/join/wwmobmarlxubqng4mof4kglr/

    Question bank
    https://presemo.aalto.fi/linearopt2021

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

    "Administratrivia"

    • Assessment methods: homework assignments and participation in online sessions. 
    • Grading scale: 0-5
    • Study material: Lecture notes, slides and exercises. Main textbook (Introduction to Linear Optimization, D. Bertsimas and J. Tsitsiklis, 1997, Athena Scientific).
    • Language of instruction: English
    • Programming language: Julia http://www.julialang.org. Detailed information here.
    • Aalto CS Jupyter hub:https://jupyter.cs.aalto.fi. Detailed information here.