Topic outline

  • MS-E2122 - NonLinear Optimization - Fall 2023

    Why take this course in NonLinear Optimization?

    Mathematical optimization 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 optimization method at its core, and it is precisely these methods that we will learn in this course.

    Mathematical optimization (nonlinear optimization, in its most general form) is a powerful framework in which one seeks to find variable values within a domain that maximize (or minimize) the value of a given function. Using the analogy that variables represent decisions or parameters to be defined and the function is a performance measure; one can use that framework to support decision-making in various applications, from planning industrial chemical plants to training models that learn from data. 

    In this course, the student will learn the basic optimization theory behind the main numerical algorithms available and how they can be applied to solve optimization problems. At the end of the course, it is expected that the student will be capable of analyzing the main characteristics of an optimization problem and deciding the most suitable method for its solution. 


    Learning Outcomes

    Upon completing this course, the student should be able to:

    • understand how several important problems arising from diverse fields can be cast and solved as nonlinear optimisation problems;
    • familiarise themselves with classical non-linear problems;
    • know the main techniques for modelling and solving nonlinear optimisation problems and how to apply them in practice;
    • know how to use optimisation software for implementing and solving nonlinear optimisation problems.


    Additional Materials

    Lecture notes, lecture slides and exercises.

    Book: Nonlinear Programming, Theory and Algorithms by Bazaraa, Sherali and Shetty.


    Grading

    The course is graded on a scale of 0-5 based on 4 homework assignments (50 %) and 2 projects (25% each). 


    Course Staff:

    Lecturer:  Fernando Dias (forename.surname@aalto.fi)

    Assistant: Topias Terho (forename.surname@aalto.fi)

    Reception hour:
    Lecturer:  Wednesdays at 12:00 - 13:00 in room Y214 (Otakaari 1). Please confirm the appointment by contacting via email first.  

    Zulip chat invitation link: https://ms-e2122.zulip.aalto.fi/join/3mvt6c55kaflno6zn5ujkco2/