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  • MS-E2122 - NonLinear Optimization - Fall 2024

    Welcome to Nonlinear Optimization - Fall/2024


    Mathematical optimization is one of the backbones of several important fields, such as Machine Learning, Artificial Intelligence, and Operations Research. Most decision support methods have a mathematical optimization method at their core, and we will study these methods carefully in this course.

    Mathematical optimization (nonlinear optimization, in its most prevailing form) is a vital framework that seeks to find the best variable values within a domain based on a given objective function respecting a set of pre-determined constraints. Using the analogy that variables represent decisions or parameters to be defined, constraints represent limits and restrictions, and the function is a performance measure and constraints, that framework can 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, the student is expected to 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 essential problems arising from diverse fields can be cast and solved as nonlinear optimization problems;
    • Familiarise themselves with classical nonlinear problems;
    • Know the main techniques for modelling and solving nonlinear optimization problems and how to apply them in practice;
    • Know how to use optimization software to implement and solve nonlinear optimization 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 the combined score of all 5 assignments.

    Course Staff:

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

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

    Reception hour:

    Lecturer: Wednesdays from 12:00 to 13:00 in room Y214 (Otakaari 1). Please confirm the appointment by emailing me first.  

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