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.
Mathematical optimisation is a powerful framework in which one seeks to find variable values within a domain that maximise (or minimise) the value of a given function. Using the analogy that variables represent decisions or parameters to be defined 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 student will learn the basic optimisation theory, how to formulate problems and how they can be solved. Linear, integer, and nonlinear optimisation will be covered in the course. At the end of this course, it is expected that the student will be capable of analysing the main characteristics of an optimisation problem and decide what is the most suitable method to be employed for its solution.
Practical matters
Lecturer: Fabricio Oliveira
Head Assistant: Ellie Dillon
- Teaching: Lectures (24h) and exercise sessions (24h)
- Assessment method: Exam (100%)
- Grading scale: 0-5
- Study material: Lecture slides and exercises. Books: (main Operations Research: An Introduction by Hamdy Taha, Pearsons 8th edition or above, and secondary Operations Research: Applications and Algorithms by Wayne L. Winston).
- Language of instruction: English
Lecture and exercise plan File PDF document