The Sargent Centre is hosting a five-day Computational Optimisation Summer School in Python. The first two days will focus on Fundamentals and Industrial Applications, followed by 3 days of deeper dive into Methods and AlgorithmsIn this course, we will cover the various types of mathematical optimization problems, namely Linear Programming (LP), Mixed Integer Linear Programming (MILP), Non-Linear Programming, and Mixed Integer Non-Linear Programming.
This course will allow attendees to understand the formulation of decision variables, objective functions, constraints, and parameters required to develop optimization models and code and solve them in Python.The second part of this summer school will cover advanced and emerging computational optimization topics such as data-driven optimization, global optimization, optimization under uncertainty, optimization for machine learning, amongst others.