09-01, 11:25–11:40 (Europe/Zurich), HS 118
Mathematical optimization is the selection of the best alternative with respect to some criterion, among a set of candidate options.
There are multiple applications of mathematical optimization. For example, in investment portfolio optimization, we search for the best way to invest capital given different alternatives. In this case, an optimization problem will allow us to choose a portfolio that minimizes risk (or maximizes profit), among all possible allocations that meet the defined requirements.
In most cases, mathematical optimization is used as a tool to facilitate decision-making. Sometimes these decisions can be made automatically in real-time.
This talk will explore how to formulate and solve mathematical optimization problems with Python, using different optimization libraries.
Mathematical optimization is an important tool in decision-making. With it, it is possible to optimize the economic benefit, time, distance, or any desired variable.
The first step in optimization is the construction of a model. A good choice of model is essential. If the model is too simple, it will not provide useful information about the problem. If it is too complex, it may be too difficult to solve. After the model has been created, it is possible to solve the problem, usually with the help of a computer.
It is important to note that there is no universal optimization algorithm; rather, there are different algorithms that are adapted to different optimization problems. The correct choice of the right algorithm for a specific application usually rests with the user. This choice is important, as it can determine whether the problem is solved quickly or slowly and, indeed, whether the solution is found.
In this talk we will learn how to solve mathematical optimization problems, using Python and different optimization libraries.
Machine Learning, StatisticsExpected audience expertise: Python –
nonePublic link to supporting material – Abstract as a tweet –
In this talk you will learn how to formulate and solve mathematical optimization problems with Python, using different optimization libraries.
Pamela Bustamante is a PhD candidate in Engineering Sciences from Pontificia Universidad Católica de Chile. Previously she did a Master's Degree in Industrial Engineering, and studied Industrial Civil Engineering.
She has been using Python since the start of her doctoral studies. Because of her love for the Python language, she participates as a co-organizer (and co-founder) of the groups "Python Chile" and "PyLadies Santiago de Chile" in Chile.
Currently, she is doing an internship in the context of her PhD in Lille, France. This talk is part of her Ph.D. Thesis in Engineering Sciences at the Pontificia Universidad Católica de Chile.