Optimisation for Artificial Intelligence, a 4-day course
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In this section, we will go through a particular class of problems where functions are differentiable.
This topic is often referred to as Non-Linear Programming, in contrast with Linear Programming which we will study in a further section.
Topics | |
---|---|
2.1 | Example problems and modelling |
2.2 | A walk through gradient methods (notebook) |
2.3 | Solve the city placement problem (notebook) |
Notebook sessions can be run on:
your own computer: there are no specific requirements besides the numpy
, matplotlib
and scipy
libraries.
the computer in the classroom. Activate the environment before running Jupyter lab:
# The following commands only work in the classroom
module load python/3.7
source activate ~x.olive/students
Google Colab is a good fallback option, but data files must be uploaded, and solutions to exercices must be checked separately from the Github folder.