This repo contains codes and documentations for the "Global Optimization" course at University of Florence.
The projects has been conducted together with @fedem96.
The report file (in Italian) can be found here.
The aim of this project is to compare two algorithms for hyperparameters global optimization, one based on Radial Basis Functions and the other based on Bayesian gaussian processes.
- download Bonmin, Ipopt and other binaries: from
https://ampl.com/dl/open/download the binaries for your operating system - extract the binaries, and add extracted folder path to your
$PATHenvironment variable - install pytorch
- install these Python 3 packages too: rbfopt, bayesian-optimization, tensorboardX
- download the project:
git clone https://github.com/emanuelevivoli/Hyperparameters_Optimization.git - enter in the project directory:
cd Hyperparameters_Optimization - (modify and) run evaluation.py:
python3 evaluate.py
If you are interested and have some questions, don't hesitate to contact us or open an issue.