Tutorial - SciPy 2022

Practical Numerical Optimization with SciPy, Estimagic and JAXopt

by Janos Gabler & Tim Mensinger

Contents

1. Installation
2. Slides
3. Exercises
4. Troubleshooting

Warning Please pull the repo and update your conda environment before the tutorial to make sure that the most recent versions are installed.

Installation

1. Install miniconda

2. Clone the repository

   $ git clone https://github.com/OpenSourceEconomics/scipy-estimagic.git
   $ cd scipy-estimagic

3. Install and activate the environment

   $ conda env create -f environment.yml
   $ conda activate scipy-estimagic

   Note You have to repeat the activation step each time after closing your terminal.

4. Test your installation

   $ python test_installation.py

5. Update the environment

   Note This step is only necessary if you have installed the environment a long time ago and want to make sure that you're using the most recent versions.

   $ cd scipy-estimagic
   $ conda activate scipy-estimagic
   $ conda env update -f environment.yml
   

   or use a completely fresh install:

   $ cd scipy-estimagic
   $ conda deactivate scipy-estimagic
   $ conda env remove --name scipy-estimagic
   $ conda env create -f environment.yml

Slides

You can download the slides by clicking here, or you can view them directly on GitHub here.

Exercises

You find the exercise notebooks in the folder exercises, and the corresponding solutions in the subfolder exercises/solutions.

Troubleshooting

If you have questions, problems with the installation or any other part of the repository, please open an issue.