noloadj

NoLOAD_Jax: Non Linear Optimization by Automatic Differentiation using Jax

We are happy that you will use or develop the NoLOAD_Jax. It is an Open Source project located on GitLab at https://gricad-gitlab.univ-grenoble-alpes.fr/design_optimization/NoLoad_v2 It aims at solving constrained optimization problem for the design of engineering systems

Project Presentation

NoLOAD_Jax: Please have a look to NoLOAD presentation : https://noload-jax.readthedocs.io/en/latest/

A scientific article presenting NoLOAD is available here:

Agobert Lucas, Hodencq Sacha, Delinchant Benoit, Gerbaud Laurent, Frederic Wurtz, “NoLOAD, Open Software for Optimal Design and Operation using Automatic Differentiation”, OIPE 2020, Poland, 09-2021. https://hal.archives-ouvertes.fr/hal-03352443

Please cite us when you use NoLOAD.

NoLOAD_Jax Community

Please use the git issues system to report an error: https://gricad-gitlab.univ-grenoble-alpes.fr/design_optimization/NoLoad_v2 Otherwise you can also contact the developer team using the following email adress: [e-mail]

Installation Help

You can install the library as a user or as a developer. Please follow the corresponding installation steps below.

Prerequisite

Please install Python 3.10 or later https://www.python.org/downloads/

Installation as a user

Please install NoLOAD_Jax with pip using the command prompt.

If you are admin on Windows or working on a virtual environment

pip install noloadj

If you want a local installation or you are not admin

pip install --user noloadj

If you are admin on Linux:

sudo pip install noloadj

Launch the examples to understand how the NoLOAD_Jax works:

python noloadj/01-UnconstrainedMonoObjective.py
python noloadj/02-ConstrainedMonoObjective.py
python noloadj/03-ConstrainedMultiObjective.py
python noloadj/04-ConstrainedMonoObjective2.py

Enjoy your time using NoLOAD_Jax !

GPU

As it uses the JAX library, NoLOAD_Jax can run on CPU (Central Processor Unit) or GPU (Graphics Processor Unit), where GPU offers better performances than CPU. On Windows, only CPU can be used. To use GPU you may run NoLOAD on Ubuntu. If you want to use GPU, you need to install CUDA and CuDNN on your computer then write on Pycharm terminal :

pip install --upgrade pip
pip install -U "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

If you use GPU, you need to put these lines at the beginning of your "optimization" file to avoid memory issues :

import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'
os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION']='0.50'

To have more information, please have a look to : https://jax.readthedocs.io/en/latest/installation.html

IPOPT Algorithm

NoLOAD_Jax runs with SLSQP optimization algorithm from Scipy. To install IPOPT algorithm, please install an Anaconda environment and run this command on a terminal :

conda install -c conda-forge cyipopt

Library Installation Requirements

Matplotlib >= 3.0 Scipy >= 1.2 Jax >= 0.4.18 Jaxlib >= 0.4.18 Pandas >= 1.3.5 tk >= 0.1.0 openpyxl >= 3.1.2

Main Authors:

B. DELINCHANT, L. GERBAUD, F. WURTZ, L. AGOBERT

Partners:

Vesta-System: http://vesta-system.fr/

Acknowledgments:

Licence

This code is under the Apache License, Version 2.0