Collection of various optimization methods (search for the global minimum and/or maximum) for multivariate functions and multidimensional data arrays (tensors). This library is based on a software product teneva. See also related benchmarks library teneva_bm.
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The package can be installed via pip (it requires the Python programming language of the version 3.8 or 3.9):
pip install teneva_opti==0.6.0
The package can be also downloaded from the repository teneva_opti and be installed by
python setup.py installcommand from the root folder of the project. -
We test optimizers with benchmarks from teneva_bm library. For installation of additional dependencies (
gym,mujoco, etc.), please, do the following (for existing conda environmentteneva_opti; if you are using a different environment name, then please make the appropriate substitution in the script; note that you don't need to use environment in colab):wget https://raw.githubusercontent.com/AIRI-Institute/teneva_bm/main/install_all.py && python install_all.py --env teneva_opti && rm install_all.py
In the case of problems with
scikit-learn, uninstall it aspip uninstall scikit-learnand then install it from the anaconda:conda install -c anaconda scikit-learn. If you have problems downloading the script via wget, you can download it manually from the root folder of the repository teneva_bm.
Please, run the demo script from the root of the teneva_opti repository:
clear && python demo/base.pySee also other demo scripts in the folder
demoof the teneva_opti repository.
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