The first version of “GPOL: General Purpose Optimization Library” has just been published.
In a joint effort between the Nova Information Management School (Universidade NOVA de Lisboa) and the Department of Informatics Systems and Communication (University of Milano – Bicocca), authors Illya Bakurov, Marco Buzzelli, Mauro Castelli, Leonardo Vanneschi and Raimondo Schettini have released a flexible and efficient multipurpose optimization Python library, that covers a wide range of stochastic iterative search algorithms.
Its flexible and modular implementation allows solving many different problem types, from the fields of continuous and combinatorial optimization, to supervised machine learning problem-solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on CPU or GPU.
Get the library at https://gitlab.com/ibakurov/general-purpose-optimization-library
Read the paper at https://www.mdpi.com/2076-3417/11/11/4774