General Purpose Optimization Library (GPOL) : a flexible and efficient multi-purpose optimization library in Python

Bakurov, Illya (author), Buzzelli, Marco (author), Castelli, Mauro (author), Vanneschi, Leonardo (author), Schettini, Raimondo (author)

Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).

• Language: English
• Keywords: optimization, evolutionary computation, swarm intelligence, local search, continuous optimization, combinatorial optimization, inductive programming, supervised machine learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: 34 str.
• Numbering: Vol. 11, iss. 11, art. 4774
• PID: 20.500.12556/RUL-127465
• UDC: 004:78
• ISSN on article: 2076-3417
• DOI: 10.3390/app11114774
• COBISS.SI-ID: 64772611
• Publication date in RUL: 09.06.2021

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 23.05.2021