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

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

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).

• Jezik: Angleški jezik
• Ključne besede: optimization, evolutionary computation, swarm intelligence, local search, continuous optimization, combinatorial optimization, inductive programming, supervised machine learning
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: 34 str.
• Številčenje: Vol. 11, iss. 11, art. 4774
• PID: 20.500.12556/RUL-127465
• UDK: 004:78
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app11114774
• COBISS.SI-ID: 64772611
• Datum objave v RUL: 09.06.2021

BAKUROV, Illya, BUZZELLI, Marco, CASTELLI, Mauro, VANNESCHI, Leonardo in SCHETTINI, Raimondo, 2021, General Purpose Optimization Library (GPOL) : a flexible and efficient multi-purpose optimization library in Python. Applied sciences [na spletu]. 2021. Vol. 11, no. 11,  4774. [Dostopano 2 april 2025]. DOI 10.3390/app11114774. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=127465

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 23.05.2021