دورية أكاديمية

General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python

التفاصيل البيبلوغرافية
العنوان: General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python
المؤلفون: Illya Bakurov, Marco Buzzelli, Mauro Castelli, Leonardo Vanneschi, Raimondo Schettini
المصدر: Applied Sciences; Volume 11; Issue 11; Pages: 4774
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2021
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: optimization, evolutionary computation, swarm intelligence, local search, continuous optimization, combinatorial optimization, inductive programming, supervised machine learning
جغرافية الموضوع: agris
الوصف: 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).
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
العلاقة: Computing and Artificial Intelligence; https://dx.doi.org/10.3390/app11114774Test
DOI: 10.3390/app11114774
الإتاحة: https://doi.org/10.3390/app11114774Test
حقوق: https://creativecommons.org/licenses/by/4.0Test/
رقم الانضمام: edsbas.C246FC34
قاعدة البيانات: BASE