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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2021

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app11114774