General purpose optimization library (Gpol): A flexible and efficient multi-purpose optimization library in python

Illya Bakurov, Marco Buzzelli, Mauro Castelli, Leonardo Vanneschi, Raimondo Schettini

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
37 Downloads (Pure)


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).

Original languageEnglish
Article number4774
Pages (from-to)1-34
Number of pages34
JournalApplied Sciences
Issue number11
Publication statusPublished - 1 Jun 2021


  • Combinatorial optimization
  • Continuous optimization
  • Evolutionary computation
  • Inductive programming
  • Local search
  • Optimization
  • Supervised machine learning
  • Swarm intelligence


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