Abstract #

Optimisation problems are frequently encountered in science and industry. Given a real-valued function f defined on a set called the search space X, optimising the function f consists of finding a point x ∈ X that has the optimal value f (x), or at least constructing a sequence (xt) t∈ N∈ XN that is close to the optimum. Depending on the search space X, optimisation problems can be globally classified as discrete problems (eg X= 10, 1ln) or as continuous problems (eg X= Rn). Tools for modelling and solving discrete (Soni, 2017) and continuous (Agarwal et al., 2020) problems are proposed in the literature. In this paper, Macop for Minimalist And Customisable Optimisation Package, is proposed as a discrete optimisation Python package which doesn’t implement every algorithm in the literature, but provides the ability to quickly develop and test your own algorithm and strategies. The main objective of this package is to provide maximum flexibility, which allows easy implementation when experimenting new algorithms.

Method #

• DOI: https://doi.org/10.21105/joss.02812

Citation #

@article{buisine2021minimalist,
  title={Minimalist And Customisable Optimisation Package},
  author={Buisine, J{\'e}r{\^o}me and Delepoulle, Samuel and Renaud, Christophe},
  journal={Journal of Open Source Software},
  volume={6},
  number={59},
  pages={2812},
  year={2021}
}