MD-JEEP: a New Release for Discretizable Distance Geometry Problems with Interval Data

Antonio Mucherino (1, 2) , Douglas Gonçalves (3) , Leo Liberti (4, 5, 6) , Jung-Hsin Lin (7) , Carlile Lavor (8) , Nelson Maculan (9)

1 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
2 Inria Rennes – Bretagne Atlantique
3 UFSC - Universidade Federal de Santa Catarina = Federal University of Santa Catarina [Florianópolis]
4 CNRS - Centre National de la Recherche Scientifique
5 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
6 OptimiX - Optimization at LIX
7 Academia Sinica
8 UNICAMP - Universidade Estadual de Campinas = University of Campinas
9 UFRJ - Universidade Federal do Rio de Janeiro [Brasil] = Federal University of Rio de Janeiro [Brazil] = Université fédérale de Rio de Janeiro [Brésil]

With the most recent releases of MD-JEEP, new relevant features have been included to our software tool. MD-JEEP solves instances of the class of Discretizable Distance Geometry Problems (DDGPs), which ask to find possible realizations, in a Euclidean space, of a simple weighted undirected graph for which distance constraints between vertices are given, and for which a discretization of the search space can be supplied. Since its version 0.3.0, MD-JEEP is able to deal with instances containing interval data. We focus in this short paper on the most recent release MD-JEEP 0.3.2: among the new implemented features, we will focus our attention on three features: (i) an improved procedure for the generation and update of the boxes used in the coarse-grained representation (necessary to deal with instances containing interval data); (ii) a new procedure for the selection of the so-called discretization vertices (necessary to perform the discretization of the search space); (iii) the implementation of a general parser which allows the user to easily load DDGP instances in a given specified format. The source code of MD-JEEP 0.3.2 is available on GitHub, where the reader can find all additional details about the implementation of such new features, as well as verify the effectiveness of such features by comparing MD-JEEP 0.3.2 with its previous releases.

Domaines: Recherche opérationnelle [math.OC]; Informatique [cs]

Antonio Mucherino, Douglas Gonçalves, Leo Liberti, Jung-Hsin Lin, Carlile Lavor, et al.. MD-JEEP: a New Release for Discretizable Distance Geometry Problems with Interval Data. FedCSIS 2020 - 15th Conference on Computer Science and Information Systems, Sep 2020, Sofia, Bulgaria. pp.1-6. ⟨hal-03030154⟩