Data from our research
Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca, doi.org/10.5281/zenodo.10017717, 2023
International competitions of constraint solvers help us improving our knowledge about components (e.g., filtering algorithms, search heuristics, exploration strategies, encoding/reformulation techniques, learning approaches, …) that are behind the efficiency of solving systems for combinatorial constrained problems.
Gökhan Tahıl, Fabien Delorme, Daniel Le Berre, Éric Monflier, Adlane Sayede, Sébastien Tilloy, doi.org/10.5281/zenodo.7575579, 2023
Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices.
Gökhan Tahıl, Fabien Delorme, Daniel Le Berre, Éric Monflier, Adlane Sayede, Sébastien Tilloy, doi.org/10.5281/zenodo.7575539, 2023
Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices.
Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca, doi.org/10.5281/zenodo.7575992, 2023
International competitions of constraint solvers help us improving our knowledge about components (e.g., filtering algorithms, search heuristics, exploration strategies, encoding/reformulation techniques, learning approaches, …) that are behind the efficiency of solving systems for combinatorial constrained problems.
Srdjan Vesic, Predrag Teovanovic, Bruno Yun, 2022
In this project, we study whether humans comply with the principles from the area of computational argumentation.
Daniel Le Berre, Romain Wallon, doi.org/10.5281/zenodo.4751685, 2021
Current implementations of pseudo-Boolean (PB) solvers working on native PB constraints are based on the CDCL architecture which empowers highly efficient modern SAT solvers. In particular, such PB solvers not only implement a (cutting-planes-based) conflict analysis procedure, but also complementary strategies for components that are crucial for the efficiency of CDCL, namely branching heuristics, learned constraint deletion and restarts. However, these strategies are mostly reused by PB solvers without considering the particular form of the PB constraints they deal with.