- HAL publications
GAMBLE Research team
Geometric Algorithms and Models Beyond the Linear and Euclidean realm
- Leader : Olivier Devillers
- Type : team
- Research center(s) : Nancy
- Field : Algorithmics, Programming, Software and Architecture
- Theme : Algorithmics, Computer Algebra and Cryptology
- Inria teams are typically groups of researchers working on the definition of a common project, and objectives, with the goal to arrive at the creation of a project-team. Such project-teams may include other partners (universities or research institutions)
Classical computational geometry usually deals with linear objects in a Euclidean setting and when other situations happen, curved objects are typically linearized and non-Euclidean spaces are locally approximated by Euclidean spaces. The goals of the Gamble team are to address such limitations of classical computational geometry.
Non-linear computational geometry. Curved objects are ubiquitous in the world we live in. However, despite this ubiquity and decades of research in several communities, curved objects are far from being robustly and efficiently manipulated by geometric algorithms. Our work on, for instance, quadric intersections and certified drawing of plane curves has proven that dramatic improvements can be accomplished when the right mathematics and computer science are put into motion. In this direction, many problems are fundamental and solutions have potential industrial impact in Computer Aided Design and Robotics for instance. Intersecting NURBS and meshing singular surfaces in a certified manner are important examples of such problems.
Non-Euclidean computational geometry. Triangulations are central geometric data structures in many areas of science and engineering. Traditionally, their study has been limited to the Euclidean setting. Needs for triangulations in non-Euclidean settings have emerged in many areas dealing with objects whose sizes range from the nuclear to the astrophysical scale, and both in academia and in industry. It has become timely to extend the traditional focus on Rd of computational geometry and encompass non-Euclidean spaces.
Probability in computational geometry. The design of efficient algorithms is driven by the analysis of their complexity. Traditionally, worst-case input and sometimes uniform distributions are considered and many results in these settings have had a great influence on the domain. Nowadays, it is necessary to be more subtle and to prove new results in between these two extreme settings. For instance, smoothed analysis, which was introduced for the simplex algorithm and which we applied successfully to convex hulls, proves that such promising alternatives exist.
Research teams of the same theme :
- ARIC - Arithmetic and Computing
- AROMATH -
- CARAMBA - Cryptology, arithmetic : algebraic methods for better algorithms
- CASCADE - Construction and Analysis of Systems for Confidentiality and Authenticity of Data and Entities
- DATASHAPE - Understanding the Shape of Data
- GRACE - Geometry, arithmetic, algorithms, codes and encryption
- LFANT - Lithe and fast algorithmic number theory
- OURAGAN - OUtils de Résolution Algébriques pour la Géométrie et ses ApplicatioNs
- POLSYS - Polynomial Systems
- SECRET - Security, Cryptology and Transmissions
- SPECFUN - Symbolic Special Functions : Fast and Certified