Section: Overall Objectives
Overall Objectives
Starting in the eighties, the emerging computational geometry community has put a lot of effort into designing and analyzing algorithms for geometric problems. The most commonly used framework was to study the worstcase theoretical complexity of geometric problems involving linear objects (points, lines, polyhedra...) in Euclidean spaces. This socalled classical computational geometry has some known limitations:

Objects: dealing with objects only defined by linear equations.

Complexity: worstcase complexities often do not capture realistic behaviour.

Dimension: complexities are often exponential in the dimension.
Even if these limitations have already got some attention from the community [44], a quick look at the flagship conference SoCG (Symposium on Computational Geometry. http://www.computationalgeometry.org/.) proceedings shows that these topics still need a big effort.
It should be stressed that, in this document, the notion of certified algorithms is to be understood with respect to robustness issues. In other words, certification does not refer to programs that are proven correct with the help of mechnical proof assistants such as Coq, but to algorithms that are proven correct on paper even in the presence of degeneracies and computerinduced numerical rounding errors.
We address several of the above limitations:
$\u2022$ Nonlinear 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 concepts 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 (Nonuniform rational basis splines) and meshing singular surfaces in a certified manner are important examples of such problems.
$\u2022$ NonEuclidean 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 nonEuclidean 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 ${\mathbb{R}}^{d}$ of computational geometry and encompass nonEuclidean spaces.
$\u2022$ Probability in computational geometry. The design of efficient algorithms is driven by the analysis of their complexity. Traditionally, worstcase 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.
$\u2022$ Discrete geometric structures. Many geometric algorithms work, explicitly or implicitly, over discrete structures such as graphs, hypergraphs, lattices that are induced by the geometric input data. For example, convex hulls or straightline graph drawing are essentially based on orientation predicates, and therefore operate on the socalled order type of the input point set. Order types are a subclass of oriented matroids that remains poorly understood: for instance, we do not even know how to sample this space with reasonable bias. One of our goals is to contribute to the development of these foundations by better understanding these discrete geometric structures.