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Section: Overall Objectives

Overall Objectives

Starting in the eighties, the emerging computational geometry community has put a lot of effort to design and analyze algorithms for geometric problems. The most commonly used framework was to study the worst-case theoretical complexity of geometric problems involving linear objects (points, lines, polyhedra...) in Euclidean spaces. This so-called classical computational geometry has some known limitations:

• Objects: dealing with objects only defined by linear equations.

• Ambient space: considering only Euclidean spaces.

• Complexity: worst-case complexities often do not capture realistic behaviour.

• Dimension: complexities are often exponential in the dimension.

• Robustness: ignoring degeneracies and rounding errors.

Even if these limitations have already got some attention from the community [26], a quick look at the flagship conference SoCG (Symposium on Computational Geometry. http://www.computational-geometry.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 computer-induced numerical rounding errors.

We address several of the above limitations:

$•$ 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 (Non-uniform rational basis spline) 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 ${ℝ}^d$ 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.