Internship proposal Walking in triangulation , probabilistic analysis
نویسنده
چکیده
Geometric problems are central in many areas of science and engineering. Computational geometry, the study of combinatorial and algorithmic problems in a geometric setting, and in particular triangulations have tremendous practical applications in areas such as computer graphics, computer vision and imaging, scientific visualization, geographic information systems, astronomy, computational biology... Traditionally, the complexity of computational geometry algorithms is studied in the worst case setting. This kind of analysis is often quite pessimistic compared to real life data. Due to the emergence of standardized software libraries, in particular the Computational Geometry Algorithms Library CGAL, developed in the framework of an Open Source Project, the so-far mostly theoretical results developed in computational geometry are being used and extended for practical use like never before. CGAL has been proposing efficient and robust packages computing Delaunay triangulations in the 2D and 3D Euclidean spaces for years.
منابع مشابه
Research internship ( Master M 2 ) Title Probabilistic specifications
Current needs in the verification of systems evolve from boolean properties to finer quantitative properties. The overall theory behind automatic verification is actively being extended by the scientific community from the boolean to the quantitative setting. One of the foremost extension concerns the study of probabilistic systems or probabilistic specifications. In this internship, we mainly ...
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The currently most efficient algorithm for inference with a probabilistic network builds upon a triangulation of a network’s graph. In this paper, we show that pre-processing can help in finding good triangulations for probabilistic networks, that is, triangulations with a minimal maximum clique size. We provide a set of rules for stepwise reducing a graph. The reduction allows us to solve the ...
متن کاملInternship proposal
On the one hand, classical analysis of geometric algorithms is done in the worst case. The worst case is often very pessimistic and need to designed carefully pathological data set to be reached. On the other hand, analysis of geometric algorithms can also be done under probabilistic hypotheses on the data distribution. Unfortunately, common hypotheses, such as Poisson distribution or uniform d...
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تاریخ انتشار 2011