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Multilevel least squares approximation of scattered data over binary triangulations

机译:二进制三角剖分上分散数据的多级最小二乘逼近

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摘要

An adaptive method for approximating huge scattered data sets is presented. The approximation scheme generates multilevel triangulations obtained using a subdivision scheme known as longest edge bisection. Nested function spaces are defined over the multilevel triangulations. The approximation problem is solved by successive refinement of the triangulation while iterative methods are used for solving a system of linear equations at intermediate levels of the multi-level scheme. Regularization terms are coupled with a standard least squares formulation to guarantee uniqueness and control smoothness of the solution.
机译:提出了一种自适应方法,用于近似庞大的分散数据集。近似方案生成使用称为最长边二等分的细分方案获得的多级三角剖分。嵌套函数空间在多级三角剖分中定义。近似问题是通过对三角剖分的逐次细化来解决的,而迭代方法则用于求解多级方案中间级的线性方程组。正则化项与标准最小二乘公式相结合,以确保解决方案的唯一性和控制平滑性。

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