Online Geometric Reconstruction

被引：9
|
作者：
Chazelle, Bernard [1 ]
Seshadhri, C. [2 ]
机构：
[1] Princeton Univ, Princeton, NJ 08544 USA
[2] Sandia Natl Labs, Livermore, CA 94551 USA
来源：
JOURNAL OF THE ACM | 2011年 / 58卷 / 04期
关键词：
Algorithms; Theory; Computational geometry; sublinear algorithms;
D O I：
10.1145/1989727.1989728
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
We investigate a new class of geometric problems based on the idea of online error correction. Suppose one is given access to a large geometric dataset though a query mechanism; for example, the dataset could be a terrain and a query might ask for the coordinates of a particular vertex or for the edges incident to it. Suppose, in addition, that the dataset satisfies some known structural property P (for example, monotonicity or convexity) but that, because of errors and noise, the queries occasionally provide answers that violate P. Can one design a filter that modifies the query's answers so that (i) the output satisfies P; (ii) the amount of data modification is minimized? We provide upper and lower bounds on the complexity of online reconstruction for convexity in 2D and 3D.
引用
收藏
页数：32
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