We revisit the problem of property testing for convex position for point sets in $\mathbb{R}^d$. Our results draw from previous ideas of Czumaj, Sohler, and Ziegler (ESA 2000). First, the algorithm is redesigned and its analysis is revised for correctness. Second, its functionality is expanded by (i)~exhibiting both negative and positive certificates along with the convexity determination, and (ii)~significantly extending the input range for moderate and higher dimensions. The behavior of the randomized tester is as follows: (i)~if $P$ is in convex position, it accepts; (ii)~if $P$ is far from convex position, with probability at least $2/3$, it rejects and outputs a $(d+2)$-point witness of non-convexity as a negative certificate; (iiii)~if $P$ is close to convex position, with probability at least $2/3$, it accepts and outputs an approximation of the largest subset in convex position. The algorithm examines a sublinear number of points and runs in subquadratic time for every dimension $d$ (and is faster in low dimensions).
翻译:我们重新审视了对点数的 $\ mathbb{R ⁇ d$ 进行 convex 位置属性测试的问题。 我们的结果来自Czumaj、 Sohler 和 Ziegler (ESA 2000) 的先前想法。 首先,对算法进行了重新设计,并根据正确性对其分析进行了修改。 其次,它的功能扩大为 (一) 限制负和正证书,同时确定稳度,以及 (二) 显著扩大中等和较高维度的输入范围。随机测试器的行为如下:(一) ~ 如果美元处于Czumex 位置,它接受;(二) ~ 如果美元远离Czumex 位置, 它拒绝使用和输出一个 $(d+2) 点的非convex 证人为负值;(三) ~ 如果美元接近 convex 位置, 概率至少为2/3美元, 它接受并输出一个最大的子项的近似值。 算法对一个小段点数和小段的大小进行快速检查( 方位) 。