Behnam Iranfar; Mohammad Farshi
Abstract
Given a point set $S\subset \mathbb{R}^d$, the $\theta$-graph of $S$ is as follows: for each point $s\in S$, draw cones with apex at $s$ and angle $\theta$ %fix a line through $p$ at each cone and connect $s$ to the point in each cone such that the projection of the point on the bisector of the cone ...
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Given a point set $S\subset \mathbb{R}^d$, the $\theta$-graph of $S$ is as follows: for each point $s\in S$, draw cones with apex at $s$ and angle $\theta$ %fix a line through $p$ at each cone and connect $s$ to the point in each cone such that the projection of the point on the bisector of the cone is the closest to~$s$. One can define the $\theta$- graph on an uncertain point set, i.e. a point set where each point $s_i$ exists with an independent probability $\pi_i \in (0,1]$. In this paper, we propose an algorithm that computes the expected weight of the $\theta$-graph on a given uncertain point set. The proposed algorithm takes $O(n^2\alpha(n^2,n)^{2d})$ time and $O(n^2)$ space, where $n$ is the number of points, $d$ and $\theta$ are constants, and $\alpha$ is the inverse of the Ackermann's function.
Amir Mesrikhani; Mohammad Farshi; Behnam Iranfar
Abstract
Let $S$ be a set of imprecise points that is represented by axis-aligned pairwise disjoint squares in the plane. A precise instance of $S$ is a set of points, one from each region of $S$. In this paper, we study the optimal minimum spanning tree (\textit{OptMST}) problem on $S$. The \textit{OptMST} problem ...
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Let $S$ be a set of imprecise points that is represented by axis-aligned pairwise disjoint squares in the plane. A precise instance of $S$ is a set of points, one from each region of $S$. In this paper, we study the optimal minimum spanning tree (\textit{OptMST}) problem on $S$. The \textit{OptMST} problem looks for the precise instance of $S$ such that the weight of the MST in this instance, maximize (Max-MST) or minimize (Min-MST) between all precise instances of~$S$ under $L_1$-metric. We present a $(\frac{3}{7})$-approximation algorithm for Max-MST. This is an improvement on the best-known approximation factor of $1/3$. If $S$ satisfies $k$-separability property (the distance between any pair of squares are at least $k.a_{max}$ where $a_{max}$ is the maximum length of the squares), the factor parameterizes to $\frac{2k+3}{2k+7}$. We propose a new lower bound for Min-MST problem on $S$ under $L_1$-metric where $S$ contains unit squares and provide an approximation algorithm with $(1+2\sqrt{2})$ asymptotic factor.
