No Arabic abstract
Let $mathscr O$ be a set of $n$ disjoint obstacles in $mathbb{R}^2$, $mathscr M$ be a moving object. Let $s$ and $l$ denote the starting point and maximum path length of the moving object $mathscr M$, respectively. Given a point $p$ in ${R}^2$, we say the point $p$ is achievable for $mathscr M$ such that $pi(s,p)leq l$, where $pi(cdot)$ denotes the shortest path length in the presence of obstacles. One is to find a region $mathscr R$ such that, for any point $pin mathbb{R}^2$, if it is achievable for $mathscr M$, then $pin mathscr R$; otherwise, $p otin mathscr R$. In this paper, we restrict our attention to the case of line-segment obstacles. To tackle this problem, we develop three algorithms. We first present a simpler-version algorithm for the sake of intuition. Its basic idea is to reduce our problem to computing the union of a set of circular visibility regions (CVRs). This algorithm takes $O(n^3)$ time. By analysing its dominant steps, we break through its bottleneck by using the short path map (SPM) technique to obtain those circles (unavailable beforehand), yielding an $O(n^2log n)$ algorithm. Owing to the finding above, the third algorithm also uses the SPM technique. It however, does not continue to construct the CVRs. Instead, it directly traverses each region of the SPM to trace the boundaries, the final algorithm obtains $O(nlog n)$ complexity.
Given points $p_1, dots, p_n$ in $mathbb{R}^d$, how do we find a point $x$ which maximizes $frac{1}{n} sum_{i=1}^n e^{-|p_i - x|^2}$? In other words, how do we find the maximizing point, or mode of a Gaussian kernel density estimation (KDE) centered at $p_1, dots, p_n$? Given the power of KDEs in representing probability distributions and other continuous functions, the basic mode finding problem is widely applicable. However, it is poorly understood algorithmically. Few provable algorithms are known, so practitioners rely on heuristics like the mean-shift algorithm, which are not guaranteed to find a global optimum. We address this challenge by providing fast and provably accurate approximation algorithms for mode finding in both the low and high dimensional settings. For low dimension $d$, our main contribution is to reduce the mode finding problem to a solving a small number of systems of polynomial inequalities. For high dimension $d$, we prove the first dimensionality reduction result for KDE mode finding, which allows for reduction to the low dimensional case. Our result leverages Johnson-Lindenstrauss random projection, Kirszbrauns classic extension theorem, and perhaps surprisingly, the mean-shift heuristic for mode finding.
We give algorithms with running time $2^{O({sqrt{k}log{k}})} cdot n^{O(1)}$ for the following problems. Given an $n$-vertex unit disk graph $G$ and an integer $k$, decide whether $G$ contains (1) a path on exactly/at least $k$ vertices, (2) a cycle on exactly $k$ vertices, (3) a cycle on at least $k$ vertices, (4) a feedback vertex set of size at most $k$, and (5) a set of $k$ pairwise vertex-disjoint cycles. For the first three problems, no subexponential time parameterized algorithms were previously known. For the remaining two problems, our algorithms significantly outperform the previously best known parameterized algorithms that run in time $2^{O(k^{0.75}log{k})} cdot n^{O(1)}$. Our algorithms are based on a new kind of tree decompositions of unit disk graphs where the separators can have size up to $k^{O(1)}$ and there exists a solution that crosses every separator at most $O(sqrt{k})$ times. The running times of our algorithms are optimal up to the $log{k}$ factor in the exponent, assuming the Exponential Time Hypothesis.
We solve the problem of finding interspersed maximal repeats using a suffix array construction. As it is well known, all the functionality of suffix trees can be handled by suffix arrays, gaining practicality. Our solution improves the suffix tree based approaches for the repeat finding problem, being particularly well suited for very large inputs. We prove the corrrectness and complexity of the algorithms.
In this paper we present new algorithmic solutions for several constrained geometric server placement problems. We consider the problems of computing the 1-center and obnoxious 1-center of a set of line segments, constrained to lie on a line segment, and the problem of computing the K-median of a set of points, constrained to lie on a line. The presented algorithms have applications in many types of distributed systems, as well as in various fields which make use of distributed systems for running some of their applications (like chemistry, metallurgy, physics, etc.).
For any $epsilon>0$, Laue and Matijevi{c} [CCCG07, IPL08] give a PTAS for finding a $(1+epsilon)$-approximate solution to the $k$-hop MST problem in the Euclidean plane that runs in time $(n/epsilon)^{O(k/epsilon)}$. In this paper, we present an algorithm that runs in time $(n/epsilon)^{O(log k cdot(1/epsilon)^2cdotlog^2(1/epsilon))}$. This gives an improvement on the dependency on $k$ on the exponent, while having a worse dependency on $epsilon$. As in Laue and Matijevi{c}, we follow the framework introduced by Arora for Euclidean TSP. Our key ingredients include exponential distance scaling and compression of dynamic programming state tables.