A Faster 4-Approximation Algorithm for the Unit Disk Cover Problem

Abstract Given a set P of n points in the plane, we consider the problem of covering P with a minimum number of unit disks. This problem is known to be NP-hard. We present a simple 4-approximation algorithm for this problem which runs in O(n log n)-time and uses the plane-sweep technique. Previous algorithms that achieve the same approximation ratio have a higher time complexity. We also show how to extend this algorithm to other metrics, and to three dimensions

Illuminating the x-Axis by ?-Floodlights

Given a set S of regions with piece-wise linear boundary and a positive angle α < 90°, we consider the problem of computing the locations and orientations of the minimum number of α-floodlights positioned at points in S which suffice to illuminate the entire x-axis. We show that the problem can be solved in O(n log n) time and O(n) space, where n is the number of vertices of the set S

Sparse Higher Order ?ech Filtrations

For a finite set of balls of radius r, the k-fold cover is the space covered by at least k balls. Fixing the ball centers and varying the radius, we obtain a nested sequence of spaces that is called the k-fold filtration of the centers. For k = 1, the construction is the union-of-balls filtration that is popular in topological data analysis. For larger k, it yields a cleaner shape reconstruction in the presence of outliers. We contribute a sparsification algorithm to approximate the topology of the k-fold filtration. Our method is a combination and adaptation of several techniques from the well-studied case k = 1, resulting in a sparsification of linear size that can be computed in expected near-linear time with respect to the number of input points

On the Complexity of Two Dots for Narrow Boards and Few Colors

Two Dots is a popular single-player puzzle video game for iOS and Android. A level of this game consists of a grid of colored dots. The player connects two or more adjacent dots, removing them from the grid and causing the remaining dots to fall, as if influenced by gravity. One special move, which is frequently a game-changer, consists of connecting a cycle of dots: this removes all the dots of the given color from the grid. The goal is to remove a certain number of dots of each color using a limited number of moves. The computational complexity of Two Dots has already been addressed in [Misra, FUN 2016], where it has been shown that the general version of the problem is NP-complete. Unfortunately, the known reductions produce Two Dots levels having both a large number of colors and many columns. This does not completely match the spirit of the game, where, on the one hand, only few colors are allowed, and on the other hand, the grid of the game has only a constant number of columns. In this paper, we partially fill this gap by assessing the computational complexity of Two Dots instances having a small number of colors or columns. More precisely, we show that Two Dots is hard even for instances involving only 3 colors or 2 columns. As a contrast, we also prove that the problem can be solved in polynomial-time on single-column instances with a constant number of goals

Stability Yields Sublinear Time Algorithms for Geometric Optimization in Machine Learning

In this paper, we study several important geometric optimization problems arising in machine learning. First, we revisit the Minimum Enclosing Ball (MEB) problem in Euclidean space ?^d. The problem has been extensively studied before, but real-world machine learning tasks often need to handle large-scale datasets so that we cannot even afford linear time algorithms. Motivated by the recent developments on beyond worst-case analysis, we introduce the notion of stability for MEB, which is natural and easy to understand. Roughly speaking, an instance of MEB is stable, if the radius of the resulting ball cannot be significantly reduced by removing a small fraction of the input points. Under the stability assumption, we present two sampling algorithms for computing radius-approximate MEB with sample complexities independent of the number of input points n. In particular, the second algorithm has the sample complexity even independent of the dimensionality d. We also consider the general case without the stability assumption. We present a hybrid algorithm that can output either a radius-approximate MEB or a covering-approximate MEB, which improves the running time and the number of passes for the previous sublinear MEB algorithms. Further, we extend our proposed notion of stability and design sublinear time algorithms for other geometric optimization problems including MEB with outliers, polytope distance, one-class and two-class linear SVMs (without or with outliers). Our proposed algorithms also work fine for kernels

Space Efficient Two-Dimensional Orthogonal Colored Range Counting

In the two-dimensional orthogonal colored range counting problem, we preprocess a set, $P$, of $n$ colored points on the plane, such that given an orthogonal query rectangle, the number of distinct colors of the points contained in this rectangle can be computed efficiently. For this problem, we design three new solutions, and the bounds of each can be expressed in some form of time-space tradeoff. By setting appropriate parameter values for these solutions, we can achieve new specific results with (the space are in words and $\epsilon$ is an arbitrary constant in $(0,1)$): ** $O(n\lg^3 n)$ space and $O(\sqrt{n}\lg^{5/2} n \lg \lg n)$ query time; ** $O(n\lg^2 n)$ space and $O(\sqrt{n}\lg^{4+\epsilon} n)$ query time; ** $O(n\frac{\lg^2 n}{\lg \lg n})$ space and $O(\sqrt{n}\lg^{5+\epsilon} n)$ query time; ** $O(n\lg n)$ space and $O(n^{1/2+\epsilon})$ query time. A known conditional lower bound to this problem based on Boolean matrix multiplication gives some evidence on the difficulty of achieving near-linear space solutions with query time better than $\sqrt{n}$ by more than a polylogarithmic factor using purely combinatorial approaches. Thus the time and space bounds in all these results are efficient. Previously, among solutions with similar query times, the most space-efficient solution uses $O(n\lg^4 n)$ space to answer queries in $O(\sqrt{n}\lg^8 n)$ time (SIAM. J. Comp.~2008). Thus the new results listed above all achieve improvements in space efficiency, while all but the last result achieve speed-up in query time as well.Comment: full version of an ESA 2021 pape

Hierarchical Structures for High Dimensional Data Analysis

The volume of data is not the only problem in modern data analysis, data complexity is often more challenging. In many areas such as computational biology, topological data analysis, and machine learning, the data resides in high dimensional spaces which may not even be Euclidean. Therefore, processing such massive and complex data and extracting some useful information is a big challenge. Our methods will apply to any data sets given as a set of objects and a metric that measures the distance between them. In this dissertation, we first consider the problem of preprocessing and organizing such complex data into a hierarchical data structure that allows efficient nearest neighbor and range queries. There have been many data structures for general metric spaces, but almost all of them have construction time that can be quadratic in terms of the number of points. There are only two data structures with O(n log n) construction time, but both have very complex algorithms and analyses. Also, they cannot be implemented efficiently. Here, we present a simple, randomized incremental algorithm that builds a metric data structure in O(n log n) time in expectation. Thus, we achieve the best of both worlds, simple implementation with asymptotically optimal performance. Furthermore, we consider the close relationship between our metric data structure and point orderings used in applications such as k-center clustering. We give linear time algorithms to go back and forth between these orderings and our metric data structure. In the last part, we use metric data structures to extract topological features of a data set, such as the number of connected components, holes, and voids. We give an efficient algorithm for constructing a (1 + epsilon)-approximation to the so-called Nerve filtration of a metric space, a fundamental tool in topological data analysis