arXiv subjects

Haitao Wang

Publications and source records attributed to Haitao Wang.

At least 145 records · Page 8Linked to original sources

arXiv2018

An $O(n\log n)$-Time Algorithm for the k-Center Problem in Trees

We consider a classical k-center problem in trees. Let T be a tree of n vertices and every vertex has a nonnegative weight. The problem is to find k centers on the edges of T such that the maximum weighted distance from all vertices to their closest centers is minimized. Megiddo and Tamir (SIAM J. Comput., 1983) gave an algorithm that can solve the problem in $O(n\log^2 n)$ time by using Cole's parametric search. Since then it has been open for over three decades whether the problem can be solved in $O(n\log n)$ time. In this paper, we present an $O(n\log n)$ time algorithm for the problem and thus settle the open problem affirmatively.

cs.DS↗

arXiv2017

Quantitative Pointwise Estimate of the Solution of the Linearized Boltzmann Equation

We study the quantitative pointwise behavior of the solutions of the linearized Boltzmann equation for hard potentials, Maxwellian molecules and soft potentials, with Grad's angular cutoff assumption. More precisely, for solutions inside the finite Mach number region, we obtain the pointwise fluid structure for hard potentials and Maxwellian molecules, and optimal time decay in the fluid part and sub-exponential time decay in the non-fluid part for soft potentials. For solutions outside the finite Mach number region, we obtain sub-exponential decay in the space variable. The singular wave estimate, regularization estimate and refined weighted energy estimate play important roles in this paper. Our results largely extend the classical results of Liu-Yu \cite{[LiuYu], [LiuYu2], [LiuYu1]} and Lee-Liu-Yu \cite% {[LeeLiuYu]} to hard and soft potentials by imposing suitable exponential velocity weight on the initial condition.

math.AP↗

arXiv2017

Pointwise wave behavior of the Navier-Stokes equations in half space

In this paper, we investigate the pointwise behavior of the solution for the compressible Navier-Stokes equations with mixed boundary condition in half space. Our results show that the leading order of Green's function for the linear system in half space are heat kernels propagating with sound speed in two opposite directions and reflected heat kernel (due to the boundary effect) propagating with positive sound speed. With the strong wave interactions, the nonlinear analysis exhibits the rich wave structure: the diffusion waves interact with each other and consequently, the solution decays with algebraic rate.

math.AP↗

arXiv2017

Smoothing effects and decay estimate of the solution of the linearized two species Landau equation

We study the Landau equation for a mixture of two species in the whole space, with initial condition of one species near a vacuum and the other near a Maxwellian equilibrium state. For the linearized level, without any smoothness assumption on the initial data, it is shown that the solution becomes smooth instantaneously in both the space and momentum variables. Moreover, the large time behavior of the solution is also obtained.

math-ph↗

arXiv2017

Solving Linearized Landau Equation Pointwisely

We study the pointwise (in the space and time variables) behavior of the linearized Landau equation for hard and moderately soft potentials. The solution has very clear description in the $(x,t)-$variables, including large time behavior and asymptotic behavior. More precisely, we obtain the pointwise fluid structure inside finite Mach number region, and exponential or sub-exponential decay, depending on interactions between particles, in the space variable outside finite Mach number region. The spectrum analysis, regularization effect and refined weighted energy estimate play important roles in this paper.

math-ph↗

arXiv2017

Bicriteria Rectilinear Shortest Paths among Rectilinear Obstacles in the Plane

Given a rectilinear domain $\mathcal{P}$ of $h$ pairwise-disjoint rectilinear obstacles with a total of $n$ vertices in the plane, we study the problem of computing bicriteria rectilinear shortest paths between two points $s$ and $t$ in $\mathcal{P}$. Three types of bicriteria rectilinear paths are considered: minimum-link shortest paths, shortest minimum-link paths, and minimum-cost paths where the cost of a path is a non-decreasing function of both the number of edges and the length of the path. The one-point and two-point path queries are also considered. Algorithms for these problems have been given previously. Our contributions are threefold. First, we find a critical error in all previous algorithms. Second, we correct the error in a not-so-trivial way. Third, we further improve the algorithms so that they are even faster than the previous (incorrect) algorithms when $h$ is relatively small. For example, for the minimum-link shortest paths, we obtain the following results. Our algorithm computes a minimum-link shortest $s$-$t$ path in $O(n+h\log^{3/2} h)$ time. For the one-point queries, we build a data structure of size $O(n+ h\log h)$ in $O(n+h\log^{3/2} h)$ time for a source point $s$, such that given any query point $t$, a minimum-link shortest $s$-$t$ path can be determined in $O(\log n)$ time. For the two-point queries, with $O(n+h^2\log^2 h)$ time and space preprocessing, a minimum-link shortest $s$-$t$ path can be determined in $O(\log n+\log^2 h)$ time for any two query points $s$ and $t$; alternatively, with $O(n+h^2\cdot \log^{2} h \cdot 4^{\sqrt{\log h}})$ time and $O(n+h^2\cdot \log h \cdot 4^{\sqrt{\log h}})$ space preprocessing, we can answer each two-point query in $O(\log n)$ time.

cs.CG↗

arXiv2017

COPSS-lite: Lightweight ICN Based Pub/Sub for IoT Environments

Information Centric Networking is a new networking paradigm that treats content as first class entity. It provides content to users without regards to the current location of the content. The publish/subscribe systems have gained popularity in Internet. Pub/sub systems dismisses the need for users to request every content of their interest. Instead, the content is supplied to interested users (subscribers) as and when it is published. CCN/NDN are popular ICN proposals widely accepted in the ICN community however, they do not provide an efficient pub/sub mechanism. COPSS enhances CCN/NDN with an efficient pub/sub capability. Internet of Things (IoT) is a growing topic of interest in both Academia and Industry. The current designs for IoT relies on IP. However, the IoT devices are constrained in their available resources and IP is heavy for their operation.We observed that IoT's are information centric in nature and hence ICN is a more suitable candidate to support IoT environments. Although NDN and COPSS work well for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. CCN-lite is a light weight, inter-operable version of the CCNx protocol for supporting the IoT devices. However, CCN-lite like its ancestors lacks the support for an efficient pub/sub mechanism. In this paper, we developed COPSS-lite, an efficient and light weight implementation of pub/sub for IoT. COPSS-lite is developed to enhance CCN-lite and also support multi-hop connection by incorporating the famous RPL protocol for low power and lossy networks. We provide a preliminary evaluation to show proof of operability with real world sensor devices in IoT lab. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and we observe significant performance benefits with COPSS-lite in IoT environments.

cs.NI↗

arXiv2017

Covering Uncertain Points in a Tree

In this paper, we consider a coverage problem for uncertain points in a tree. Let T be a tree containing a set P of n (weighted) demand points, and the location of each demand point P_i\in P is uncertain but is known to appear in one of m_i points on T each associated with a probability. Given a covering range λ, the problem is to find a minimum number of points (called centers) on T to build facilities for serving (or covering) these demand points in the sense that for each uncertain point P_i\in P, the expected distance from P_i to at least one center is no more than $λ$. The problem has not been studied before. We present an O(|T|+M\log^2 M) time algorithm for the problem, where |T| is the number of vertices of T and M is the total number of locations of all uncertain points of P, i.e., M=\sum_{P_i\in P}m_i. In addition, by using this algorithm, we solve a k-center problem on T for the uncertain points of P.

cs.CG↗

arXiv2017

Quickest Visibility Queries in Polygonal Domains

Let $s$ be a point in a polygonal domain $\mathcal{P}$ of $h-1$ holes and $n$ vertices. We consider a quickest visibility query problem. Given a query point $q$ in $\mathcal{P}$, the goal is to find a shortest path in $\mathcal{P}$ to move from $s$ to see $q$ as quickly as possible. Previously, Arkin et al. (SoCG 2015) built a data structure of size $O(n^22^{α(n)}\log n)$ that can answer each query in $O(K\log^2 n)$ time, where $α(n)$ is the inverse Ackermann function and $K$ is the size of the visibility polygon of $q$ in $\mathcal{P}$ (and $K$ can be $Θ(n)$ in the worst case). In this paper, we present a new data structure of size $O(n\log h + h^2)$ that can answer each query in $O(h\log h\log n)$ time. Our result improves the previous work when $h$ is relatively small. In particular, if $h$ is a constant, then our result even matches the best result for the simple polygon case (i.e., $h=1$), which is optimal. As a by-product, we also have a new algorithm for a shortest-path-to-segment query problem. Given a query line segment $τ$ in $\mathcal{P}$, the query seeks a shortest path from $s$ to all points of $τ$. Previously, Arkin et al. gave a data structure of size $O(n^22^{α(n)}\log n)$ that can answer each query in $O(\log^2 n)$ time, and another data structure of size $O(n^3\log n)$ with $O(\log n)$ query time. We present a data structure of size $O(n)$ with query time $O(h\log \frac{n}{h})$, which also favors small values of $h$ and is optimal when $h=O(1)$.

cs.CG↗

arXiv2017

Separating Overlapped Intervals on a Line

Given $n$ intervals on a line $\ell$, we consider the problem of moving these intervals on $\ell$ such that no two intervals overlap and the maximum moving distance of the intervals is minimized. The difficulty for solving the problem lies in determining the order of the intervals in an optimal solution. By interesting observations, we show that it is sufficient to consider at most $n$ "candidate" lists of ordered intervals. Further, although explicitly maintaining these lists takes $Ω(n^2)$ time and space, by more observations and a pruning technique, we present an algorithm that can compute an optimal solution in $O(n\log n)$ time and $O(n)$ space. We also prove an $Ω(n\log n)$ time lower bound for solving the problem, which implies the optimality of our algorithm.

cs.CG↗

arXiv2017

Linear Time Approximation Schemes for Geometric Maximum Coverage

We study approximation algorithms for the following geometric version of the maximum coverage problem: Let $\mathcal{P}$ be a set of $n$ weighted points in the plane. Let $D$ represent a planar object, such as a rectangle, or a disk. We want to place $m$ copies of $D$ such that the sum of the weights of the points in $\mathcal{P}$ covered by these copies is maximized. For any fixed $\varepsilon>0$, we present efficient approximation schemes that can find a $(1-\varepsilon)$-approximation to the optimal solution. In particular, for $m=1$ and for the special case where $D$ is a rectangle, our algorithm runs in time $O(n\log (\frac{1}{\varepsilon}))$, improving on the previous result. For $m>1$ and the rectangular case, our algorithm runs in $O(\frac{n}{\varepsilon}\log (\frac{1}{\varepsilon})+\frac{m}{\varepsilon}\log m +m(\frac{1}{\varepsilon})^{O(\min(\sqrt{m},\frac{1}{\varepsilon}))})$ time. For a more general class of shapes (including disks, polygons with $O(1)$ edges), our algorithm runs in $O(n(\frac{1}{\varepsilon})^{O(1)}+\frac{m}ε\log m + m(\frac{1}{\varepsilon})^{O(\min(m,\frac{1}{\varepsilon^2}))})$ time.

cs.CG↗

arXiv2017

Improved Algorithms For Structured Sparse Recovery

It is known that certain structures of the signal in addition to the standard notion of sparsity (called structured sparsity) can improve the sample complexity in several compressive sensing applications. Recently, Hegde et al. proposed a framework, called approximation-tolerant model-based compressive sensing, for recovering signals with structured sparsity. Their framework requires two oracles, the head- and the tail-approximation projection oracles. The two oracles should return approximate solutions in the model which is closest to the query signal. In this paper, we consider two structured sparsity models and obtain improved projection algorithms. The first one is the tree sparsity model, which captures the support structure in the wavelet decomposition of piecewise-smooth signals. We propose a linear time $(1-ε)$-approximation algorithm for head-approximation projection and a linear time $(1+ε)$-approximation algorithm for tail-approximation projection. The best previous result is an $\tilde{O}(n\log n)$ time bicriterion approximation algorithm (meaning that their algorithm may return a solution of sparsity larger than $k$) by Hegde et al. Our result provides an affirmative answer to the open problem mentioned in the survey of Hegde and Indyk. As a corollary, we can recover a constant approximate $k$-sparse signal. The other is the Constrained Earth Mover Distance (CEMD) model, which is useful to model the situation where the positions of the nonzero coefficients of a signal do not change significantly as a function of spatial (or temporal) locations. We obtain the first single criterion constant factor approximation algorithm for the head-approximation projection. The previous best known algorithm is a bicriterion approximation. Using this result, we can get a faster constant approximation algorithm with fewer measurements for the recovery problem in CEMD model.

cs.IT↗

arXiv2016

Dispersing Points on Intervals

We consider a problem of dispersing points on disjoint intervals on a line. Given n pairwise disjoint intervals sorted on a line, we want to find a point in each interval such that the minimum pairwise distance of these points is maximized. Based on a greedy strategy, we present a linear time algorithm for the problem. Further, we also solve in linear time the cycle version of the problem where the intervals are given on a cycle.

cs.CG↗

arXiv2016

Computing the $L_1$ Geodesic Diameter and Center of a Polygonal Domain

For a polygonal domain with $h$ holes and a total of $n$ vertices, we present algorithms that compute the $L_1$ geodesic diameter in $O(n^2+h^4)$ time and the $L_1$ geodesic center in $O((n^4+n^2 h^4)α(n))$ time, respectively, where $α(\cdot)$ denotes the inverse Ackermann function. No algorithms were known for these problems before. For the Euclidean counterpart, the best algorithms compute the geodesic diameter in $O(n^{7.73})$ or $O(n^7(h+\log n))$ time, and compute the geodesic center in $O(n^{11}\log n)$ time. Therefore, our algorithms are significantly faster than the algorithms for the Euclidean problems. Our algorithms are based on several interesting observations on $L_1$ shortest paths in polygonal domains.

cs.CG↗

arXiv2016

An Improved Algorithm for Diameter-Optimally Augmenting Paths in a Metric Space

Let $P$ be a path graph of $n$ vertices embedded in a metric space. We consider the problem of adding a new edge to $P$ such that the diameter of the resulting graph is minimized. Previously (in ICALP 2015) the problem was solved in $O(n\log^3 n)$ time. In this paper, based on new observations and different algorithmic techniques, we present an $O(n\log n)$ time algorithm.

cs.DS↗

arXiv2016

On the Geodesic Centers of Polygonal Domains

In this paper, we study the problem of computing Euclidean geodesic centers of a polygonal domain $\mathcal{P}$ with a total of $n$ vertices. We discover many interesting observations. We give a necessary condition for a point being a geodesic center. We show that there is at most one geodesic center among all points of $\mathcal{P}$ that have topologically-equivalent shortest path maps. This implies that the total number of geodesic centers is bounded by the combinatorial size of the shortest path map equivalence decomposition of $\mathcal{P}$, which is known to be $O(n^{10})$. One key observation is a $π$-range property on shortest path lengths when points are moving. With these observations, we propose an algorithm that computes all geodesic centers in $O(n^{11}\log n)$ time. Previously, an algorithm of $O(n^{12+ε})$ time was known for this problem, for any $ε>0$.

cs.CG↗

arXiv2016

$ε$-Kernel Coresets for Stochastic Points

With the dramatic growth in the number of application domains that generate probabilistic, noisy and uncertain data, there has been an increasing interest in designing algorithms for geometric or combinatorial optimization problems over such data. In this paper, we initiate the study of constructing $ε$-kernel coresets for uncertain points. We consider uncertainty in the existential model where each point's location is fixed but only occurs with a certain probability, and the locational model where each point has a probability distribution describing its location. An $ε$-kernel coreset approximates the width of a point set in any direction. We consider approximating the expected width (an \expkernel), as well as the probability distribution on the width (an \probkernel) for any direction. We show that there exists a set of $O(1/ε^{(d-1)/2})$ deterministic points which approximate the expected width under the existential and locational models, and we provide efficient algorithms for constructing such coresets. We show, however, it is not always possible to find a subset of the original uncertain points which provides such an approximation. However, if the existential probability of each point is lower bounded by a constant, an exp-kernel\ (or an fpow-kernel) is still possible. We also construct an quant-kernel coreset in linear time. Finally, combining with known techniques, we show a few applications to approximating the extent of uncertain functions, maintaining extent measures for stochastic moving points and some shape fitting problems under uncertainty.

cs.DS↗

arXiv2015

Computing the Rectilinear Center of Uncertain Points in the Plane

In this paper, we consider the rectilinear one-center problem on uncertain points in the plane. In this problem, we are given a set $P$ of $n$ (weighted) uncertain points in the plane and each uncertain point has $m$ possible locations each associated with a probability for the point appearing at that location. The goal is to find a point $q^*$ in the plane which minimizes the maximum expected rectilinear distance from $q^*$ to all uncertain points of $P$, and $q^*$ is called a rectilinear center. We present an algorithm that solves the problem in $O(mn)$ time. Since the input size of the problem is $Θ(mn)$, our algorithm is optimal.

cs.CG↗