Dense Point Sets Have Sparse Delaunay Triangulations or “... But Not Too Nasty”

The spread of a finite set of points is the ratio between the longest and shortest pairwise distances. We prove that the Delaunay triangulation of any set of $n$ points in~$\Real^3$ with spread $\Delta$ has complexity $O(\Delta^3)$. This bound is tight in the worst case for all $\Delta = O(\sqrt{n})$. In particular, the Delaunay triangulation of any dense point set has linear complexity. We also generalize this upper bound to regular triangulations of $k$-ply systems of balls, unions of several dense point sets, and uniform samples of smooth surfaces. On the other hand, for any $n$ and $\Delta = O(n)$, we construct a regular triangulation of complexity $\Omega(n\Delta)$ whose $n$ vertices have spread $\Delta$.     

Stabbing Delaunay Tetrahedralizations

A Delaunay tetrahedralization of $n$ vertices is exhibited for which a straight line can pass through the interiors of $\Theta(n^2)$ tetrahedra. This solves an open problem of Amenta, who asked whether a line can stab more than $O(n)$ tetrahedra. The construction generalizes to higher dimensions: in $d$ dimensions, a line can stab the interiors of $\Theta(n^{\lceil d / 2 \rceil})$ Delaunay $d$-simplices. The relationship between a Delaunay triangulation and a convex polytope yields another result: a two-dimensional slice of a $d$-dimensional $n$-vertex polytope can have $\Theta(n^{\lfloor d / 2 \rfloor})$ facets. This last result was first demonstrated by Amenta and Ziegler, but the construction given here is simpler and more intuitive.     

A Tight Bound for the Delaunay Triangulation of Points on a Polyhedron

We show that the Delaunay triangulation of a set of n points distributed nearly uniformly on a p-dimensional polyhedron (not necessarily convex) in d-dimensional Euclidean space is $O(n^{\frac{d-k+1}{p}})$ , where $k = \lceil\frac{d+1}{p+1} \rceil$ . This bound is tight in the worst case and improves on the prior upper bound for most values of?p.     

On the Complexity of Halfspace Area Queries

Given a nonconvex simple polygon $P$ with $n$ vertices, is it possible to construct a data structure which after preprocessing can answer halfspace area queries (i.e., given a line, determine the area of the portion of the polygon above the line) in $o(n)$ time? We answer negatively, proving an $\Omega(n)$ lower bound on the query time of any data structure performing this task. We then consider the offline version of the same problem: given a polygon $P$ with $n$ vertices, and $k$ query lines, we present an algorithm that computes the area of $P$ on both sides of each line in $O(k^{2/3}n^{2/3+\varepsilon}+(n+k)\polylog{n})$ time. Variants of this method allow the query of a collection of weighted polygons with or without holes, and solve several other related problems within the same time bounds.     

A Single Cell in an Arrangement of Convex Polyhedra in ${\Bbb R}^3$

We show that the combinatorial complexity of a single cell in an arrangement of k convex polyhedra in 3-space having n facets in total is , for any , thus settling a conjecture of Aronov et al. We then extend our analysis and show that the overall complexity of the zone of a low-degree algebraic surface, or of the boundary of an arbitrary convex set, in an arrangement of k convex polyhedra in 3-space with n facets in total, is also , for any . Finally, we present a deterministic algorithm that constructs a single cell in an arrangement of this kind, in time , for any .     

A Two-Dimensional Kinetic Triangulation with Near-Quadratic Topological Changes

A triangulation of a set S of points in the plane is a subdivision of the convex hull of S into triangles whose vertices are points of S. Given a set S of n points in each moving independently, we wish to maintain a triangulation of S. The triangulation needs to be updated periodically as the points in S move, so the goal is to maintain a triangulation with a small number of topological events, each being the insertion or deletion of an edge. We propose a kinetic data structure (KDS) that processes topological events with high probability if the trajectories of input points are algebraic curves of fixed degree. Each topological event can be processed in time. This is the first known KDS for maintaining a triangulation that processes a near-quadratic number of topological events, and almost matches the lower bound [1]. The number of topological events can be reduced to if only k of the points are moving.     

Extrapolation of Nyström Solutions of Boundary Integral Equations on Non-Smooth Domains

The interior Dirichlet problem for Laplace's equation on a plane polygonal region $\Omega$ with boundary $\Gamma$ may be reformulated as a second kind integral equation on $\Gamma$. This equation may be solved by the Nyström method using the composite trapezoidal rule. It is known that if the mesh has $O(n)$ points and is graded appropriately, then $O(1/n^2)$ convergence is obtained for the solution of the integral equation and the associated solution to the Dirichlet problem at any $x\in \Omega$. We present a simple extrapolation scheme which increases these rates of convergence to $O(1/n^4)$ .     

A Monte Carlo algorithm for weighted integration over

We present and analyze a new randomized algorithm for numerical computation of weighted integrals over the unbounded domain . The algorithm and its desirable theoretical properties are derived based on certain stochastic assumptions about the integrands. It is easy to implement, enjoys convergence rate, and uses only standard random number generators. Numerical results are also included.

     

Point Counting in Families of Hyperelliptic Curves

Let E _Γ be a family of hyperelliptic curves defined by , where is defined over a small finite field of odd characteristic. Then with in an extension degree n field over this small field, we present a deterministic algorithm for computing the zeta function of the curve by using Dwork deformation in rigid cohomology. The time complexity of the algorithm is and it needs bits of memory. A slight adaptation requires only space, but costs time . An implementation of this last result turns out to be quite efficient for n big enough. H. Hubrechts is a Research Assistant of the Research Foundation–Flanders (FWO–Vlaanderen).     

Preconditioning of the Stiffness Matrix of Local Refined Triangulation

A preconditioning method for the finite element stiffness matrix is given in this paper. The triangulation is refined in a subregion; the preconditioning process is composed of resolution of two regular subproblems; the condition number of the preconditioned matrix is O(1 logH/h), where H and h are mesh sizes of the unrefined and local refined triangulations respectively.     

Graph Sparsification by Universal Greedy Algorithms

Graph sparsification is to approximate an arbitrary graph by a sparse graph and is useful in many applications, such as simplification of social networks, least squares problems, and numerical solution of symmetric positive definite linear systems. In this paper, inspired by the well-known sparse signal recovery algorithm called orthogonal matching pursuit (OMP), we introduce a deterministic, greedy edge selection algorithm, which is called the universal greedy approach (UGA) for the graph sparsification problem. For a general spectral sparsification problem, e.g., the positive subset selection problem from a set of $m$ vectors in $\mathbb{R}^n$, we propose a nonnegative UGA algorithm which needs $O(mn^2+ n^3/\epsilon^2)$ time to find a $\frac{1+\epsilon/\beta}{1-\epsilon/\beta}$-spectral sparsifier with positive coefficients with sparsity at most $\lceil\frac{n}{\epsilon^2}\rceil$, where $\beta$ is the ratio between the smallest length and largest length of the vectors. The convergence of the nonnegative UGA algorithm is established. For the graph sparsification problem, another UGA algorithm is proposed which can output a $\frac{1+O(\epsilon)}{1-O(\epsilon)}$-spectral sparsifier with $\lceil\frac{n}{\epsilon^2}\rceil$ edges in $O(m+n^2/\epsilon^2)$ time from a graph with $m$ edges and $n$ vertices under some mild assumptions. This is a linear time algorithm in terms of the number of edges that the community of graph sparsification is looking for. The best result in the literature to the knowledge of the authors is the existence of a deterministic algorithm which is almost linear, i.e. $O(m^{1+o(1)})$ for some $o(1)=O(\frac{(\log\log(m))^{2/3}}{\log^{1/3}(m)})$. Finally, extensive experimental results, including applications to graph clustering and least squares regression, show the effectiveness of proposed approaches.     

High-Dimensional Shape Fitting in Linear Time

Let $P$ be a set of $n$ points in $\Re^d$. The {\em radius} of a $k$-dimensional flat ${\cal F}$ with respect to $P$, which we denote by ${\cal RD}({\cal F},P)$, is defined to be $\max_{p \in P} \mathop{\rm dist}({\cal F},p)$, where $\mathop{\rm dist}({\cal F},p)$ denotes the Euclidean distance between $p$ and its projection onto ${\cal F}$. The $k$-flat radius of $P$, which we denote by ${R^{\rm opt}_k}(P)$, is the minimum, over all $k$-dimensional flats ${\cal F}$, of ${\cal RD}({\cal F},P)$. We consider the problem of computing ${R^{\rm opt}_k}(P)$ for a given set of points $P$. We are interested in the high-dimensional case where $d$ is a part of the input and not a constant. This problem is NP-hard even for $k = 1$. We present an algorithm that, given $P$ and a parameter $0 < \eps \leq 1$, returns a $k$-flat ${\cal F}$ such that ${\cal RD}({\cal F},P) \leq (1 + \eps) {R^{\rm opt}_k}(P)$. The algorithm runs in $O(nd C_{\eps,k})$ time, where $C_{\eps,k}$ is a constant that depends only on $\eps$ and $k$. Thus the algorithm runs in time linear in the size of the point set and is a substantial improvement over previous known algorithms, whose running time is of the order of $d n^{O(k/\eps^c)}$, where $c$ is an appropriate constant.     

Greedy Algorithm Computing Minkowski Reduced Lattice Bases with Quadratic Bit Complexity of Input Vectors

The authors present an algorithm which is a modification of the Nguyen-Stehle greedy reduction algorithm due to Nguyen and Stehle in 2009. This algorithm can be used to compute the Minkowski reduced lattice bases for arbitrary rank lattices with quadratic bit complexity on the size of the input vectors. The total bit complexity of the algorithm is $O(n^2 \cdot (4n!)^n \cdot (\tfrac{{n!}} {{2^n }})^{\tfrac{n} {2}} \cdot (\tfrac{4} {3})^{\tfrac{{n(n - 1)}} {4}} \cdot (\tfrac{3} {2})^{\tfrac{{n^2 (n - 1)}} {2}} \cdot \log ^2 A) $O(n^2 \cdot (4n!)^n \cdot (\tfrac{{n!}} {{2^n }})^{\tfrac{n} {2}} \cdot (\tfrac{4} {3})^{\tfrac{{n(n - 1)}} {4}} \cdot (\tfrac{3} {2})^{\tfrac{{n^2 (n - 1)}} {2}} \cdot \log ^2 A) , where n is the rank of the lattice and A is maximal norm of the input base vectors. This is an O(log² A) algorithm which can be used to compute Minkowski reduced bases for the fixed rank lattices. A time complexity n! · 3 ⁿ (log A) ^O(1) algorithm which can be used to compute the successive minima with the help of the dual Hermite-Korkin-Zolotarev base was given by Blomer in 2000 and improved to the time complexity n! · (log A) ^O(1) by Micciancio in 2008. The algorithm in this paper is more suitable for computing the Minkowski reduced bases of low rank lattices with very large base vector sizes.     

Function Spaces in Lipschitz Domains and Optimal Rates of Convergence for Sampling

Assume that we want to recover $f : \Omega \to {\bf C}$ in the $L_r$-quasi-norm ($0 < r \le \infty$) by a linear sampling method $$ S_n f = \sum_{j=1}^n f(x^j) h_j , $$ where $h_j \in L_r(\Omega )$ and $x^j \in \Omega$ and $\Omega \subset {\bf R}^d$ is an arbitrary bounded Lipschitz domain. We assume that $f$ is from the unit ball of a Besov space $B^s_{pq} (\Omega)$ or of a Triebel--Lizorkin space $F^s_{pq} (\Omega)$ with parameters such that the space is compactly embedded into $C(\overline{\Omega})$. We prove that the optimal rate of convergence of linear sampling methods is $$ n^{ -{s}/{d} + ({1}/{p}-{1}/{r})_+} , $$ nonlinear methods do not yield a better rate. To prove this we use a result from Wendland (2001) as well as results concerning the spaces $B^s_{pq} (\Omega) $ and $F^s_{pq}(\Omega)$. Actually, it is another aim of this paper to complement the existing literature about the function spaces $B^s_{pq} (\Omega)$ and $F^s_{pq} (\Omega)$ for bounded Lipschitz domains $\Omega \subset {\bf R}^d$. In this sense, the paper is also a continuation of a paper by Triebel (2002).     

Better saddlepoint confidence intervals via bootstrap calibration

Confidence interval construction for parameters of lattice distributions is considered. By using saddlepoint formulas and bootstrap calibration, we obtain relatively short intervals and bounds with coverage errors, in contrast with and coverage errors for normal theory intervals and bounds when the population distribution is absolutely continuous. Closed form solutions are also provided for the cases of binomial and Poisson distributions. The method is illustrated by some simulation results.

     

An Asymptotical $O((k + 1)n^3L)$ Affine Scaling Algorithm for the $P_*(k)$-Matrix Linear Complementarity Problem

Based on the generalized Dikin-type direction proposed by Jansen et al in 1997, we give out in this paper a generalized Dinkin-type affine scaling algorithm for solving the $P_*(k)$-matrix linear complementarity problem (LCP). By using high-order correctors technique and rank-one updating, the iteration complexity and the total computational turn out asymptotically $O((\kappa+1)\sqrt{n}L)$ and $O((\kappa+1)n^3L)$ respectively.     

Distinct Distances in Homogeneous Sets in Euclidean Space

It is shown that every homogeneous set of n points in d-dimensional Euclidean space determines at least distinct distances for a constant c(d) > 0. In three-space the above general bound is slightly improved and it is shown that every homogeneous set of n points determines at least distinct distances.     

区间数据任意阶原点矩的估计

在生存分析和可靠性研究中, 区间数据的存在常常使得传统的统计方法无法直接使用\bd 本文从无偏转换的思想出发, 对区间数据的任意阶原点矩进行了估计\bd 当截断变量的分布密度函数已知时, 得到了一批具有强相合性(收敛速度可以达到$n^{-1/2}(\log\log n)^{1/2}$)和渐近正态性的估计量, 并通过模拟计算对这种估计方法的可行性和有效性进行了验证.     

Estimates for Fourier Coefficients of Cusp Forms in Weight Aspect

Let f be a holomorphic Hecke eigenform of weight k for the modular groupΓ = SL2(Z) and let λf(n) be the n-th normalized Fourier coefficient. In this paper, by a new estimate of the second integral moment of the symmetric square L-function related to f, the estimate 1λf(n21) x2 k2(log(x + k))6n≤x is established, which improves the previous result.     

The skin effect in vibrating systems with many concentrated masses

We address the asymptotic behaviour of the vibrations of a body occupying a domain $\Omega\subset\mathbb{R}^n, n=2,3$. The density, which depends on a small parameter $\varepsilon$\nopagenumbers\end , is of the order $O(1)$\nopagenumbers\end out of certain regions where it is $O(\varepsilon^{‐m})$\nopagenumbers\end with $m>2$\nopagenumbers\end . These regions, the concentrated masses with diameter $O(\varepsilon)$\nopagenumbers\end , are located near the boundary, at mutual distances $O(\eta)$\nopagenumbers\end , with $\eta=\eta(\varepsilon)\rightarrow 0$\nopagenumbers\end . We impose Dirichlet (resp. Neumann) conditions at the points of $\partial\Omega$\nopagenumbers\end in contact with (resp. out of) the masses. We look at the asymptotic behaviour, as $\varepsilon\rightarrow 0$\nopagenumbers\end , of the eigenvalues of order $O(1)$\nopagenumbers\end , the high frequencies, of the corresponding eigenvalue problem. We show that they accumulate on the whole positive real axis and characterize those giving rise to global vibrations of the whole system. We use the fact that the corresponding eigenfunctions, microscopically, present a skin effect in the concentrated masses. Copyright © 2001 John Wiley & Sons, Ltd.