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Register a new userAdaptive Planar Point Location
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Added by
Siu-Wing Cheng
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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We present self-adjusting data structures for answering point location queries in convex and connected subdivisions. Let $n$ be the number of vertices in a convex or connected subdivision. Our structures use $O(n)$ space. For any convex subdivision $S$, our method processes any online query sequence $sigma$ in $O(mathrm{OPT} + n)$ time, where $mathrm{OPT}$ is the minimum time required by any linear decision tree for answering point location queries in $S$ to process $sigma$. For connected subdivisions, the processing time is $O(mathrm{OPT} + n + |sigma|log(log^* n))$. In both cases, the time bound includes the $O(n)$ preprocessing time.
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We study how to dynamize the Trapezoidal Search Tree - a well known randomized point location structure for planar subdivisions of kinetic line segments. Our approach naturally extends incremental leaf-level insertions to recursive methods and allows adaptation for the online setting. Moreover, the dynamization carries over to the Trapezoidal Search DAG, offering a linear sized data structure with logarithmic point location costs as a by-product. On a set $S$ of non-crossing segments, each update performs expected ${mathcal O}(log^2|S|)$ operations. We demonstrate the practicality of our method with an open-source implementation, based on the Computational Geometry Algorithms Library, and experiments on the update performance.
We propose a dynamic data structure for the distribution-sensitive point location problem. Suppose that there is a fixed query distribution in $mathbb{R}^2$, and we are given an oracle that can return in $O(1)$ time the probability of a query point falling into a polygonal region of constant complexity. We can maintain a convex subdivision $cal S$ with $n$ vertices such that each query is answered in $O(mathrm{OPT})$ expected time, where OPT is the minimum expected time of the best linear decision tree for point location in $cal S$. The space and construction time are $O(nlog^2 n)$. An update of $cal S$ as a mixed sequence of $k$ edge insertions and deletions takes $O(klog^5 n)$ amortized time. As a corollary, the randomized incremental construction of the Voronoi diagram of $n$ sites can be performed in $O(nlog^5 n)$ expected time so that, during the incremental construction, a nearest neighbor query at any time can be answered optimally with respect to the intermediate Voronoi diagram at that time.
Given a finite set $X subset mathbb{R}^d$ and a binary linear classifier $c: mathbb{R}^d to {0,1}$, how many queries of the form $c(x)$ are required to learn the label of every point in $X$? Known as textit{point location}, this problem has inspired over 35 years of research in the pursuit of an optimal algorithm. Building on the prior work of Kane, Lovett, and Moran (ICALP 2018), we provide the first nearly optimal solution, a randomized linear decision tree of depth $tilde{O}(dlog(|X|))$, improving on the previous best of $tilde{O}(d^2log(|X|))$ from Ezra and Sharir (Discrete and Computational Geometry, 2019). As a corollary, we also provide the first nearly optimal algorithm for actively learning halfspaces in the membership query model. En route to these results, we prove a novel characterization of Barthes Theorem (Inventiones Mathematicae, 1998) of independent interest. In particular, we show that $X$ may be transformed into approximate isotropic position if and only if there exists no $k$-dimensional subspace with more than a $k/d$-fraction of $X$, and provide a similar characterization for exact isotropic position.
We study dynamic planar point location in the External Memory Model or Disk Access Model (DAM). Previous work in this model achieves polylog query and polylog amortized update time. We present a data structure with $O( log_B^2 N)$ query time and $O(frac{1}{ B^{1-epsilon}} log_B N)$ amortized update time, where $N$ is the number of segments, $B$ the block size and $epsilon$ is a small positive constant, under the assumption that all faces have constant size. This is a $B^{1-epsilon}$ factor faster for updates than the fastest previous structure, and brings the cost of insertion and deletion down to subconstant amortized time for reasonable choices of $N$ and $B$. Our structure solves the problem of vertical ray-shooting queries among a dynamic set of interior-disjoint line segments; this is well-known to solve dynamic planar point location for a connected subdivision of the plane with faces of constant size.
For a fixed virtual scene (=collection of simplices) S and given observer position p, how many elements of S are weakly visible (i.e. not fully occluded by others) from p? The present work explores the trade-off between query time and preprocessing space for these quantities in 2D: exactly, in the approximate deterministic, and in the probabilistic sense. We deduce the EXISTENCE of an O(m^2/n^2) space data structure for S that, given p and time O(log n), allows to approximate the ratio of occluded segments up to arbitrary constant absolute error; here m denotes the size of the Visibility Graph--which may be quadratic, but typically is just linear in the size n of the scene S. On the other hand, we present a data structure CONSTRUCTIBLE in O(n*log(n)+m^2*polylog(n)/k) preprocessing time and space with similar approximation properties and query time O(k*polylog n), where k<n is an arbitrary parameter. We describe an implementation of this approach and demonstrate the practical benefit of the parameter k to trade memory for query time in an empirical evaluation on three classes of benchmark scenes.
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