اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدSensitive Distance and Reachability Oracles for Large Batch Updates
83
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In the sensitive distance oracle problem, there are three phases. We first preprocess a given directed graph $G$ with $n$ nodes and integer weights from $[-W,W]$. Second, given a single batch of $f$ edge insertions and deletions, we update the data structure. Third, given a query pair of nodes $(u,v)$, return the distance from $u$ to $v$. In the easier problem called sensitive reachability oracle problem, we only ask if there exists a directed path from $u$ to $v$. Our first result is a sensitive distance oracle with $tilde{O}(Wn^{omega+(3-omega)mu})$ preprocessing time, $tilde{O}(Wn^{2-mu}f^{2}+Wnf^{omega})$ update time, and $tilde{O}(Wn^{2-mu}f+Wnf^{2})$ query time where the parameter $muin[0,1]$ can be chosen. The data-structure requires $O(Wn^{2+mu} log n)$ bits of memory. This is the first algorithm that can handle $fgelog n$ updates. Previous results (e.g. [Demetrescu et al. SICOMP08; Bernstein and Karger SODA08 and FOCS09; Duan and Pettie SODA09; Grandoni and Williams FOCS12]) can handle at most 2 updates. When $3le flelog n$, the only non-trivial algorithm was by [Weimann and Yuster FOCS10]. When $W=tilde{O}(1)$, our algorithm simultaneously improves their preprocessing time, update time, and query time. In particular, when $f=omega(1)$, their update and query time is $Omega(n^{2-o(1)})$, while our update and query time are truly subquadratic in $n$, i.e., ours is faster by a polynomial factor of $n$. To highlight the technique, ours is the first graph algorithm that exploits the kernel basis decomposition of polynomial matrices by [Jeannerod and Villard J.Comp05; Zhou, Labahn and Storjohann J.Comp15] developed in the symbolic computation community. [...]
قيم البحث
اقرأ أيضاً
We present new and improved data structures that answer exact node-to-node distance queries in planar graphs. Such data structures are also known as distance oracles. For any directed planar graph on n nodes with non-negative lengths we obtain the fo
llowing: * Given a desired space allocation $Sin[nlglg n,n^2]$, we show how to construct in $tilde O(S)$ time a data structure of size $O(S)$ that answers distance queries in $tilde O(n/sqrt S)$ time per query. As a consequence, we obtain an improvement over the fastest algorithm for k-many distances in planar graphs whenever $kin[sqrt n,n)$. * We provide a linear-space exact distance oracle for planar graphs with query time $O(n^{1/2+eps})$ for any constant eps>0. This is the first such data structure with provable sublinear query time. * For edge lengths at least one, we provide an exact distance oracle of space $tilde O(n)$ such that for any pair of nodes at distance D the query time is $tilde O(min {D,sqrt n})$. Comparable query performance had been observed experimentally but has never been explained theoretically. Our data structures are based on the following new tool: given a non-self-crossing cycle C with $c = O(sqrt n)$ nodes, we can preprocess G in $tilde O(n)$ time to produce a data structure of size $O(n lglg c)$ that can answer the following queries in $tilde O(c)$ time: for a query node u, output the distance from u to all the nodes of C. This data structure builds on and extends a related data structure of Klein (SODA05), which reports distances to the boundary of a face, rather than a cycle. The best distance oracles for planar graphs until the current work are due to Cabello (SODA06), Djidjev (WG96), and Fakcharoenphol and Rao (FOCS01). For $sigmain(1,4/3)$ and space $S=n^sigma$, we essentially improve the query time from $n^2/S$ to $sqrt{n^2/S}$.
A (1 + eps)-approximate distance oracle for a graph is a data structure that supports approximate point-to-point shortest-path-distance queries. The most relevant measures for a distance-oracle construction are: space, query time, and preprocessing t
ime. There are strong distance-oracle constructions known for planar graphs (Thorup, JACM04) and, subsequently, minor-excluded graphs (Abraham and Gavoille, PODC06). However, these require Omega(eps^{-1} n lg n) space for n-node graphs. We argue that a very low space requirement is essential. Since modern computer architectures involve hierarchical memory (caches, primary memory, secondary memory), a high memory requirement in effect may greatly increase the actual running time. Moreover, we would like data structures that can be deployed on small mobile devices, such as handhelds, which have relatively small primary memory. In this paper, for planar graphs, bounded-genus graphs, and minor-excluded graphs we give distance-oracle constructions that require only O(n) space. The big O hides only a fixed constant, independent of epsilon and independent of genus or size of an excluded minor. The preprocessing times for our distance oracle are also faster than those for the previously known constructions. For planar graphs, the preprocessing time is O(n lg^2 n). However, our constructions have slower query times. For planar graphs, the query time is O(eps^{-2} lg^2 n). For our linear-space results, we can in fact ensure, for any delta > 0, that the space required is only 1 + delta times the space required just to represent the graph itself.
One of the most fundamental problems in computer science is the reachability problem: Given a directed graph and two vertices s and t, can s reach t via a path? We revisit existing techniques and combine them with new approaches to support a large po
rtion of reachability queries in constant time using a linear-sized reachability index. Our new algorithm OReach can be easily combined with previously developed solutions for the problem or run standalone. In a detailed experimental study, we compare a variety of algorithms with respect to their index-building and query times as well as their memory footprint on a diverse set of instances. Our experiments indicate that the query performance often depends strongly not only on the type of graph, but also on the result, i.e., reachable or unreachable. Furthermore, we show that previous algorithms are significantly sped up when combined with our new approach in almost all scenarios. Surprisingly, due to cache effects, a higher investment in space doesnt necessarily pay off: Reachability queries can often be answered even faster than single memory accesses in a precomputed full reachability matrix.
Many context-sensitive data flow analyses can be formulated as a variant of the all-pairs Dyck-CFL reachability problem, which, in general, is of sub-cubic time complexity and quadratic space complexity. Such high complexity significantly limits the
scalability of context-sensitive data flow analysis and is not affordable for analyzing large-scale software. This paper presents textsc{Flare}, a reduction from the CFL reachability problem to the conventional graph reachability problem for context-sensitive data flow analysis. This reduction allows us to benefit from recent advances in reachability indexing schemes, which often consume almost linear space for answering reachability queries in almost constant time. We have applied our reduction to a context-sensitive alias analysis and a context-sensitive information-flow analysis for C/C++ programs. Experimental results on standard benchmarks and open-source software demonstrate that we can achieve orders of magnitude speedup at the cost of only moderate space to store the indexes. The implementation of our approach is publicly available.
Distance oracles are data structures that provide fast (possibly approximate) answers to shortest-path and distance queries in graphs. The tradeoff between the space requirements and the query time of distance oracles is of particular interest and th
e main focus of this paper. In FOCS01, Thorup introduced approximate distance oracles for planar graphs. He proved that, for any eps>0 and for any planar graph on n nodes, there exists a (1+eps)-approximate distance oracle using space O(n eps^{-1} log n) such that approximate distance queries can be answered in time O(1/eps). Ten years later, we give the first improvements on the space-querytime tradeoff for planar graphs. * We give the first oracle having a space-time product with subquadratic dependency on 1/eps. For space ~O(n log n) we obtain query time ~O(1/eps) (assuming polynomial edge weights). The space shows a doubly logarithmic dependency on 1/eps only. We believe that the dependency on eps may be almost optimal. * For the case of moderate edge weights (average bounded by polylog(n), which appears to be the case for many real-world road networks), we hit a sweet spot, improving upon Thorups oracle both in terms of eps and n. Our oracle uses space ~O(n log log n) and it has query time ~O(log log log n + 1/eps). (Asymptotic notation in this abstract hides low-degree polynomials in log(1/eps) and log*(n).)
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
