We present an algorithm that computes a (1+epsilon)-approximation of the diameter of a weighted, undirected planar graph of n vertices with non-negative edge lengths in O(n log n (log n + (1/epsilon)^5)) expected time, improving upon the O(n ((1/epsilon)^4 log^4 n + 2^{O(1/epsilon)}))-time algorithm of Weimann and Yuster [ICALP 2013]. Our algorithm makes two improvements over that result: first and foremost, it replaces the exponential dependency on 1/epsilon with a polynomial one, by adapting and specializing Cabello's recent abstract-Voronoi-diagram-based technique [SODA 2017] for approximation purposes; second, it shaves off two logarithmic factors by choosing a better sequence of error parameters during recursion.
Moreover, using similar techniques, we improve the (1+epsilon)-approximate distance oracle of Gu and Xu [ISAAC 2015] by first replacing the exponential dependency on 1/epsilon on the preprocessing time and space with a polynomial one and second removing a logarithmic factor from the preprocessing time.
We address the All-Pairs Shortest Paths (APSP) problem for a number of unweighted, undirected geometric intersection graphs. We present a general reduction of the problem to static, offline intersection searching (specifically detection). As a consequence, we can solve APSP for intersection graphs of n arbitrary disks in O(n^2 log n) time, axis-aligned line segments in O(n^2 loglog n) time, arbitrary line segments in O(n^{7/3} log^{1/3} n) time, d-dimensional axis-aligned boxes in O(n^2 log^{d-1.5} n) time for d >= 2, and d-dimensional axis-aligned unit hypercubes in O(n^2 loglog n) time for d=3 and O(n^2 log^{d-3} n) time for d >= 4.
In addition, we show how to solve the Single-Source Shortest Paths (SSSP) problem in unweighted intersection graphs of axis-aligned line segments in O(n log n) time, by a reduction to dynamic orthogonal point location.
We revisit the orthogonal range searching problem and the exact l_infinity nearest neighbor searching problem for a static set of n points when the dimension d is moderately large. We give the first data structure with near linear space that achieves truly sublinear query time when the dimension is any constant multiple of log n. Specifically, the preprocessing time/space is O(n^{1+delta}) for any constant delta > 0, and the expected query time is n^{1 - 1/O(c log c)}. The data structure is simple and is based on a new "augmented, randomized, lopsided" variant of k-d trees. It matches (in fact, slightly improves) the performance of previous combinatorial algorithms that work only in the case of offline queries [Impagliazzo, Lovett, Paturi, and Schneider (2014) and Chan (SODA'15)]. It leads to faster combinatorial algorithms for all-pairs shortest paths in general weighted graphs and rectangular Boolean matrix multiplication.
In the offline case, we show that the problem can be reduced to the Boolean orthogonal vectors problem and thus admits an n^{2 - 1/O(log c)}-time non-combinatorial algorithm [Abboud, Williams, and Yu (SODA'15)]. This reduction is also simple and is based on range trees.
Finally, we use a similar approach to obtain a small improvement to Indyk's data structure [FOCS'98] for approximate l_infinity nearest neighbor search when d=c log n.
In this paper we study the all-pairs shortest paths problem in (unweighted) unit-disk graphs. The previous best solution for this problem required O(n^2 log n) time, by running the O(n log n)-time single-source shortest path algorithm of Cabello and Jejcic (2015) from every source vertex, where n is the number of vertices. We not only manage to eliminate the logarithmic factor, but also obtain the first (slightly) subquadratic algorithm for the problem, running in O(n^2 sqrt{loglog n/log n}) time. Our algorithm computes an implicit representation of all the shortest paths, and, in the same amount of time, can also compute the diameter of the graph.
We show how to solve all-pairs shortest paths on n nodes in deterministic n^3 / 2^{Omega(sqrt{log n})} time, and how to count the pairs of orthogonal vectors among n 0-1 vectors in d = c log n dimensions in deterministic n^{2 - 1/O(log c)} time. These running times essentially match the best known randomized algorithms of (Williams, STOC'14) and (Abboud, Williams, and Yu, SODA 2015) respectively, and the ability to count was open even for randomized algorithms. By reductions, these two results yield faster deterministic algorithms for many other problems. Our techniques can also be used to count k-SAT assignments on n variable formulas in 2^{n - n/O(k)} time, roughly matching the best known running times for detecting satisfiability and resolving an open problem of Santhanam (2013).
A key to our constructions is an efficient way to deterministically simulate certain probabilistic polynomials critical to the algorithms of prior work, applying epsilon-biased sets and modulus-amplifying polynomials.
We present a new combinatorial algorithm for Boolean matrix multiplication that runs in O(n^3 (loglog n)^3 / log^3 n) time. This improves the previous combinatorial algorithm by Bansal and Williams [FOCS'09] that runs in O(n^3 (loglog n)^2 / log^{9/4} n) time. Whereas Bansal and Williams' algorithm uses regularity lemmas for graphs, the new algorithm is simple and uses entirely elementary techniques: table lookup, word operations, plus a deceptively straightforward divide-and-conquer.
Our algorithm is in part inspired by a recent result of Impagliazzo, Lovett, Paturi, and Schneider (2014) on a different geometric problem, offline dominance range reporting; we improve their analysis for that problem as well.
In the first part of the paper, we reexamine the all-pairs shortest paths (APSP) problem and present a new algorithm with running time approaching O(n^3 log^3log n / log^2 n), which improves all known algorithms for general real-weighted dense graphs.
In the second part of the paper, we use fast matrix multiplication to obtain truly subcubic APSP algorithms for a large class of "geometrically weighted" graphs, where the weight of an edge is a function of the coordinates of its vertices. For example, for graphs embedded in Euclidean space of a constant dimension d, we obtain a time bound near O(n^{3-(3-w)/(2d+4)}), where w < 2.376; in two dimensions, this is O(n^{2.922}). Our framework greatly extends the previously considered case of small-integer-weighted graphs, and incidentally also yields the first truly subcubic result (near O(n^{3-(3-w)/4}) = O(n^{2.844}) time) for APSP in real-vertex-weighted graphs, as well as an improved result (near O(n^{(3+w)/2}) = O(n^{2.688}) time) for the all-pairs lightest shortest path problem for small-integer-weighted graphs.
We give subquadratic algorithms that, given two necklaces each with n beads at arbitrary positions, compute the optimal rotation of the necklaces to best align the beads. Here alignment is measured according to the l_p norm of the vector of distances between pairs of beads from opposite necklaces in the best perfect matching. We show surprisingly different results for p=1, p=2, and p=infty. For p=2, we reduce the problem to standard convolution, while for p=infty and p=1, we reduce the problem to (min,+) convolution and (median,+) convolution. Then we solve the latter two convolution problems in subquadratic time, which are interesting results in their own right. These results shed some light on the classic sorting X+Y problem, because the convolutions can be viewed as computing order statistics on the antidiagonals of the X+Y matrix. All of our algorithms run in o(n^2) time, whereas the obvious algorithms for these problems run in Theta(n^2) time.
We revisit the all-pairs-shortest-paths problem for an unweighted undirected graph with n vertices and m edges. We present new algorithms with the following running times:
We describe an O(n^3 / log n)-time algorithm for the all-pairs-shortest-paths problem for a real-weighted directed graph with n vertices. This slightly improves a series of previous, slightly subcubic algorithms by Fredman (1976), Takaoka (1992), Dobosiewicz (1990), Han (2004), Takaoka (2004), and Zwick (2004). The new algorithm is surprisingly simple and different from previous ones.
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