Sebastian Krinninger

A deterministic almost-tight distributed algorithm for approximating single-source shortest paths

We present a deterministic (1+o(1))-approximation O(n1/2+o(1)+D1+o(1))-time algorithm for solving the single-source shortest paths problem on distributed weighted networks (the CONGEST model); here n is the number of nodes in the network and D is its (hop) diameter. This is the first non-trivial deterministic algorithm for this problem. It also improves (i) the running time of the randomized (1+o(1))-approximation Õ(n1/2D1/4+D)-time algorithm of Nanongkai [STOC 2014] by a factor of as large as n1/8, and (ii) the O(є−1logє−1)-approximation factor of Lenzen and Patt-Shamir’s Õ(n1/2+є+D)-time algorithm [STOC 2013] within the same running time. Our running time matches the known time lower bound of Ω(n1/2/logn + D) [Das Sarma et al. STOC 2011] modulo some lower-order terms, thus essentially settling the status of this problem which was raised at least a decade ago [Elkin SIGACT News 2004]. It also implies a (2+o(1))-approximation O(n1/2+o(1)+D1+o(1))-time algorithm for approximating a network’s weighted diameter which almost matches the lower bound by Holzer et al. [PODC 2012]. In achieving this result, we develop two techniques which might be of independent interest and useful in other settings: (i) a deterministic process that replaces the “hitting set argument” commonly used for shortest paths computation in various settings, and (ii) a simple, deterministic, construction of an (no(1), o(1))-hop set of size O(n1+o(1)). We combine these techniques with many distributed algorithmic techniques, some of which from problems that are not directly related to shortest paths, e.g. ruling sets [Goldberg et al. STOC 1987], source detection [Lenzen, Peleg PODC 2013], and partial distance estimation [Lenzen, Patt-Shamir PODC 2015]. Our hop set construction also leads to single-source shortest paths algorithms in two other settings: (i) a (1+o(1))-approximation O(no(1))-time algorithm on congested cliques, and (ii) a (1+o(1))-approximation O(no(1)logW)-pass O(n1+o(1)logW)-space streaming algorithm, when edge weights are in {1, 2, …, W}. The first result answers an open problem in [Nanongkai, STOC 2014]. The second result partially answers an open problem raised by McGregor in 2006 [sublinear.info, Problem 14].

Sublinear-time decremental algorithms for single-source reachability and shortest paths on directed graphs

We consider dynamic algorithms for maintaining Single-Source Reachability (SSR) and approximate Single-Source Shortest Paths (SSSP) on n-node m-edge directed graphs under edge deletions (decremental algorithms). The previous fastest algorithm for SSR and SSSP goes back three decades to Even and Shiloach (JACM 1981); it has O(1) query time and O(mn) total update time (i.e., linear amortized update time if all edges are deleted). This algorithm serves as a building block for several other dynamic algorithms. The question whether its total update time can be improved is a major, long standing, open problem. In this paper, we answer this question affirmatively. We obtain a randomized algorithm which, in a simplified form, achieves an Õ(mn0.984) expected total update time for SSR and (1 + ε)-approximate SSSP, where Õ(·) hides poly log n. We also extend our algorithm to achieve roughly the same running time for Strongly Connected Components (SCC), improving the algorithm of Roditty and Zwick (FOCS 2002), and an algorithm that improves the Õ (mn log W)-time algorithm of Bernstein (STOC 2013) for approximating SSSP on weighted directed graphs, where the edge weights are integers from 1 to W. All our algorithms have constant query time in the worst case.

Dynamic Approximate All-Pairs Shortest Paths: Breaking the

We study dynamic (1 + ϵ)-approximation algorithms for the all-pairs shortest paths problem in unweighted undirected n-node m-edge graphs under edge deletions. The fastest algorithm for this problem is a randomized algorithm with a total update time of Ȏ(mn) and constant query time by Roditty and Zwick (FOCS 2004). The fastest deterministic algorithm is from a 1981 paper by Even and Shiloach (JACM 1981); it has a total update time of O(mn2) and constant query time. We improve these results as follows: (1) We present an algorithm with a total update time of Ȏ(n5/2) and constant query time that has an additive error of two in addition to the 1 + ϵ multiplicative error. This beats the previous Ȏ(mn) time when m = Ω(n3/2). Note that the additive error is unavoidable since, even in the static case, an O(n3-δ)-time (a so-called truly sub cubic) combinatorial algorithm with 1 + ϵ multiplicative error cannot have an additive error less than 2 - ϵ, unless we make a major breakthrough for Boolean matrix multiplication (Dor, Halperin and Zwick FOCS 1996) and many other long-standing problems (Vassilevska Williams and Williams FOCS 2010). The algorithm can also be turned into a (2 + ϵ)-approximation algorithm (without an additive error) with the same time guarantees, improving the recent (3 + ϵ)-approximation algorithm with Ȏ(n5/2+O(1√(log n))) running time of Bernstein and Roditty (SODA 2011) in terms of both approximation and time guarantees. (2) We present a deterministic algorithm with a total update time of Ȏ(mn) and a query time of O(log log n). The algorithm has a multiplicative error of 1 + ϵ and gives the first improved deterministic algorithm since 1981. It also answers an open question raised by Bernstein in his STOC 2013 paper. In order to achieve our results, we introduce two new techniques: (1) A lazy Even-Shiloach tree algorithm which maintains a bounded-distance shortest-paths tree on a certain type of emulator called locally persevering emulator. (2) A derandomization technique based on moving Even-Shiloach trees as a way to derandomize the standard random set argument. These techniques might be of independent interest.

O(mn)

We present faster algorithms for computing the 2-edge and 2-vertex strongly connected components of a directed graph, which are straightforward generalizations of strongly connected components. While in undirected graphs the 2-edge and 2-vertex connected components can be found in linear time, in directed graphs only rather simple

Barrier and Derandomization

O(m n)

Finding 2-Edge and 2-Vertex Strongly Connected Components in Quadratic Time

-time algorithms were known. We use a hierarchical sparsification technique to obtain algorithms that run in time

Polynomial-Time Algorithms for Energy Games with Special Weight Structures

O(n^2)

Improved Algorithms for Decremental Single-Source Reachability on Directed Graphs

. For 2-edge strongly connected components our algorithm gives the first running time improvement in 20 years. Additionally we present an

A subquadratic-time algorithm for decremental single-source shortest paths

O(m^2 / \log{n})

Dynamic Approximate All-Pairs Shortest Paths: Breaking the O(mn) Barrier and Derandomization

-time algorithm for 2-edge strongly connected components, and thus improve over the