TY - GEN
T1 - Deterministic combinatorial replacement paths and distance sensitivity oracles
AU - Alon, Noga
AU - Chechik, Shiri
AU - Cohen, Sarel
N1 - Publisher Copyright:
© Noga Alon, Shiri Chechik, and Sarel Cohen; licensed under Creative Commons License CC-BY
PY - 2019/7/1
Y1 - 2019/7/1
N2 - In this work we derandomize two central results in graph algorithms, replacement paths and distance sensitivity oracles (DSOs) matching in both cases the running time of the randomized algorithms. For the replacement paths problem, let G = (V, E) be a directed unweighted graph with n vertices and m edges and let P be a shortest path from s to t in G. The replacement paths problem is to find for every edge e ∈ P the shortest path from s to t avoiding e. Roditty and Zwick [ICALP 2005] obtained a randomized algorithm with running time of Oe(m√n). Here we provide the first deterministic algorithm for this problem, with the same Oe(m√n) time. Due to matching conditional lower bounds of Williams et al. [FOCS 2010], our deterministic combinatorial algorithm for the replacement paths problem is optimal up to polylogarithmic factors (unless the long standing bound of Oe(mn) for the combinatorial boolean matrix multiplication can be improved). This also implies a deterministic algorithm for the second simple shortest path problem in Oe(m√n) time, and a deterministic algorithm for the k-simple shortest paths problem in Oe(km√n) time (for any integer constant k > 0). For the problem of distance sensitivity oracles, let G = (V, E) be a directed graph with real-edge weights. An f-Sensitivity Distance Oracle (f-DSO) gets as input the graph G = (V, E) and a parameter f, preprocesses it into a data-structure, such that given a query (s, t, F) with s, t ∈ V and F ⊆ E ∪ V, |F| ≤ f being a set of at most f edges or vertices (failures), the query algorithm efficiently computes the distance from s to t in the graph G \ F (i.e., the distance from s to t in the graph G after removing from it the failing edges and vertices F). For weighted graphs with real edge weights, Weimann and Yuster [FOCS 2010] presented several randomized f-DSOs. In particular, they presented a combinatorial f-DSO with Oe(mn4−α) preprocessing time and subquadratic Oe(n2−2(1−α)/f) query time, giving a tradeoff between preprocessing and query time for every value of 0 < α < 1. We derandomize this result and present a combinatorial deterministic f-DSO with the same asymptotic preprocessing and query time.
AB - In this work we derandomize two central results in graph algorithms, replacement paths and distance sensitivity oracles (DSOs) matching in both cases the running time of the randomized algorithms. For the replacement paths problem, let G = (V, E) be a directed unweighted graph with n vertices and m edges and let P be a shortest path from s to t in G. The replacement paths problem is to find for every edge e ∈ P the shortest path from s to t avoiding e. Roditty and Zwick [ICALP 2005] obtained a randomized algorithm with running time of Oe(m√n). Here we provide the first deterministic algorithm for this problem, with the same Oe(m√n) time. Due to matching conditional lower bounds of Williams et al. [FOCS 2010], our deterministic combinatorial algorithm for the replacement paths problem is optimal up to polylogarithmic factors (unless the long standing bound of Oe(mn) for the combinatorial boolean matrix multiplication can be improved). This also implies a deterministic algorithm for the second simple shortest path problem in Oe(m√n) time, and a deterministic algorithm for the k-simple shortest paths problem in Oe(km√n) time (for any integer constant k > 0). For the problem of distance sensitivity oracles, let G = (V, E) be a directed graph with real-edge weights. An f-Sensitivity Distance Oracle (f-DSO) gets as input the graph G = (V, E) and a parameter f, preprocesses it into a data-structure, such that given a query (s, t, F) with s, t ∈ V and F ⊆ E ∪ V, |F| ≤ f being a set of at most f edges or vertices (failures), the query algorithm efficiently computes the distance from s to t in the graph G \ F (i.e., the distance from s to t in the graph G after removing from it the failing edges and vertices F). For weighted graphs with real edge weights, Weimann and Yuster [FOCS 2010] presented several randomized f-DSOs. In particular, they presented a combinatorial f-DSO with Oe(mn4−α) preprocessing time and subquadratic Oe(n2−2(1−α)/f) query time, giving a tradeoff between preprocessing and query time for every value of 0 < α < 1. We derandomize this result and present a combinatorial deterministic f-DSO with the same asymptotic preprocessing and query time.
KW - Derandomization
KW - Distance sensitivity oracles
KW - Replacement paths
UR - http://www.scopus.com/inward/record.url?scp=85069210780&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069210780&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.ICALP.2019.12
DO - 10.4230/LIPIcs.ICALP.2019.12
M3 - Conference contribution
AN - SCOPUS:85069210780
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 46th International Colloquium on Automata, Languages, and Programming, ICALP 2019
A2 - Baier, Christel
A2 - Chatzigiannakis, Ioannis
A2 - Flocchini, Paola
A2 - Leonardi, Stefano
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 46th International Colloquium on Automata, Languages, and Programming, ICALP 2019
Y2 - 9 July 2019 through 12 July 2019
ER -