Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF or MPI-SWS or Local Campus Event Calendar

<< Previous Entry Next Entry >> New Event Entry Edit this Entry Login to DB (to update, delete)
What and Who
Title:Near-Optimal Approximate Shortest Paths and Transshipment in Distributed and Streaming Models
Speaker:Ruben Becker
coming from:Max-Planck-Institut für Informatik - D1
Speakers Bio:
Event Type:AG1 Mittagsseminar (own work)
Visibility:D1, D2, D3, D4, D5, RG1, SWS, MMCI
We use this to send out email in the morning.
Level:AG Audience
Date, Time and Location
Date:Thursday, 12 October 2017
Duration:30 Minutes
Building:E1 4
We present a method for solving the shortest transshipment problem—also known as uncapa- citated minimum cost flow—up to a multiplicative error of 1 + ε in undirected graphs with polynomially bounded integer edge weights using a tailored gradient descent algorithm. Our gradient descent algorithm takes ε^(−3) polylog n iterations, and in each iteration it needs to solve an instance of the transshipment problem up to a multiplicative error of polylog n, where n is the number of nodes. In particular, this allows us to perform a single iteration by computing a solution on a sparse spanner of logarithmic stretch. Using a careful white-box analysis, we can further extend the method to finding approximate solutions for the single-source shortest paths

(SSSP) problem. As a consequence, we improve prior work by obtaining the following results: 1. Broadcast congest model: (1+ε)-approximate SSSP using O ̃(ε^(−O(1))(√n+D)) rounds, where
D is the (hop) diameter of the network.
2. Broadcast congested clique model: (1+ε)-approximate shortest transshipment and SSSP using
O ̃(ε^(−O(1))) rounds.
3. Multipass streaming model: (1+ε)-approximate shortest transshipment and SSSP using O ̃(n) space and O ̃(ε^(−O(1))) passes.
The previously fastest SSSP algorithms for these models leverage sparse hop sets. We bypass the hop set construction; computing a spanner is sufficient with our method. The above bounds assume non-negative integer edge weights that are polynomially bounded in n; for general non- negative weights, running times scale with the logarithm of the maximum ratio between non-zero weights. In case of asymmetric costs for pushing flow through an edge in opposite directions, running times scale with the maximum ratio between the costs of both directions over all edges.

Name(s):Ruben Becker
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):

Created by:Ruben Becker, 09/29/2017 10:09 AMLast modified by:Uwe Brahm/MPII/DE, 10/12/2017 07:01 AM
  • Ruben Becker, 09/29/2017 10:09 AM -- Created document.