Fast Augmenting Paths by Random Sampling from Residual Graphs
Name
Karger-2015-Fast augmenting.pdf
Size
295.21 KB
Format
Adobe PDF
Checksum (MD5)
05a5bc33cc517f7beb8be6fde4c8ab17
Author(s)
Karger, David R.
Levine, Matthew S.
Date Issued
March 2015
Journal
SIAM Journal on Computing
Publisher
Society for Industrial and Applied Mathematics
Citation
Karger, David R., and Matthew S. Levine. “Fast Augmenting Paths by Random Sampling from Residual Graphs.” SIAM Journal on Computing 44, no. 2 (January 2015): 320–339.
Version
Final published version
Abstract
Consider an n-vertex, m-edge, undirected graph with integral capacities and max-flow value v. We give a new [~ over O](m + nv)-time maximum flow algorithm. After assigning certain special sampling probabilities to edges in [~ over O](m)$ time, our algorithm is very simple: repeatedly find an augmenting path in a random sample of edges from the residual graph. Breaking from past work, we demonstrate that we can benefit by random sampling from directed (residual) graphs. We also slightly improve an algorithm for approximating flows of arbitrary value, finding a flow of value (1 - ε) times the maximum in [~ over O](m√n/ε) time.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1137/070705994
