Fast approximation of minimum multicast congestion – Implementation VERSUS Theory
Andreas Baltz; Anand Srivastav
RAIRO - Operations Research (2010)
- Volume: 38, Issue: 4, page 319-344
- ISSN: 0399-0559
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topBaltz, Andreas, and Srivastav, Anand. "Fast approximation of minimum multicast congestion – Implementation VERSUS Theory." RAIRO - Operations Research 38.4 (2010): 319-344. <http://eudml.org/doc/105318>.
@article{Baltz2010,
abstract = {
The problem of minimizing the maximum edge congestion in a multicast
communication network generalizes the well-known NP-hard multicommodity
flow problem. We give the presently best theoretical approximation results as
well as efficient implementations. In particular we show that for a network
with m edges and k multicast requests, an
r(1 + ε)(rOPT + exp(1)lnm)-approximation can be computed in
O(kmε-2lnklnm) time, where β bounds the time for
computing an r-approximate minimum Steiner tree. Moreover, we present a new
fast heuristic that outperforms the primal-dual approaches with respect to
both running time and objective value.
},
author = {Baltz, Andreas, Srivastav, Anand},
journal = {RAIRO - Operations Research},
keywords = {Combinatorial optimization; approximation algorithms.; approximation algorithms},
language = {eng},
month = {3},
number = {4},
pages = {319-344},
publisher = {EDP Sciences},
title = {Fast approximation of minimum multicast congestion – Implementation VERSUS Theory},
url = {http://eudml.org/doc/105318},
volume = {38},
year = {2010},
}
TY - JOUR
AU - Baltz, Andreas
AU - Srivastav, Anand
TI - Fast approximation of minimum multicast congestion – Implementation VERSUS Theory
JO - RAIRO - Operations Research
DA - 2010/3//
PB - EDP Sciences
VL - 38
IS - 4
SP - 319
EP - 344
AB -
The problem of minimizing the maximum edge congestion in a multicast
communication network generalizes the well-known NP-hard multicommodity
flow problem. We give the presently best theoretical approximation results as
well as efficient implementations. In particular we show that for a network
with m edges and k multicast requests, an
r(1 + ε)(rOPT + exp(1)lnm)-approximation can be computed in
O(kmε-2lnklnm) time, where β bounds the time for
computing an r-approximate minimum Steiner tree. Moreover, we present a new
fast heuristic that outperforms the primal-dual approaches with respect to
both running time and objective value.
LA - eng
KW - Combinatorial optimization; approximation algorithms.; approximation algorithms
UR - http://eudml.org/doc/105318
ER -
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