TY - JOUR
T1 - Submodular unsplittable flow on trees
AU - Adamaszek, Anna
AU - Chalermsook, Parinya
AU - Ene, Alina
AU - Wiese, Andreas
N1 - Publisher Copyright:
© 2018, The Author(s).
PY - 2018/11/1
Y1 - 2018/11/1
N2 - We study the Unsplittable Flow problem (UFP) on trees with a submodular objective function. The input to this problem is a tree with edge capacities and a collection of tasks, each characterized by a source node, a sink node, and a demand. A subset of the tasks is feasible if the tasks can simultaneously send their demands from the source to the sink without violating the edge capacities. The goal is to select a feasible subset of the tasks that maximizes a submodular objective function. Our main result is an O(klog n) -approximation algorithm for Submodular UFP on trees where k denotes the pathwidth of the given tree. Since every tree has pathwidth O(log n) , we obtain an O(log 2n) approximation for arbitrary trees. This is the first non-trivial approximation guarantee for the problem, matching the best known approximation ratio for UFP on trees with a linear objective function. Our main technical contribution is a new geometric relaxation for UFP on trees that builds on the recent work of Bonsma et al. (2014, FOCS), Anagnostopoulos et al. (Amazing 2+ϵ approximation for unsplittable flow on a path, SIAM, pp 26–41, 2014) for UFP on paths with a linear objective. Our relaxation is very structured, so it can be combined with the contention resolution framework of Chekuri et al. (2009, STOC). Our approach is robust and extends to several related problems, such as UFP with bag constraints and the Storage Allocation Problem. Additionally, we study the special case of UFP on trees with a linear objective and upward instances where, for each task, the source node is a descendant of the sink node. Such instances generalize UFP on paths. We build on the work of Bansal et al. (A quasi-PTAS for unsplittable flow on line graphs, ACM, pp 721–729, 2006) for UFP on paths and obtain a QPTAS for upward instances when the input data is quasi-polynomially bounded. We complement this result by showing that, unlike the path setting, upward instances are APX-hard if the input data is arbitrary.
AB - We study the Unsplittable Flow problem (UFP) on trees with a submodular objective function. The input to this problem is a tree with edge capacities and a collection of tasks, each characterized by a source node, a sink node, and a demand. A subset of the tasks is feasible if the tasks can simultaneously send their demands from the source to the sink without violating the edge capacities. The goal is to select a feasible subset of the tasks that maximizes a submodular objective function. Our main result is an O(klog n) -approximation algorithm for Submodular UFP on trees where k denotes the pathwidth of the given tree. Since every tree has pathwidth O(log n) , we obtain an O(log 2n) approximation for arbitrary trees. This is the first non-trivial approximation guarantee for the problem, matching the best known approximation ratio for UFP on trees with a linear objective function. Our main technical contribution is a new geometric relaxation for UFP on trees that builds on the recent work of Bonsma et al. (2014, FOCS), Anagnostopoulos et al. (Amazing 2+ϵ approximation for unsplittable flow on a path, SIAM, pp 26–41, 2014) for UFP on paths with a linear objective. Our relaxation is very structured, so it can be combined with the contention resolution framework of Chekuri et al. (2009, STOC). Our approach is robust and extends to several related problems, such as UFP with bag constraints and the Storage Allocation Problem. Additionally, we study the special case of UFP on trees with a linear objective and upward instances where, for each task, the source node is a descendant of the sink node. Such instances generalize UFP on paths. We build on the work of Bansal et al. (A quasi-PTAS for unsplittable flow on line graphs, ACM, pp 721–729, 2006) for UFP on paths and obtain a QPTAS for upward instances when the input data is quasi-polynomially bounded. We complement this result by showing that, unlike the path setting, upward instances are APX-hard if the input data is arbitrary.
KW - Approximation algorithms
KW - Submodular functions
KW - Unsplittable flow problem
UR - http://www.scopus.com/inward/record.url?scp=85040577996&partnerID=8YFLogxK
U2 - 10.1007/s10107-017-1218-4
DO - 10.1007/s10107-017-1218-4
M3 - Article
AN - SCOPUS:85040577996
SN - 0025-5610
VL - 172
SP - 565
EP - 589
JO - Mathematical Programming
JF - Mathematical Programming
IS - 1-2
ER -