A (5/3 +)-Approximation for unsplittable flow on a path: Placing small tasks into boxes
Artículo
Open/ Download
Publication date
2018Metadata
Show full item record
Cómo citar
Grandoni, Fabrizio
Cómo citar
A (5/3 +)-Approximation for unsplittable flow on a path: Placing small tasks into boxes
Author
Abstract
In the unsplittable flow on a path problem (UFP) we are given a
path with edge capacities and a collection of tasks. Each task is
characterized by a subpath, a profit, and a demand. Our goal is to
compute a maximum profit subset of tasks such that, for each edge
e, the total demand of selected tasks that use e does not exceed the
capacity of e. The current best polynomial-time approximation factor for this problem is 2+ε for any constant ε > 0 [Anagostopoulos
et al.-SODA 2014]. This is the best known factor even in the case of
uniform edge capacities [Călinescu et al.-IPCO 2002, TALG 2011].
These results, likewise most prior work, are based on a partition
of tasks into large and small depending on their ratio of demand
to capacity over their respective edges: these algorithms invoke
(1 + ε)-approximations for large and small tasks separately.
The known techniques do not seem to be able to combine a big
fraction of large and small tasks together (apart from some special
cases and quasi-polynomial-time algorithms). The main contribution of this paper is to overcome this critical barrier. Namely, we
present a polynomial-time algorithm that obtains roughly all profit
from the optimal large tasks plus one third of the profit from the optimal small tasks. In combination with known results, this implies
a polynomial-time (5/3 + ε)-approximation algorithm for UFP.
Our algorithm is based on two main ingredients. First, we prove
that there exist certain sub-optimal solutions where, roughly speaking, small tasks are packed into boxes. To prove that such solutions can yield high profit we introduce a horizontal slicing lemma which
yields a novel geometric interpretation of certain solutions. The
resulting boxed structure has polynomial complexity, hence cannot
be guessed directly. Therefore, our second contribution is a dynamic
program that guesses this structure (plus a packing of large and
small tasks) on the fly, while losing at most one third of the profit
of the remaining small tasks.
Indexation
Artículo de publicación SCOPUS
Identifier
URI: https://repositorio.uchile.cl/handle/2250/169376
DOI: 10.1145/3188745.3188894
ISSN: 07378017
Collections