112 research outputs found
Improved approximation for 3-dimensional matching via bounded pathwidth local search
One of the most natural optimization problems is the k-Set Packing problem,
where given a family of sets of size at most k one should select a maximum size
subfamily of pairwise disjoint sets. A special case of 3-Set Packing is the
well known 3-Dimensional Matching problem. Both problems belong to the Karp`s
list of 21 NP-complete problems. The best known polynomial time approximation
ratio for k-Set Packing is (k + eps)/2 and goes back to the work of Hurkens and
Schrijver [SIDMA`89], which gives (1.5 + eps)-approximation for 3-Dimensional
Matching. Those results are obtained by a simple local search algorithm, that
uses constant size swaps.
The main result of the paper is a new approach to local search for k-Set
Packing where only a special type of swaps is considered, which we call swaps
of bounded pathwidth. We show that for a fixed value of k one can search the
space of r-size swaps of constant pathwidth in c^r poly(|F|) time. Moreover we
present an analysis proving that a local search maximum with respect to O(log
|F|)-size swaps of constant pathwidth yields a polynomial time (k + 1 +
eps)/3-approximation algorithm, improving the best known approximation ratio
for k-Set Packing. In particular we improve the approximation ratio for
3-Dimensional Matching from 3/2 + eps to 4/3 + eps.Comment: To appear in proceedings of FOCS 201
Approximating Upper Degree-Constrained Partial Orientations
In the Upper Degree-Constrained Partial Orientation problem we are given an
undirected graph , together with two degree constraint functions
. The goal is to orient as many edges as possible,
in such a way that for each vertex the number of arcs entering is
at most , whereas the number of arcs leaving is at most .
This problem was introduced by Gabow [SODA'06], who proved it to be MAXSNP-hard
(and thus APX-hard). In the same paper Gabow presented an LP-based iterative
rounding -approximation algorithm.
Since the problem in question is a special case of the classic 3-Dimensional
Matching, which in turn is a special case of the -Set Packing problem, it is
reasonable to ask whether recent improvements in approximation algorithms for
the latter two problems [Cygan, FOCS'13; Sviridenko & Ward, ICALP'13] allow for
an improved approximation for Upper Degree-Constrained Partial Orientation. We
follow this line of reasoning and present a polynomial-time local search
algorithm with approximation ratio . Our algorithm uses a
combination of two types of rules: improving sets of bounded pathwidth from the
recent -approximation algorithm for 3-Set Packing [Cygan,
FOCS'13], and a simple rule tailor-made for the setting of partial
orientations. In particular, we exploit the fact that one can check in
polynomial time whether it is possible to orient all the edges of a given graph
[Gy\'arf\'as & Frank, Combinatorics'76].Comment: 12 pages, 1 figur
Metric Embedding via Shortest Path Decompositions
We study the problem of embedding shortest-path metrics of weighted graphs
into spaces. We introduce a new embedding technique based on low-depth
decompositions of a graph via shortest paths. The notion of Shortest Path
Decomposition depth is inductively defined: A (weighed) path graph has shortest
path decomposition (SPD) depth . General graph has an SPD of depth if it
contains a shortest path whose deletion leads to a graph, each of whose
components has SPD depth at most . In this paper we give an
-distortion embedding for graphs of SPD
depth at most . This result is asymptotically tight for any fixed ,
while for it is tight up to second order terms.
As a corollary of this result, we show that graphs having pathwidth embed
into with distortion . For
, this improves over the best previous bound of Lee and Sidiropoulos that
was exponential in ; moreover, for other values of it gives the first
embeddings whose distortion is independent of the graph size . Furthermore,
we use the fact that planar graphs have SPD depth to give a new
proof that any planar graph embeds into with distortion . Our approach also gives new results for graphs with bounded treewidth,
and for graphs excluding a fixed minor
On local search and LP and SDP relaxations for k-Set Packing
Set packing is a fundamental problem that generalises some well-known
combinatorial optimization problems and knows a lot of applications. It is
equivalent to hypergraph matching and it is strongly related to the maximum
independent set problem. In this thesis we study the k-set packing problem
where given a universe U and a collection C of subsets over U, each of
cardinality k, one needs to find the maximum collection of mutually disjoint
subsets. Local search techniques have proved to be successful in the search for
approximation algorithms, both for the unweighted and the weighted version of
the problem where every subset in C is associated with a weight and the
objective is to maximise the sum of the weights. We make a survey of these
approaches and give some background and intuition behind them. In particular,
we simplify the algebraic proof of the main lemma for the currently best
weighted approximation algorithm of Berman ([Ber00]) into a proof that reveals
more intuition on what is really happening behind the math. The main result is
a new bound of k/3 + 1 + epsilon on the integrality gap for a polynomially
sized LP relaxation for k-set packing by Chan and Lau ([CL10]) and the natural
SDP relaxation [NOTE: see page iii]. We provide detailed proofs of lemmas
needed to prove this new bound and treat some background on related topics like
semidefinite programming and the Lovasz Theta function. Finally we have an
extended discussion in which we suggest some possibilities for future research.
We discuss how the current results from the weighted approximation algorithms
and the LP and SDP relaxations might be improved, the strong relation between
set packing and the independent set problem and the difference between the
weighted and the unweighted version of the problem.Comment: There is a mistake in the following line of Theorem 17: "As an
induced subgraph of H with more edges than vertices constitutes an improving
set". Therefore, the proofs of Theorem 17, and hence Theorems 19, 23 and 24,
are false. It is still open whether these theorems are tru
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