162 research outputs found
Bidimensionality and Geometric Graphs
In this paper we use several of the key ideas from Bidimensionality to give a
new generic approach to design EPTASs and subexponential time parameterized
algorithms for problems on classes of graphs which are not minor closed, but
instead exhibit a geometric structure. In particular we present EPTASs and
subexponential time parameterized algorithms for Feedback Vertex Set, Vertex
Cover, Connected Vertex Cover, Diamond Hitting Set, on map graphs and unit disk
graphs, and for Cycle Packing and Minimum-Vertex Feedback Edge Set on unit disk
graphs. Our results are based on the recent decomposition theorems proved by
Fomin et al [SODA 2011], and our algorithms work directly on the input graph.
Thus it is not necessary to compute the geometric representations of the input
graph. To the best of our knowledge, these results are previously unknown, with
the exception of the EPTAS and a subexponential time parameterized algorithm on
unit disk graphs for Vertex Cover, which were obtained by Marx [ESA 2005] and
Alber and Fiala [J. Algorithms 2004], respectively.
We proceed to show that our approach can not be extended in its full
generality to more general classes of geometric graphs, such as intersection
graphs of unit balls in R^d, d >= 3. Specifically we prove that Feedback Vertex
Set on unit-ball graphs in R^3 neither admits PTASs unless P=NP, nor
subexponential time algorithms unless the Exponential Time Hypothesis fails.
Additionally, we show that the decomposition theorems which our approach is
based on fail for disk graphs and that therefore any extension of our results
to disk graphs would require new algorithmic ideas. On the other hand, we prove
that our EPTASs and subexponential time algorithms for Vertex Cover and
Connected Vertex Cover carry over both to disk graphs and to unit-ball graphs
in R^d for every fixed d
Polynomial-Time Approximation Schemes for Independent Packing Problems on Fractionally Tree-Independence-Number-Fragile Graphs
We investigate a relaxation of the notion of treewidth-fragility, namely tree-independence-number-fragility. In particular, we obtain polynomial-time approximation schemes for independent packing problems on fractionally tree-independence-number-fragile graph classes. Our approach unifies and extends several known polynomial-time approximation schemes on seemingly unrelated graph classes, such as classes of intersection graphs of fat objects in a fixed dimension or proper minor-closed classes. We also study the related notion of layered tree-independence number, a relaxation of layered treewidth
A Weakly-Robust PTAS for Minimum Clique Partition in Unit Disk Graphs
We consider the problem of partitioning the set of vertices of a given unit
disk graph (UDG) into a minimum number of cliques. The problem is NP-hard and
various constant factor approximations are known, with the current best ratio
of 3. Our main result is a {\em weakly robust} polynomial time approximation
scheme (PTAS) for UDGs expressed with edge-lengths, it either (i) computes a
clique partition or (ii) gives a certificate that the graph is not a UDG; for
the case (i) that it computes a clique partition, we show that it is guaranteed
to be within (1+\eps) ratio of the optimum if the input is UDG; however if
the input is not a UDG it either computes a clique partition as in case (i)
with no guarantee on the quality of the clique partition or detects that it is
not a UDG. Noting that recognition of UDG's is NP-hard even if we are given
edge lengths, our PTAS is a weakly-robust algorithm. Our algorithm can be
transformed into an O(\frac{\log^* n}{\eps^{O(1)}}) time distributed PTAS.
We consider a weighted version of the clique partition problem on vertex
weighted UDGs that generalizes the problem. We note some key distinctions with
the unweighted version, where ideas useful in obtaining a PTAS breakdown. Yet,
surprisingly, it admits a (2+\eps)-approximation algorithm for the weighted
case where the graph is expressed, say, as an adjacency matrix. This improves
on the best known 8-approximation for the {\em unweighted} case for UDGs
expressed in standard form.Comment: 21 pages, 9 figure
A QPTAS for Maximum Weight Independent Set of Polygons with Polylogarithmically Many Vertices
The Maximum Weight Independent Set of Polygons problem is a fundamental
problem in computational geometry. Given a set of weighted polygons in the
2-dimensional plane, the goal is to find a set of pairwise non-overlapping
polygons with maximum total weight. Due to its wide range of applications, the
MWISP problem and its special cases have been extensively studied both in the
approximation algorithms and the computational geometry community. Despite a
lot of research, its general case is not well-understood. Currently the best
known polynomial time algorithm achieves an approximation ratio of n^(epsilon)
[Fox and Pach, SODA 2011], and it is not even clear whether the problem is
APX-hard. We present a (1+epsilon)-approximation algorithm, assuming that each
polygon in the input has at most a polylogarithmic number of vertices. Our
algorithm has quasi-polynomial running time.
We use a recently introduced framework for approximating maximum weight
independent set in geometric intersection graphs. The framework has been used
to construct a QPTAS in the much simpler case of axis-parallel rectangles. We
extend it in two ways, to adapt it to our much more general setting. First, we
show that its technical core can be reduced to the case when all input polygons
are triangles. Secondly, we replace its key technical ingredient which is a
method to partition the plane using only few edges such that the objects
stemming from the optimal solution are evenly distributed among the resulting
faces and each object is intersected only a few times. Our new procedure for
this task is not more complex than the original one, and it can handle the
arising difficulties due to the arbitrary angles of the polygons. Note that
already this obstacle makes the known analysis for the above framework fail.
Also, in general it is not well understood how to handle this difficulty by
efficient approximation algorithms
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