6,433 research outputs found
An Approximate Kernel for Connected Feedback Vertex Set
The Feedback Vertex Set problem is a fundamental computational problem which has been the subject of intensive study in various domains of algorithmics. In this problem, one is given an undirected graph G and an integer k as input. The objective is to determine whether at most k vertices can be deleted from G such that the resulting graph is acyclic. The study of preprocessing algorithms for this problem has a long and rich history, culminating in the quadratic kernelization of Thomasse [SODA 2010].
However, it is known that when the solution is required to induce a connected subgraph (such a set is called a connected feedback vertex set), a polynomial kernelization is unlikely to exist and the problem is NP-hard to approximate below a factor of 2 (assuming the Unique Games Conjecture).
In this paper, we show that if one is interested in only preserving approximate solutions (even of quality arbitrarily close to the optimum), then there is a drastic improvement in our ability to preprocess this problem. Specifically, we prove that for every fixed 0<epsilon<1, graph G, and k in N, the following holds:
There is a polynomial time computable graph G\u27 of size k^O(1) such that for every c >= 1, any c-approximate connected feedback vertex set of G\u27 of size at most k is a c * (1+epsilon)-approximate connected feedback vertex set of G.
Our result adds to the set of approximate kernelization algorithms introduced by Lokshtanov et al. [STOC 2017]. As a consequence of our main result, we show that Connected Feedback Vertex Set can be approximated within a factor min{OPT^O(1),n^(1-delta)} in polynomial time for some delta>0
Approximate Turing Kernelization for Problems Parameterized by Treewidth
We extend the notion of lossy kernelization, introduced by Lokshtanov et al.
[STOC 2017], to approximate Turing kernelization. An -approximate
Turing kernel for a parameterized optimization problem is a polynomial-time
algorithm that, when given access to an oracle that outputs -approximate
solutions in time, obtains an -approximate solution to
the considered problem, using calls to the oracle of size at most for
some function that only depends on the parameter.
Using this definition, we show that Independent Set parameterized by
treewidth has a -approximate Turing kernel with
vertices, answering an open question posed by
Lokshtanov et al. [STOC 2017]. Furthermore, we give
-approximate Turing kernels for the following graph problems
parameterized by treewidth: Vertex Cover, Edge Clique Cover, Edge-Disjoint
Triangle Packing and Connected Vertex Cover.
We generalize the result for Independent Set and Vertex Cover, by showing
that all graph problems that we will call "friendly" admit
-approximate Turing kernels of polynomial size when
parameterized by treewidth. We use this to obtain approximate Turing kernels
for Vertex-Disjoint -packing for connected graphs , Clique Cover,
Feedback Vertex Set and Edge Dominating Set
Lossy Kernelization
In this paper we propose a new framework for analyzing the performance of
preprocessing algorithms. Our framework builds on the notion of kernelization
from parameterized complexity. However, as opposed to the original notion of
kernelization, our definitions combine well with approximation algorithms and
heuristics. The key new definition is that of a polynomial size
-approximate kernel. Loosely speaking, a polynomial size
-approximate kernel is a polynomial time pre-processing algorithm that
takes as input an instance to a parameterized problem, and outputs
another instance to the same problem, such that . Additionally, for every , a -approximate solution
to the pre-processed instance can be turned in polynomial time into a
-approximate solution to the original instance .
Our main technical contribution are -approximate kernels of
polynomial size for three problems, namely Connected Vertex Cover, Disjoint
Cycle Packing and Disjoint Factors. These problems are known not to admit any
polynomial size kernels unless . Our approximate
kernels simultaneously beat both the lower bounds on the (normal) kernel size,
and the hardness of approximation lower bounds for all three problems. On the
negative side we prove that Longest Path parameterized by the length of the
path and Set Cover parameterized by the universe size do not admit even an
-approximate kernel of polynomial size, for any , unless
. In order to prove this lower bound we need to combine
in a non-trivial way the techniques used for showing kernelization lower bounds
with the methods for showing hardness of approximationComment: 58 pages. Version 2 contain new results: PSAKS for Cycle Packing and
approximate kernel lower bounds for Set Cover and Hitting Set parameterized
by universe siz
Tree Deletion Set has a Polynomial Kernel (but no OPT^O(1) approximation)
In the Tree Deletion Set problem the input is a graph G together with an
integer k. The objective is to determine whether there exists a set S of at
most k vertices such that G-S is a tree. The problem is NP-complete and even
NP-hard to approximate within any factor of OPT^c for any constant c. In this
paper we give a O(k^4) size kernel for the Tree Deletion Set problem. To the
best of our knowledge our result is the first counterexample to the
"conventional wisdom" that kernelization algorithms automatically provide
approximation algorithms with approximation ratio close to the size of the
kernel. An appealing feature of our kernelization algorithm is a new algebraic
reduction rule that we use to handle the instances on which Tree Deletion Set
is hard to approximate
Feedback Vertex Set Inspired Kernel for Chordal Vertex Deletion
Given a graph and a parameter , the Chordal Vertex Deletion (CVD)
problem asks whether there exists a subset of size at most
that hits all induced cycles of size at least 4. The existence of a
polynomial kernel for CVD was a well-known open problem in the field of
Parameterized Complexity. Recently, Jansen and Pilipczuk resolved this question
affirmatively by designing a polynomial kernel for CVD of size
, and asked whether one can design a kernel of size
. While we do not completely resolve this question, we design a
significantly smaller kernel of size , inspired by the
-size kernel for Feedback Vertex Set. Furthermore, we introduce the
notion of the independence degree of a vertex, which is our main conceptual
contribution
Meta-Kernelization using Well-Structured Modulators
Kernelization investigates exact preprocessing algorithms with performance
guarantees. The most prevalent type of parameters used in kernelization is the
solution size for optimization problems; however, also structural parameters
have been successfully used to obtain polynomial kernels for a wide range of
problems. Many of these parameters can be defined as the size of a smallest
modulator of the given graph into a fixed graph class (i.e., a set of vertices
whose deletion puts the graph into the graph class). Such parameters admit the
construction of polynomial kernels even when the solution size is large or not
applicable. This work follows up on the research on meta-kernelization
frameworks in terms of structural parameters.
We develop a class of parameters which are based on a more general view on
modulators: instead of size, the parameters employ a combination of rank-width
and split decompositions to measure structure inside the modulator. This allows
us to lift kernelization results from modulator-size to more general
parameters, hence providing smaller kernels. We show (i) how such large but
well-structured modulators can be efficiently approximated, (ii) how they can
be used to obtain polynomial kernels for any graph problem expressible in
Monadic Second Order logic, and (iii) how they allow the extension of previous
results in the area of structural meta-kernelization
Hitting and Harvesting Pumpkins
The "c-pumpkin" is the graph with two vertices linked by c>0 parallel edges.
A c-pumpkin-model in a graph G is a pair A,B of disjoint subsets of vertices of
G, each inducing a connected subgraph of G, such that there are at least c
edges in G between A and B. We focus on covering and packing c-pumpkin-models
in a given graph: On the one hand, we provide an FPT algorithm running in time
2^O(k) n^O(1) deciding, for any fixed c>0, whether all c-pumpkin-models can be
covered by at most k vertices. This generalizes known single-exponential FPT
algorithms for Vertex Cover and Feedback Vertex Set, which correspond to the
cases c=1,2 respectively. On the other hand, we present a O(log
n)-approximation algorithm for both the problems of covering all
c-pumpkin-models with a smallest number of vertices, and packing a maximum
number of vertex-disjoint c-pumpkin-models.Comment: v2: several minor change
Parameterized Approximation Schemes for Steiner Trees with Small Number of Steiner Vertices
We study the Steiner Tree problem, in which a set of terminal vertices needs
to be connected in the cheapest possible way in an edge-weighted graph. This
problem has been extensively studied from the viewpoint of approximation and
also parametrization. In particular, on one hand Steiner Tree is known to be
APX-hard, and W[2]-hard on the other, if parameterized by the number of
non-terminals (Steiner vertices) in the optimum solution. In contrast to this
we give an efficient parameterized approximation scheme (EPAS), which
circumvents both hardness results. Moreover, our methods imply the existence of
a polynomial size approximate kernelization scheme (PSAKS) for the considered
parameter.
We further study the parameterized approximability of other variants of
Steiner Tree, such as Directed Steiner Tree and Steiner Forest. For neither of
these an EPAS is likely to exist for the studied parameter: for Steiner Forest
an easy observation shows that the problem is APX-hard, even if the input graph
contains no Steiner vertices. For Directed Steiner Tree we prove that
approximating within any function of the studied parameter is W[1]-hard.
Nevertheless, we show that an EPAS exists for Unweighted Directed Steiner Tree,
but a PSAKS does not. We also prove that there is an EPAS and a PSAKS for
Steiner Forest if in addition to the number of Steiner vertices, the number of
connected components of an optimal solution is considered to be a parameter.Comment: 23 pages, 6 figures An extended abstract appeared in proceedings of
STACS 201
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