8 research outputs found
Finding Disjoint Paths on Edge-Colored Graphs: More Tractability Results
The problem of finding the maximum number of vertex-disjoint uni-color paths
in an edge-colored graph (called MaxCDP) has been recently introduced in
literature, motivated by applications in social network analysis. In this paper
we investigate how the complexity of the problem depends on graph parameters
(namely the number of vertices to remove to make the graph a collection of
disjoint paths and the size of the vertex cover of the graph), which makes
sense since graphs in social networks are not random and have structure. The
problem was known to be hard to approximate in polynomial time and not
fixed-parameter tractable (FPT) for the natural parameter. Here, we show that
it is still hard to approximate, even in FPT-time. Finally, we introduce a new
variant of the problem, called MaxCDDP, whose goal is to find the maximum
number of vertex-disjoint and color-disjoint uni-color paths. We extend some of
the results of MaxCDP to this new variant, and we prove that unlike MaxCDP,
MaxCDDP is already hard on graphs at distance two from disjoint paths.Comment: Journal version in JOC
Independent Set, Induced Matching, and Pricing: Connections and Tight (Subexponential Time) Approximation Hardnesses
We present a series of almost settled inapproximability results for three
fundamental problems. The first in our series is the subexponential-time
inapproximability of the maximum independent set problem, a question studied in
the area of parameterized complexity. The second is the hardness of
approximating the maximum induced matching problem on bounded-degree bipartite
graphs. The last in our series is the tight hardness of approximating the
k-hypergraph pricing problem, a fundamental problem arising from the area of
algorithmic game theory. In particular, assuming the Exponential Time
Hypothesis, our two main results are:
- For any r larger than some constant, any r-approximation algorithm for the
maximum independent set problem must run in at least
2^{n^{1-\epsilon}/r^{1+\epsilon}} time. This nearly matches the upper bound of
2^{n/r} (Cygan et al., 2008). It also improves some hardness results in the
domain of parameterized complexity (e.g., Escoffier et al., 2012 and Chitnis et
al., 2013)
- For any k larger than some constant, there is no polynomial time min
(k^{1-\epsilon}, n^{1/2-\epsilon})-approximation algorithm for the k-hypergraph
pricing problem, where n is the number of vertices in an input graph. This
almost matches the upper bound of min (O(k), \tilde O(\sqrt{n})) (by Balcan and
Blum, 2007 and an algorithm in this paper).
We note an interesting fact that, in contrast to n^{1/2-\epsilon} hardness
for polynomial-time algorithms, the k-hypergraph pricing problem admits
n^{\delta} approximation for any \delta >0 in quasi-polynomial time. This puts
this problem in a rare approximability class in which approximability
thresholds can be improved significantly by allowing algorithms to run in
quasi-polynomial time.Comment: The full version of FOCS 201
Completely inapproximable monotone and antimonotone parameterized problems
We prove that weighted circuit satisfiability for monotone or antimonotone circuits has no fixed-parameter tractable approximation algorithm with any approximation ratio function ρ, unless FPT≠W[1]. In particular, not having such an fpt-approximation algorithm implies that these problems have no polynomial-time approximation algorithms with ratio ρ(OPT) for any nontrivial function ρ. © 2012 Elsevier Inc
A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
Parameterization and approximation are two popular ways of coping with
NP-hard problems. More recently, the two have also been combined to derive many
interesting results. We survey developments in the area both from the
algorithmic and hardness perspectives, with emphasis on new techniques and
potential future research directions