63 research outputs found
Finding Connected Dense -Subgraphs
Given a connected graph on vertices and a positive integer ,
a subgraph of on vertices is called a -subgraph in . We design
combinatorial approximation algorithms for finding a connected -subgraph in
such that its density is at least a factor
of the density of the densest -subgraph
in (which is not necessarily connected). These particularly provide the
first non-trivial approximations for the densest connected -subgraph problem
on general graphs
Detecting High Log-Densities -- an O(n^1/4) Approximation for Densest k-Subgraph
In the Densest k-Subgraph problem, given a graph G and a parameter k, one
needs to find a subgraph of G induced on k vertices that contains the largest
number of edges. There is a significant gap between the best known upper and
lower bounds for this problem. It is NP-hard, and does not have a PTAS unless
NP has subexponential time algorithms. On the other hand, the current best
known algorithm of Feige, Kortsarz and Peleg, gives an approximation ratio of
n^(1/3-epsilon) for some specific epsilon > 0 (estimated at around 1/60).
We present an algorithm that for every epsilon > 0 approximates the Densest
k-Subgraph problem within a ratio of n^(1/4+epsilon) in time n^O(1/epsilon). In
particular, our algorithm achieves an approximation ratio of O(n^1/4) in time
n^O(log n). Our algorithm is inspired by studying an average-case version of
the problem where the goal is to distinguish random graphs from graphs with
planted dense subgraphs. The approximation ratio we achieve for the general
case matches the distinguishing ratio we obtain for this planted problem.
At a high level, our algorithms involve cleverly counting appropriately
defined trees of constant size in G, and using these counts to identify the
vertices of the dense subgraph. Our algorithm is based on the following
principle. We say that a graph G(V,E) has log-density alpha if its average
degree is Theta(|V|^alpha). The algorithmic core of our result is a family of
algorithms that output k-subgraphs of nontrivial density whenever the
log-density of the densest k-subgraph is larger than the log-density of the
host graph.Comment: 23 page
The Densest k-Subhypergraph Problem
The Densest -Subgraph (DS) problem, and its corresponding minimization
problem Smallest -Edge Subgraph (SES), have come to play a central role
in approximation algorithms. This is due both to their practical importance,
and their usefulness as a tool for solving and establishing approximation
bounds for other problems. These two problems are not well understood, and it
is widely believed that they do not an admit a subpolynomial approximation
ratio (although the best known hardness results do not rule this out).
In this paper we generalize both DS and SES from graphs to hypergraphs.
We consider the Densest -Subhypergraph problem (given a hypergraph ,
find a subset of vertices so as to maximize the number of
hyperedges contained in ) and define the Minimum -Union problem (given a
hypergraph, choose of the hyperedges so as to minimize the number of
vertices in their union). We focus in particular on the case where all
hyperedges have size 3, as this is the simplest non-graph setting. For this
case we provide an -approximation (for arbitrary constant )
for Densest -Subhypergraph and an -approximation for
Minimum -Union. We also give an -approximation for Minimum
-Union in general hypergraphs. Finally, we examine the interesting special
case of interval hypergraphs (instances where the vertices are a subset of the
natural numbers and the hyperedges are intervals of the line) and prove that
both problems admit an exact polynomial time solution on these instances.Comment: 21 page
On Supermodular Contracts and Dense Subgraphs
We study the combinatorial contract design problem, introduced and studied by
Dutting et. al. (2021, 2022), in both the single and multi-agent settings.
Prior work has examined the problem when the principal's utility function is
submodular in the actions chosen by the agent(s).
We complement this emerging literature with an examination of the problem
when the principal's utility is supermodular.
In the single-agent setting, we obtain a strongly polynomial time algorithm
for the optimal contract.
This stands in contrast to the NP-hardness of the problem with submodular
principal utility due to Dutting et. al. (2021).
This result has two technical components, the first of which applies beyond
supermodular or submodular utilities.
This result strengthens and simplifies analogous enumeration algorithms from
Dutting et. al. (2021), and applies to any nondecreasing valuation function for
the principal.
Second, we show that supermodular valuations lead to a polynomial number of
breakpoints, analogous to a similar result by Dutting et. al. (2021) for gross
substitutes valuations.
In the multi-agent setting, we obtain a mixed bag of positive and negative
results.
First, we show that it is NP-hard to obtain any finite multiplicative
approximation, or an additive FPTAS.
This stands in contrast to the submodular case, where efficient computation
of approximately optimal contracts was shown by Dutting et. al. (2022).
Second, we derive an additive PTAS for the problem in the instructive special
case of graph-based supermodular valuations, and equal costs.
En-route to this result, we discover an intimate connection between the
multi-agent contract problem and the notorious k-densest subgraph problem.
We build on and combine techniques from the literature on dense subgraph
problems to obtain our additive PTAS.Comment: 31 pages, 2 figure
Planted Models for the Densest k-Subgraph Problem
Given an undirected graph G, the Densest k-subgraph problem (DkS) asks to compute a set S ? V of cardinality |S| ? k such that the weight of edges inside S is maximized. This is a fundamental NP-hard problem whose approximability, inspite of many decades of research, is yet to be settled. The current best known approximation algorithm due to Bhaskara et al. (2010) computes a ?(n^{1/4 + ?}) approximation in time n^{?(1/?)}, for any ? > 0.
We ask what are some "easier" instances of this problem? We propose some natural semi-random models of instances with a planted dense subgraph, and study approximation algorithms for computing the densest subgraph in them. These models are inspired by the semi-random models of instances studied for various other graph problems such as the independent set problem, graph partitioning problems etc. For a large range of parameters of these models, we get significantly better approximation factors for the Densest k-subgraph problem. Moreover, our algorithm recovers a large part of the planted solution
Interdiction Problems on Planar Graphs
Interdiction problems are leader-follower games in which the leader is
allowed to delete a certain number of edges from the graph in order to
maximally impede the follower, who is trying to solve an optimization problem
on the impeded graph. We introduce approximation algorithms and strong
NP-completeness results for interdiction problems on planar graphs. We give a
multiplicative -approximation for the maximum matching
interdiction problem on weighted planar graphs. The algorithm runs in
pseudo-polynomial time for each fixed . We also show that
weighted maximum matching interdiction, budget-constrained flow improvement,
directed shortest path interdiction, and minimum perfect matching interdiction
are strongly NP-complete on planar graphs. To our knowledge, our
budget-constrained flow improvement result is the first planar NP-completeness
proof that uses a one-vertex crossing gadget.Comment: 25 pages, 9 figures. Extended abstract in APPROX-RANDOM 201
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