419 research outputs found
Inapproximability of the independent set polynomial in the complex plane
We study the complexity of approximating the independent set polynomial
of a graph with maximum degree when the activity
is a complex number.
This problem is already well understood when is real using
connections to the -regular tree . The key concept in that case is
the "occupation ratio" of the tree . This ratio is the contribution to
from independent sets containing the root of the tree, divided
by itself. If is such that the occupation ratio
converges to a limit, as the height of grows, then there is an FPTAS for
approximating on a graph with maximum degree .
Otherwise, the approximation problem is NP-hard.
Unsurprisingly, the case where is complex is more challenging.
Peters and Regts identified the complex values of for which the
occupation ratio of the -regular tree converges. These values carve a
cardioid-shaped region in the complex plane. Motivated by the
picture in the real case, they asked whether marks the true
approximability threshold for general complex values .
Our main result shows that for every outside of ,
the problem of approximating on graphs with maximum degree
at most is indeed NP-hard. In fact, when is outside of
and is not a positive real number, we give the stronger result
that approximating is actually #P-hard. If is a
negative real number outside of , we show that it is #P-hard to
even decide whether , resolving in the affirmative a conjecture
of Harvey, Srivastava and Vondrak.
Our proof techniques are based around tools from complex analysis -
specifically the study of iterative multivariate rational maps
On the Complexity of the Interlace Polynomial
We consider the two-variable interlace polynomial introduced by Arratia,
Bollobas and Sorkin (2004). We develop graph transformations which allow us to
derive point-to-point reductions for the interlace polynomial. Exploiting these
reductions we obtain new results concerning the computational complexity of
evaluating the interlace polynomial at a fixed point. Regarding exact
evaluation, we prove that the interlace polynomial is #P-hard to evaluate at
every point of the plane, except on one line, where it is trivially polynomial
time computable, and four lines, where the complexity is still open. This
solves a problem posed by Arratia, Bollobas and Sorkin (2004). In particular,
three specializations of the two-variable interlace polynomial, the
vertex-nullity interlace polynomial, the vertex-rank interlace polynomial and
the independent set polynomial, are almost everywhere #P-hard to evaluate, too.
For the independent set polynomial, our reductions allow us to prove that it is
even hard to approximate at any point except at 0.Comment: 18 pages, 1 figure; new graph transformation (adding cycles) solves
some unknown points, error in the statement of the inapproximability result
fixed; a previous version has appeared in the proceedings of STACS 200
Beyond Geometry : Towards Fully Realistic Wireless Models
Signal-strength models of wireless communications capture the gradual fading
of signals and the additivity of interference. As such, they are closer to
reality than other models. However, nearly all theoretic work in the SINR model
depends on the assumption of smooth geometric decay, one that is true in free
space but is far off in actual environments. The challenge is to model
realistic environments, including walls, obstacles, reflections and anisotropic
antennas, without making the models algorithmically impractical or analytically
intractable.
We present a simple solution that allows the modeling of arbitrary static
situations by moving from geometry to arbitrary decay spaces. The complexity of
a setting is captured by a metricity parameter Z that indicates how far the
decay space is from satisfying the triangular inequality. All results that hold
in the SINR model in general metrics carry over to decay spaces, with the
resulting time complexity and approximation depending on Z in the same way that
the original results depends on the path loss term alpha. For distributed
algorithms, that to date have appeared to necessarily depend on the planarity,
we indicate how they can be adapted to arbitrary decay spaces.
Finally, we explore the dependence on Z in the approximability of core
problems. In particular, we observe that the capacity maximization problem has
exponential upper and lower bounds in terms of Z in general decay spaces. In
Euclidean metrics and related growth-bounded decay spaces, the performance
depends on the exact metricity definition, with a polynomial upper bound in
terms of Z, but an exponential lower bound in terms of a variant parameter phi.
On the plane, the upper bound result actually yields the first approximation of
a capacity-type SINR problem that is subexponential in alpha
Approximate kernel clustering
In the kernel clustering problem we are given a large positive
semi-definite matrix with and a small
positive semi-definite matrix . The goal is to find a
partition of which maximizes the quantity We study the
computational complexity of this generic clustering problem which originates in
the theory of machine learning. We design a constant factor polynomial time
approximation algorithm for this problem, answering a question posed by Song,
Smola, Gretton and Borgwardt. In some cases we manage to compute the sharp
approximation threshold for this problem assuming the Unique Games Conjecture
(UGC). In particular, when is the identity matrix the UGC
hardness threshold of this problem is exactly . We present
and study a geometric conjecture of independent interest which we show would
imply that the UGC threshold when is the identity matrix is
for every
Approximating the partition function of the ferromagnetic Potts model
We provide evidence that it is computationally difficult to approximate the
partition function of the ferromagnetic q-state Potts model when q>2.
Specifically we show that the partition function is hard for the complexity
class #RHPi_1 under approximation-preserving reducibility. Thus, it is as hard
to approximate the partition function as it is to find approximate solutions to
a wide range of counting problems, including that of determining the number of
independent sets in a bipartite graph. Our proof exploits the first order phase
transition of the "random cluster" model, which is a probability distribution
on graphs that is closely related to the q-state Potts model.Comment: Minor correction
Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation
We consider the following problem: There is a set of items (e.g., movies) and
a group of agents (e.g., passengers on a plane); each agent has some intrinsic
utility for each of the items. Our goal is to pick a set of items that
maximize the total derived utility of all the agents (i.e., in our example we
are to pick movies that we put on the plane's entertainment system).
However, the actual utility that an agent derives from a given item is only a
fraction of its intrinsic one, and this fraction depends on how the agent ranks
the item among the chosen, available, ones. We provide a formal specification
of the model and provide concrete examples and settings where it is applicable.
We show that the problem is hard in general, but we show a number of
tractability results for its natural special cases
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