3,679 research outputs found
A Dichotomy Theorem for Polynomial Evaluation
A dichotomy theorem for counting problems due to Creignou and Hermann states
that or any nite set S of logical relations, the counting problem #SAT(S) is
either in FP, or #P-complete. In the present paper we show a dichotomy theorem
for polynomial evaluation. That is, we show that for a given set S, either
there exists a VNP-complete family of polynomials associated to S, or the
associated families of polynomials are all in VP. We give a concise
characterization of the sets S that give rise to "easy" and "hard" polynomials.
We also prove that several problems which were known to be #P-complete under
Turing reductions only are in fact #P-complete under many-one reductions
Fine-grained dichotomies for the Tutte plane and Boolean #CSP
Jaeger, Vertigan, and Welsh [15] proved a dichotomy for the complexity of
evaluating the Tutte polynomial at fixed points: The evaluation is #P-hard
almost everywhere, and the remaining points admit polynomial-time algorithms.
Dell, Husfeldt, and Wahl\'en [9] and Husfeldt and Taslaman [12], in combination
with Curticapean [7], extended the #P-hardness results to tight lower bounds
under the counting exponential time hypothesis #ETH, with the exception of the
line , which was left open. We complete the dichotomy theorem for the
Tutte polynomial under #ETH by proving that the number of all acyclic subgraphs
of a given -vertex graph cannot be determined in time unless
#ETH fails.
Another dichotomy theorem we strengthen is the one of Creignou and Hermann
[6] for counting the number of satisfying assignments to a constraint
satisfaction problem instance over the Boolean domain. We prove that all
#P-hard cases are also hard under #ETH. The main ingredient is to prove that
the number of independent sets in bipartite graphs with vertices cannot be
computed in time unless #ETH fails. In order to prove our results,
we use the block interpolation idea by Curticapean [7] and transfer it to
systems of linear equations that might not directly correspond to
interpolation.Comment: 16 pages, 1 figur
A Dichotomy Theorem for Homomorphism Polynomials
In the present paper we show a dichotomy theorem for the complexity of
polynomial evaluation. We associate to each graph H a polynomial that encodes
all graphs of a fixed size homomorphic to H. We show that this family is
computable by arithmetic circuits in constant depth if H has a loop or no edge
and that it is hard otherwise (i.e., complete for VNP, the arithmetic class
related to #P). We also demonstrate the hardness over the rational field of cut
eliminator, a polynomial defined by B\"urgisser which is known to be neither VP
nor VNP-complete in the field of two elements, if VP is not equal to VNP (VP is
the class of polynomials computable by arithmetic circuit of polynomial size)
Polynomial-time Solvable #CSP Problems via Algebraic Models and Pfaffian Circuits
A Pfaffian circuit is a tensor contraction network where the edges are
labeled with changes of bases in such a way that a very specific set of
combinatorial properties are satisfied. By modeling the permissible changes of
bases as systems of polynomial equations, and then solving via computation, we
are able to identify classes of 0/1 planar #CSP problems solvable in
polynomial-time via the Pfaffian circuit evaluation theorem (a variant of L.
Valiant's Holant Theorem). We present two different models of 0/1 variables,
one that is possible under a homogeneous change of basis, and one that is
possible under a heterogeneous change of basis only. We enumerate a series of
1,2,3, and 4-arity gates/cogates that represent constraints, and define a class
of constraints that is possible under the assumption of a ``bridge" between two
particular changes of bases. We discuss the issue of planarity of Pfaffian
circuits, and demonstrate possible directions in algebraic computation for
designing a Pfaffian tensor contraction network fragment that can simulate a
swap gate/cogate. We conclude by developing the notion of a decomposable
gate/cogate, and discuss the computational benefits of this definition
On the Computational Complexity of Non-dictatorial Aggregation
We investigate when non-dictatorial aggregation is possible from an
algorithmic perspective, where non-dictatorial aggregation means that the votes
cast by the members of a society can be aggregated in such a way that the
collective outcome is not simply the choices made by a single member of the
society. We consider the setting in which the members of a society take a
position on a fixed collection of issues, where for each issue several
different alternatives are possible, but the combination of choices must belong
to a given set of allowable voting patterns. Such a set is called a
possibility domain if there is an aggregator that is non-dictatorial, operates
separately on each issue, and returns values among those cast by the society on
each issue. We design a polynomial-time algorithm that decides, given a set
of voting patterns, whether or not is a possibility domain. Furthermore, if
is a possibility domain, then the algorithm constructs in polynomial time
such a non-dictatorial aggregator for . We then show that the question of
whether a Boolean domain is a possibility domain is in NLOGSPACE. We also
design a polynomial-time algorithm that decides whether is a uniform
possibility domain, that is, whether admits an aggregator that is
non-dictatorial even when restricted to any two positions for each issue. As in
the case of possibility domains, the algorithm also constructs in polynomial
time a uniform non-dictatorial aggregator, if one exists. Then, we turn our
attention to the case where is given implicitly, either as the set of
assignments satisfying a propositional formula, or as a set of consistent
evaluations of an sequence of propositional formulas. In both cases, we provide
bounds to the complexity of deciding if is a (uniform) possibility domain.Comment: 21 page
Model Counting of Query Expressions: Limitations of Propositional Methods
Query evaluation in tuple-independent probabilistic databases is the problem
of computing the probability of an answer to a query given independent
probabilities of the individual tuples in a database instance. There are two
main approaches to this problem: (1) in `grounded inference' one first obtains
the lineage for the query and database instance as a Boolean formula, then
performs weighted model counting on the lineage (i.e., computes the probability
of the lineage given probabilities of its independent Boolean variables); (2)
in methods known as `lifted inference' or `extensional query evaluation', one
exploits the high-level structure of the query as a first-order formula.
Although it is widely believed that lifted inference is strictly more powerful
than grounded inference on the lineage alone, no formal separation has
previously been shown for query evaluation. In this paper we show such a formal
separation for the first time.
We exhibit a class of queries for which model counting can be done in
polynomial time using extensional query evaluation, whereas the algorithms used
in state-of-the-art exact model counters on their lineages provably require
exponential time. Our lower bounds on the running times of these exact model
counters follow from new exponential size lower bounds on the kinds of d-DNNF
representations of the lineages that these model counters (either explicitly or
implicitly) produce. Though some of these queries have been studied before, no
non-trivial lower bounds on the sizes of these representations for these
queries were previously known.Comment: To appear in International Conference on Database Theory (ICDT) 201
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