1,797 research outputs found
An approximation trichotomy for Boolean #CSP
We give a trichotomy theorem for the complexity of approximately counting the
number of satisfying assignments of a Boolean CSP instance. Such problems are
parameterised by a constraint language specifying the relations that may be
used in constraints. If every relation in the constraint language is affine
then the number of satisfying assignments can be exactly counted in polynomial
time. Otherwise, if every relation in the constraint language is in the
co-clone IM_2 from Post's lattice, then the problem of counting satisfying
assignments is complete with respect to approximation-preserving reductions in
the complexity class #RH\Pi_1. This means that the problem of approximately
counting satisfying assignments of such a CSP instance is equivalent in
complexity to several other known counting problems, including the problem of
approximately counting the number of independent sets in a bipartite graph. For
every other fixed constraint language, the problem is complete for #P with
respect to approximation-preserving reductions, meaning that there is no fully
polynomial randomised approximation scheme for counting satisfying assignments
unless NP=RP
Generalized Satisfiability Problems via Operator Assignments
Schaefer introduced a framework for generalized satisfiability problems on
the Boolean domain and characterized the computational complexity of such
problems. We investigate an algebraization of Schaefer's framework in which the
Fourier transform is used to represent constraints by multilinear polynomials
in a unique way. The polynomial representation of constraints gives rise to a
relaxation of the notion of satisfiability in which the values to variables are
linear operators on some Hilbert space. For the case of constraints given by a
system of linear equations over the two-element field, this relaxation has
received considerable attention in the foundations of quantum mechanics, where
such constructions as the Mermin-Peres magic square show that there are systems
that have no solutions in the Boolean domain, but have solutions via operator
assignments on some finite-dimensional Hilbert space. We obtain a complete
characterization of the classes of Boolean relations for which there is a gap
between satisfiability in the Boolean domain and the relaxation of
satisfiability via operator assignments. To establish our main result, we adapt
the notion of primitive-positive definability (pp-definability) to our setting,
a notion that has been used extensively in the study of constraint satisfaction
problems. Here, we show that pp-definability gives rise to gadget reductions
that preserve satisfiability gaps. We also present several additional
applications of this method. In particular and perhaps surprisingly, we show
that the relaxed notion of pp-definability in which the quantified variables
are allowed to range over operator assignments gives no additional expressive
power in defining Boolean relations
Generalized satisfiability problems via operator assignments
Schaefer introduced a framework for generalized satisfiability problems on the Boolean domain and characterized the computational complexity of such problems. We investigate an algebraization of Schaefer's framework in which the Fourier transform is used to represent constraints by multilinear polynomials in a unique way. This representation of constraints gives rise to a relaxation of the notion of satisfiability in which the values to variables are linear operators on some Hilbert space. For constraints given by a system of linear equations over the two-element field, earlier work in the foundations of quantum mechanics has shown that there are systems that have no solutions in the Boolean domain, but have solutions via operator assignments on some finite-dimensional Hilbert space. Our main result is a complete characterization of the classes of Boolean relations for which there is a gap between satisfiability in the Boolean domain and the relaxation of satisfiability via operator assignments.Peer ReviewedPostprint (published version
The complexity of counting locally maximal satisfying assignments of Boolean CSPs
We investigate the computational complexity of the problem of counting the
maximal satisfying assignments of a Constraint Satisfaction Problem (CSP) over
the Boolean domain {0,1}. A satisfying assignment is maximal if any new
assignment which is obtained from it by changing a 0 to a 1 is unsatisfying.
For each constraint language Gamma, #MaximalCSP(Gamma) denotes the problem of
counting the maximal satisfying assignments, given an input CSP with
constraints in Gamma. We give a complexity dichotomy for the problem of exactly
counting the maximal satisfying assignments and a complexity trichotomy for the
problem of approximately counting them. Relative to the problem #CSP(Gamma),
which is the problem of counting all satisfying assignments, the maximal
version can sometimes be easier but never harder. This finding contrasts with
the recent discovery that approximately counting maximal independent sets in a
bipartite graph is harder (under the usual complexity-theoretic assumptions)
than counting all independent sets.Comment: V2 adds contextual material relating the results obtained here to
earlier work in a different but related setting. The technical content is
unchanged. V3 (this version) incorporates minor revisions. The title has been
changed to better reflect what is novel in this work. This version has been
accepted for publication in Theoretical Computer Science. 19 page
Optimization Modulo Theories with Linear Rational Costs
In the contexts of automated reasoning (AR) and formal verification (FV),
important decision problems are effectively encoded into Satisfiability Modulo
Theories (SMT). In the last decade efficient SMT solvers have been developed
for several theories of practical interest (e.g., linear arithmetic, arrays,
bit-vectors). Surprisingly, little work has been done to extend SMT to deal
with optimization problems; in particular, we are not aware of any previous
work on SMT solvers able to produce solutions which minimize cost functions
over arithmetical variables. This is unfortunate, since some problems of
interest require this functionality.
In the work described in this paper we start filling this gap. We present and
discuss two general procedures for leveraging SMT to handle the minimization of
linear rational cost functions, combining SMT with standard minimization
techniques. We have implemented the procedures within the MathSAT SMT solver.
Due to the absence of competitors in the AR, FV and SMT domains, we have
experimentally evaluated our implementation against state-of-the-art tools for
the domain of linear generalized disjunctive programming (LGDP), which is
closest in spirit to our domain, on sets of problems which have been previously
proposed as benchmarks for the latter tools. The results show that our tool is
very competitive with, and often outperforms, these tools on these problems,
clearly demonstrating the potential of the approach.Comment: Submitted on january 2014 to ACM Transactions on Computational Logic,
currently under revision. arXiv admin note: text overlap with arXiv:1202.140
An Instantiation-Based Approach for Solving Quantified Linear Arithmetic
This paper presents a framework to derive instantiation-based decision
procedures for satisfiability of quantified formulas in first-order theories,
including its correctness, implementation, and evaluation. Using this framework
we derive decision procedures for linear real arithmetic (LRA) and linear
integer arithmetic (LIA) formulas with one quantifier alternation. Our
procedure can be integrated into the solving architecture used by typical SMT
solvers. Experimental results on standardized benchmarks from model checking,
static analysis, and synthesis show that our implementation of the procedure in
the SMT solver CVC4 outperforms existing tools for quantified linear
arithmetic
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