185 research outputs found
A Survey of Satisfiability Modulo Theory
Satisfiability modulo theory (SMT) consists in testing the satisfiability of
first-order formulas over linear integer or real arithmetic, or other theories.
In this survey, we explain the combination of propositional satisfiability and
decision procedures for conjunctions known as DPLL(T), and the alternative
"natural domain" approaches. We also cover quantifiers, Craig interpolants,
polynomial arithmetic, and how SMT solvers are used in automated software
analysis.Comment: Computer Algebra in Scientific Computing, Sep 2016, Bucharest,
Romania. 201
Proof Generation from Delta-Decisions
We show how to generate and validate logical proofs of unsatisfiability from
delta-complete decision procedures that rely on error-prone numerical
algorithms. Solving this problem is important for ensuring correctness of the
decision procedures. At the same time, it is a new approach for automated
theorem proving over real numbers. We design a first-order calculus, and
transform the computational steps of constraint solving into logic proofs,
which are then validated using proof-checking algorithms. As an application, we
demonstrate how proofs generated from our solver can establish many nonlinear
lemmas in the the formal proof of the Kepler Conjecture.Comment: Appeared in SYNASC'1
Adapting Real Quantifier Elimination Methods for Conflict Set Computation
The satisfiability problem in real closed fields is decidable. In the context
of satisfiability modulo theories, the problem restricted to conjunctive sets
of literals, that is, sets of polynomial constraints, is of particular
importance. One of the central problems is the computation of good explanations
of the unsatisfiability of such sets, i.e.\ obtaining a small subset of the
input constraints whose conjunction is already unsatisfiable. We adapt two
commonly used real quantifier elimination methods, cylindrical algebraic
decomposition and virtual substitution, to provide such conflict sets and
demonstrate the performance of our method in practice
ACL2 Proofs of Nonlinear Inequalities with Imandra
We present a proof-producing integration of ACL2 and Imandra for proving
nonlinear inequalities. This leverages a new Imandra interface exposing its
nonlinear decision procedures. The reasoning takes place over the reals, but
the proofs produced are valid over the rationals and may be run in both ACL2
and ACL2(r). The ACL2 proofs Imandra constructs are extracted from
Positivstellensatz refutations, a real algebraic analogue of the
Nullstellensatz, and are found using convex optimization.Comment: In Proceedings ACL2-2023, arXiv:2311.0837
Fatal Degeneracy in the Semidefinite Programming Approach to the Decision of Polynomial Inequalities
In order to verify programs or hybrid systems, one often needs to prove that
certain formulas are unsatisfiable. In this paper, we consider conjunctions of
polynomial inequalities over the reals. Classical algorithms for deciding these
not only have high complexity, but also provide no simple proof of
unsatisfiability. Recently, a reduction of this problem to semidefinite
programming and numerical resolution has been proposed. In this article, we
show how this reduction generally produces degenerate problems on which
numerical methods stumble
Tight Size-Degree Bounds for Sums-of-Squares Proofs
We exhibit families of -CNF formulas over variables that have
sums-of-squares (SOS) proofs of unsatisfiability of degree (a.k.a. rank)
but require SOS proofs of size for values of from
constant all the way up to for some universal constant.
This shows that the running time obtained by using the Lasserre
semidefinite programming relaxations to find degree- SOS proofs is optimal
up to constant factors in the exponent. We establish this result by combining
-reductions expressible as low-degree SOS derivations with the
idea of relativizing CNF formulas in [Kraj\'i\v{c}ek '04] and [Dantchev and
Riis'03], and then applying a restriction argument as in [Atserias, M\"uller,
and Oliva '13] and [Atserias, Lauria, and Nordstr\"om '14]. This yields a
generic method of amplifying SOS degree lower bounds to size lower bounds, and
also generalizes the approach in [ALN14] to obtain size lower bounds for the
proof systems resolution, polynomial calculus, and Sherali-Adams from lower
bounds on width, degree, and rank, respectively
On the Generation of Positivstellensatz Witnesses in Degenerate Cases
One can reduce the problem of proving that a polynomial is nonnegative, or
more generally of proving that a system of polynomial inequalities has no
solutions, to finding polynomials that are sums of squares of polynomials and
satisfy some linear equality (Positivstellensatz). This produces a witness for
the desired property, from which it is reasonably easy to obtain a formal proof
of the property suitable for a proof assistant such as Coq. The problem of
finding a witness reduces to a feasibility problem in semidefinite programming,
for which there exist numerical solvers. Unfortunately, this problem is in
general not strictly feasible, meaning the solution can be a convex set with
empty interior, in which case the numerical optimization method fails.
Previously published methods thus assumed strict feasibility; we propose a
workaround for this difficulty. We implemented our method and illustrate its
use with examples, including extractions of proofs to Coq.Comment: To appear in ITP 201
Deciding the consistency of non-linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings
We present a new algorithm for determining the satisfiability of conjunctions
of non-linear polynomial constraints over the reals, which can be used as a
theory solver for satisfiability modulo theory (SMT) solving for non-linear
real arithmetic. The algorithm is a variant of Cylindrical Algebraic
Decomposition (CAD) adapted for satisfiability, where solution candidates
(sample points) are constructed incrementally, either until a satisfying sample
is found or sufficient samples have been sampled to conclude unsatisfiability.
The choice of samples is guided by the input constraints and previous
conflicts.
The key idea behind our new approach is to start with a partial sample;
demonstrate that it cannot be extended to a full sample; and from the reasons
for that rule out a larger space around the partial sample, which build up
incrementally into a cylindrical algebraic covering of the space. There are
similarities with the incremental variant of CAD, the NLSAT method of Jovanovic
and de Moura, and the NuCAD algorithm of Brown; but we present worked examples
and experimental results on a preliminary implementation to demonstrate the
differences to these, and the benefits of the new approach
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