77 research outputs found
Computation with Polynomial Equations and Inequalities arising in Combinatorial Optimization
The purpose of this note is to survey a methodology to solve systems of
polynomial equations and inequalities. The techniques we discuss use the
algebra of multivariate polynomials with coefficients over a field to create
large-scale linear algebra or semidefinite programming relaxations of many
kinds of feasibility or optimization questions. We are particularly interested
in problems arising in combinatorial optimization.Comment: 28 pages, survey pape
General Adiabatic Evolution with a Gap Condition
We consider the adiabatic regime of two parameters evolution semigroups
generated by linear operators that are analytic in time and satisfy the
following gap condition for all times: the spectrum of the generator consists
in finitely many isolated eigenvalues of finite algebraic multiplicity, away
from the rest of the spectrum. The restriction of the generator to the spectral
subspace corresponding to the distinguished eigenvalues is not assumed to be
diagonalizable. The presence of eigenilpotents in the spectral decomposition of
the generator forbids the evolution to follow the instantaneous eigenprojectors
of the generator in the adiabatic limit. Making use of superadiabatic
renormalization, we construct a different set of time-dependent projectors,
close to the instantaneous eigeprojectors of the generator in the adiabatic
limit, and an approximation of the evolution semigroup which intertwines
exactly between the values of these projectors at the initial and final times.
Hence, the evolution semigroup follows the constructed set of projectors in the
adiabatic regime, modulo error terms we control
Finding polynomial loop invariants for probabilistic programs
Quantitative loop invariants are an essential element in the verification of
probabilistic programs. Recently, multivariate Lagrange interpolation has been
applied to synthesizing polynomial invariants. In this paper, we propose an
alternative approach. First, we fix a polynomial template as a candidate of a
loop invariant. Using Stengle's Positivstellensatz and a transformation to a
sum-of-squares problem, we find sufficient conditions on the coefficients.
Then, we solve a semidefinite programming feasibility problem to synthesize the
loop invariants. If the semidefinite program is unfeasible, we backtrack after
increasing the degree of the template. Our approach is semi-complete in the
sense that it will always lead us to a feasible solution if one exists and
numerical errors are small. Experimental results show the efficiency of our
approach.Comment: accompanies an ATVA 2017 submissio
Sharper and Simpler Nonlinear Interpolants for Program Verification
Interpolation of jointly infeasible predicates plays important roles in
various program verification techniques such as invariant synthesis and CEGAR.
Intrigued by the recent result by Dai et al.\ that combines real algebraic
geometry and SDP optimization in synthesis of polynomial interpolants, the
current paper contributes its enhancement that yields sharper and simpler
interpolants. The enhancement is made possible by: theoretical observations in
real algebraic geometry; and our continued fraction-based algorithm that rounds
off (potentially erroneous) numerical solutions of SDP solvers. Experiment
results support our tool's effectiveness; we also demonstrate the benefit of
sharp and simple interpolants in program verification examples
SAT Modulo Linear Arithmetic for Solving Polynomial
Polynomial constraint solving plays a prominent role in several areas of
hardware and software analysis and verification, e.g., termination proving, program
invariant generation and hybrid system verification, to name a few. In this paper we
propose a new method for solving non-linear constraints based on encoding the problem
into an SMT problem considering only linear arithmetic. Unlike other existing methods,
our method focuses on proving satisfiability of the constraints rather than on proving
unsatisfiability, which is more relevant in several applications as we illustrate with
several examples. Nevertheless, we also present new techniques based on the analysis
of unsatisfiable cores that allow one to efficiently prove unsatisfiability too for a broad
class of problems. The power of our approach is demonstrated by means of extensive
experiments comparing our prototype with state-of-the-art tools on benchmarks taken
both from the academic and the industrial world
LNCS
We address the problem of analyzing the reachable set of a polynomial nonlinear continuous system by over-approximating the flowpipe of its dynamics. The common approach to tackle this problem is to perform a numerical integration over a given time horizon based on Taylor expansion and interval arithmetic. However, this method results to be very conservative when there is a large difference in speed between trajectories as time progresses. In this paper, we propose to use combinations of barrier functions, which we call piecewise barrier tube (PBT), to over-approximate flowpipe. The basic idea of PBT is that for each segment of a flowpipe, a coarse box which is big enough to contain the segment is constructed using sampled simulation and then in the box we compute by linear programming a set of barrier functions (called barrier tube or BT for short) which work together to form a tube surrounding the flowpipe. The benefit of using PBT is that (1) BT is independent of time and hence can avoid being stretched and deformed by time; and (2) a small number of BTs can form a tight over-approximation for the flowpipe, which means that the computation required to decide whether the BTs intersect the unsafe set can be reduced significantly. We implemented a prototype called PBTS in C++. Experiments on some benchmark systems show that our approach is effective
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
Semidefinite Characterization and Computation of Real Radical Ideals
For an ideal given by a set of generators, a new
semidefinite characterization of its real radical is
presented, provided it is zero-dimensional (even if is not). Moreover we
propose an algorithm using numerical linear algebra and semidefinite
optimization techniques, to compute all (finitely many) points of the real
variety as well as a set of generators of the real radical
ideal. The latter is obtained in the form of a border or Gr\"obner basis. The
algorithm is based on moment relaxations and, in contrast to other existing
methods, it exploits the real algebraic nature of the problem right from the
beginning and avoids the computation of complex components.Comment: 41 page
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