393 research outputs found
Heuristic average-case analysis of the backtrack resolution of random 3-Satisfiability instances
An analysis of the average-case complexity of solving random 3-Satisfiability
(SAT) instances with backtrack algorithms is presented. We first interpret
previous rigorous works in a unifying framework based on the statistical
physics notions of dynamical trajectories, phase diagram and growth process. It
is argued that, under the action of the Davis--Putnam--Loveland--Logemann
(DPLL) algorithm, 3-SAT instances are turned into 2+p-SAT instances whose
characteristic parameters (ratio alpha of clauses per variable, fraction p of
3-clauses) can be followed during the operation, and define resolution
trajectories. Depending on the location of trajectories in the phase diagram of
the 2+p-SAT model, easy (polynomial) or hard (exponential) resolutions are
generated. Three regimes are identified, depending on the ratio alpha of the
3-SAT instance to be solved. Lower sat phase: for small ratios, DPLL almost
surely finds a solution in a time growing linearly with the number N of
variables. Upper sat phase: for intermediate ratios, instances are almost
surely satisfiable but finding a solution requires exponential time (2 ^ (N
omega) with omega>0) with high probability. Unsat phase: for large ratios,
there is almost always no solution and proofs of refutation are exponential. An
analysis of the growth of the search tree in both upper sat and unsat regimes
is presented, and allows us to estimate omega as a function of alpha. This
analysis is based on an exact relationship between the average size of the
search tree and the powers of the evolution operator encoding the elementary
steps of the search heuristic.Comment: to appear in Theoretical Computer Scienc
The dynamics of proving uncolourability of large random graphs I. Symmetric Colouring Heuristic
We study the dynamics of a backtracking procedure capable of proving
uncolourability of graphs, and calculate its average running time T for sparse
random graphs, as a function of the average degree c and the number of vertices
N. The analysis is carried out by mapping the history of the search process
onto an out-of-equilibrium (multi-dimensional) surface growth problem. The
growth exponent of the average running time is quantitatively predicted, in
agreement with simulations.Comment: 5 figure
Quantum walk speedup of backtracking algorithms
We describe a general method to obtain quantum speedups of classical
algorithms which are based on the technique of backtracking, a standard
approach for solving constraint satisfaction problems (CSPs). Backtracking
algorithms explore a tree whose vertices are partial solutions to a CSP in an
attempt to find a complete solution. Assume there is a classical backtracking
algorithm which finds a solution to a CSP on n variables, or outputs that none
exists, and whose corresponding tree contains T vertices, each vertex
corresponding to a test of a partial solution. Then we show that there is a
bounded-error quantum algorithm which completes the same task using O(sqrt(T)
n^(3/2) log n) tests. In particular, this quantum algorithm can be used to
speed up the DPLL algorithm, which is the basis of many of the most efficient
SAT solvers used in practice. The quantum algorithm is based on the use of a
quantum walk algorithm of Belovs to search in the backtracking tree. We also
discuss how, for certain distributions on the inputs, the algorithm can lead to
an exponential reduction in expected runtime.Comment: 23 pages; v2: minor changes to presentatio
Hiding Satisfying Assignments: Two are Better than One
The evaluation of incomplete satisfiability solvers depends critically on the
availability of hard satisfiable instances. A plausible source of such
instances consists of random k-SAT formulas whose clauses are chosen uniformly
from among all clauses satisfying some randomly chosen truth assignment A.
Unfortunately, instances generated in this manner tend to be relatively easy
and can be solved efficiently by practical heuristics. Roughly speaking, as the
formula's density increases, for a number of different algorithms, A acts as a
stronger and stronger attractor. Motivated by recent results on the geometry of
the space of satisfying truth assignments of random k-SAT and NAE-k-SAT
formulas, we introduce a simple twist on this basic model, which appears to
dramatically increase its hardness. Namely, in addition to forbidding the
clauses violated by the hidden assignment A, we also forbid the clauses
violated by its complement, so that both A and complement of A are satisfying.
It appears that under this "symmetrization'' the effects of the two attractors
largely cancel out, making it much harder for algorithms to find any truth
assignment. We give theoretical and experimental evidence supporting this
assertion.Comment: Preliminary version appeared in AAAI 200
A DPLL(T) Framework for Verifying Deep Neural Networks
Deep Neural Networks (DNNs) have emerged as an effective approach to tackling
real-world problems. However, like human-written software,
automatically-generated DNNs can have bugs and be attacked. This thus attracts
many recent interests in developing effective and scalable DNN verification
techniques and tools. In this work, we introduce a NeuralSAT, a new constraint
solving approach to DNN verification. The design of NeuralSAT follows the
DPLL(T) algorithm used modern SMT solving, which includes (conflict) clause
learning, abstraction, and theory solving, and thus NeuralSAT can be considered
as an SMT framework for DNNs. Preliminary results show that the NeuralSAT
prototype is competitive to the state-of-the-art. We hope, with proper
optimization and engineering, NeuralSAT will carry the power and success of
modern SAT/SMT solvers to DNN verification. NeuralSAT is avaliable from:
https://github.com/dynaroars/neuralsat-solverComment: 27 pages, 8 figures. NeuralSAT is avaliable from:
https://github.com/dynaroars/neuralsat-solve
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