14 research outputs found
Resolution complexity of random constraint satisfaction problems: Another half of the story
AbstractLet Cn,m2,k,t be a random constraint satisfaction problem (CSP) on n binary variables, where m constraints are selected uniformly at random from all the possible k-ary constraints each of which contains exactly t tuples of the values as its restrictions. We establish an upper bound on the constraint tightness threshold for Cn,m2,k,t to have an exponential resolution complexity. The upper bound partly answers the open problem regarding the CSP resolution complexity with the tightness between the existing upper and lower bounds [D. Mitchell, Resolution complexity of random constraints, in: Proceedings Principles and Practices of Constraint Programming—CP 2002, Springer, Berlin, 2002, pp. 295–309]
A Simple Model to Generate Hard Satisfiable Instances
In this paper, we try to further demonstrate that the models of random CSP
instances proposed by [Xu and Li, 2000; 2003] are of theoretical and practical
interest. Indeed, these models, called RB and RD, present several nice
features. First, it is quite easy to generate random instances of any arity
since no particular structure has to be integrated, or property enforced, in
such instances. Then, the existence of an asymptotic phase transition can be
guaranteed while applying a limited restriction on domain size and on
constraint tightness. In that case, a threshold point can be precisely located
and all instances have the guarantee to be hard at the threshold, i.e., to have
an exponential tree-resolution complexity. Next, a formal analysis shows that
it is possible to generate forced satisfiable instances whose hardness is
similar to unforced satisfiable ones. This analysis is supported by some
representative results taken from an intensive experimentation that we have
carried out, using complete and incomplete search methods.Comment: Proc. of 19th IJCAI, pp.337-342, Edinburgh, Scotland, 2005. For more
information, please click
http://www.nlsde.buaa.edu.cn/~kexu/papers/ijcai05-abstract.ht
Many Hard Examples in Exact Phase Transitions with Application to Generating Hard Satisfiable Instances
This paper first analyzes the resolution complexity of two random CSP models
(i.e. Model RB/RD) for which we can establish the existence of phase
transitions and identify the threshold points exactly. By encoding CSPs into
CNF formulas, it is proved that almost all instances of Model RB/RD have no
tree-like resolution proofs of less than exponential size. Thus, we not only
introduce new families of CNF formulas hard for resolution, which is a central
task of Proof-Complexity theory, but also propose models with both many hard
instances and exact phase transitions. Then, the implications of such models
are addressed. It is shown both theoretically and experimentally that an
application of Model RB/RD might be in the generation of hard satisfiable
instances, which is not only of practical importance but also related to some
open problems in cryptography such as generating one-way functions.
Subsequently, a further theoretical support for the generation method is shown
by establishing exponential lower bounds on the complexity of solving random
satisfiable and forced satisfiable instances of RB/RD near the threshold.
Finally, conclusions are presented, as well as a detailed comparison of Model
RB/RD with the Hamiltonian cycle problem and random 3-SAT, which, respectively,
exhibit three different kinds of phase transition behavior in NP-complete
problems.Comment: 19 pages, corrected mistakes in Theorems 5 and
Random Models of Very Hard 2QBF and Disjunctive Programs: An Overview
We present an overview of models of random quantified boolean formulas and their natural random disjunctive ASP program counter-parts that we have recently proposed. The models have a simple structure but also theoretical and empirical properties that make them useful for further advancement of the SAT, QBF and ASP solvers
The Satisfiability Threshold for a Seemingly Intractable Random Constraint Satisfaction Problem
We determine the exact threshold of satisfiability for random instances of a
particular NP-complete constraint satisfaction problem (CSP). This is the first
random CSP model for which we have determined a precise linear satisfiability
threshold, and for which random instances with density near that threshold
appear to be computationally difficult. More formally, it is the first random
CSP model for which the satisfiability threshold is known and which shares the
following characteristics with random k-SAT for k >= 3. The problem is
NP-complete, the satisfiability threshold occurs when there is a linear number
of clauses, and a uniformly random instance with a linear number of clauses
asymptotically almost surely has exponential resolution complexity.Comment: This is the long version of a paper that will be published in the
SIAM Journal on Discrete Mathematics. This long version includes an appendix
and a computer program. The contents of the paper are unchanged in the latest
version. The format of the arxiv submission was changed so that the computer
program will appear as an ancillary file. Some comments in the computer
program were update
Constraint programming for type inference in flexible model-driven engineering
Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance