42,309 research outputs found
Minimal Forward Checking--a Lazy Constraint Satisfaction Search Algorithm: Experimental And Theoretical Results
Many problems that occur in Artificial Intelligence and Operations Research can be naturally represented as Constraint Satisfaction Problems (CSPs). One of the most popular backtracking search algorithms used to solve CSPs is called Forward Checking (FC). FC performs a limited amount of lookahead during its search attempting to detect future inconsistencies thereby avoiding inconsistent parts of the search tree. In this thesis we describe a new backtracking search algorithm called Minimal Forward Checking (MFC) which maintains FC\u27s ability to detect inconsistencies but which is lazy in is method of doing so. We prove that MFC is sound and complete. We also prove the MFC and FC visit the same nodes in the search tree. Most significantly, we prove that MFC\u27s worst case performance in terms of number of constraint checks performed (the common measure of performance of these algorithms) is the number of constraint checks performed by FC. We then describe how the MFC algorithm can be seen as one algorithm in a family of lazy CSP search algorithms.;As theoretical results on the average case complexity for CSP search algorithms are extremely difficult to derive, empirical comparisons need to be performed. A commonly used testbed is randomly generated problems drawn from a standard model of binary CSPs at a specific location known to contain problems that are relatively hard to solve. We generalize the standard model of binary CSPs and show how to find problems in this model that are relatively hard to solve. We also show that these hard problems are of similar hardness or harder than hard problems drawn from the standard model especially as the problem size grows and the problem has a relatively sparse structure. We perform large empirical studies of many CSP search algorithms including variants of MFC and FC with non-chronological backtracking and variants of the Fail First heuristic on two testbeds of hard random problems, each drawn from one of the two models. Our empirical comparisons on both testbeds indicate that the average case performance of algorithms based on MFC are better than all the other algorithms in the comparison in terms of the number of constraint checks performed
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
The Complexity of Weighted Boolean #CSP with Mixed Signs
We give a complexity dichotomy for the problem of computing the partition
function of a weighted Boolean constraint satisfaction problem. Such a problem
is parameterized by a set of rational-valued functions, which generalize
constraints. Each function assigns a weight to every assignment to a set of
Boolean variables. Our dichotomy extends previous work in which the weight
functions were restricted to being non-negative. We represent a weight function
as a product of the form (-1)^s g, where the polynomial s determines the sign
of the weight and the non-negative function g determines its magnitude. We show
that the problem of computing the partition function (the sum of the weights of
all possible variable assignments) is in polynomial time if either every weight
function can be defined by a "pure affine" magnitude with a quadratic sign
polynomial or every function can be defined by a magnitude of "product type"
with a linear sign polynomial. In all other cases, computing the partition
function is FP^#P-complete.Comment: 24 page
The complexity of weighted boolean #CSP*
This paper gives a dichotomy theorem for the complexity of computing the partition
function of an instance of a weighted Boolean constraint satisfaction problem. The problem
is parameterized by a finite set F of nonnegative functions that may be used to assign weights to
the configurations (feasible solutions) of a problem instance. Classical constraint satisfaction problems
correspond to the special case of 0,1-valued functions. We show that computing the partition
function, i.e., the sum of the weights of all configurations, is FP#P-complete unless either (1) every
function in F is of “product type,” or (2) every function in F is “pure affine.” In the remaining cases,
computing the partition function is in P
The Complexity of Combinations of Qualitative Constraint Satisfaction Problems
The CSP of a first-order theory is the problem of deciding for a given
finite set of atomic formulas whether is satisfiable. Let
and be two theories with countably infinite models and disjoint
signatures. Nelson and Oppen presented conditions that imply decidability (or
polynomial-time decidability) of under the
assumption that and are decidable (or
polynomial-time decidable). We show that for a large class of
-categorical theories the Nelson-Oppen conditions are not
only sufficient, but also necessary for polynomial-time tractability of
(unless P=NP)
The power of linear programming for general-valued CSPs
Let , called the domain, be a fixed finite set and let , called
the valued constraint language, be a fixed set of functions of the form
, where different functions might have
different arity . We study the valued constraint satisfaction problem
parametrised by , denoted by VCSP. These are minimisation
problems given by variables and the objective function given by a sum of
functions from , each depending on a subset of the variables.
Finite-valued constraint languages contain functions that take on only rational
values and not infinite values.
Our main result is a precise algebraic characterisation of valued constraint
languages whose instances can be solved exactly by the basic linear programming
relaxation (BLP). For a valued constraint language , BLP is a decision
procedure for if and only if admits a symmetric fractional
polymorphism of every arity. For a finite-valued constraint language ,
BLP is a decision procedure if and only if admits a symmetric
fractional polymorphism of some arity, or equivalently, if admits a
symmetric fractional polymorphism of arity 2.
Using these results, we obtain tractability of several novel classes of
problems, including problems over valued constraint languages that are: (1)
submodular on arbitrary lattices; (2) -submodular on arbitrary finite
domains; (3) weakly (and hence strongly) tree-submodular on arbitrary trees.Comment: A full version of a FOCS'12 paper by the last two authors
(arXiv:1204.1079) and an ICALP'13 paper by the first author (arXiv:1207.7213)
to appear in SIAM Journal on Computing (SICOMP
- …