Search results
Results from the WOW.Com Content Network
Only the pairs (X=0, Y=4), (X=2, Y=2), and (X=4, Y=0) match the constraint C2. AC-3 then terminates, with D(X) = {0, 2, 4} and D(Y) = {0, 2, 4}. AC-3 is expressed in pseudocode as follows: Input: A set of variables X A set of domains D(x) for each variable x in X. D(x) contains vx0, vx1... vxn, the possible values of x A set of unary ...
The most popular constraint propagation method is the AC-3 algorithm, which enforces arc consistency. Local search methods are incomplete satisfiability algorithms. They may find a solution of a problem, but they may fail even if the problem is satisfiable. They work by iteratively improving a complete assignment over the variables.
Constraints with one, two, or more variables are called unary, binary, or higher-order constraints. The number of variables in a constraint is called its arity. The hidden transformation replaces each constraint with a new, hidden variable. The hidden transformation converts an arbitrary constraint satisfaction problem into a binary one.
Unary coding, [nb 1] or the unary numeral system and also sometimes called thermometer code, is an entropy encoding that represents a natural number, n, with a code of length n + 1 ( or n), usually n ones followed by a zero (if natural number is understood as non-negative integer) or with n − 1 ones followed by a zero (if natural number is understood as strictly positive integer).
Constraint propagation in constraint satisfaction problems is a typical example of a refinement model, and formula evaluation in spreadsheets are a typical example of a perturbation model. The refinement model is more general, as it does not restrict variables to have a single value, it can lead to several solutions to the same problem.
Node consistency requires that every unary constraint on a variable is satisfied by all values in the domain of the variable, and vice versa. This condition can be trivially enforced by reducing the domain of each variable to the values that satisfy all unary constraints on that variable.
JaCoP is a constraint solver for constraint satisfaction problems. It is written in Java and it is provided as a Java library. JaCoP has an interface to the MiniZinc and AMPL modeling languages. Its main focus is on ease of use, modeling power, as well as efficiency.
Adding a constraint to the store is done like in regular constraint logic programming. Checking entailment of a constraint is done via guards to clauses. Guards require a syntactic extension: a clause of concurrent constraint logic programming is written as H :- G | B where G is a constraint called the guard of the clause. Roughly speaking, a ...