Search results
Results from the WOW.Com Content Network
Notice that the actual constraint graph representing this problem must contain two edges between X and Y since C2 is undirected but the graph representation being used by AC-3 is directed. AC-3 solves the problem by first removing the non-even values from of the domain of X as required by C1, leaving D(X) = { 0, 2, 4 }.
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.
Cost transfer algorithms have been shown to be particularly efficient to solve real-world problem when soft constraints are binary or ternary (maximal arity of constraints in the problem is equal to 2 or 3). For soft constraints of large arity, cost transfer becomes a serious issue because the risk of combinatorial explosion has to be controlled.
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).
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.
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.
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 ...