Constraint Functional Programming - significado y definición. Qué es Constraint Functional Programming
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Qué (quién) es Constraint Functional Programming - definición

PROGRAMMING PARADIGM WHICH COMBINES LOGIC PROGRAMMING AND CONSTRAINT SATISFACTION
Constraint Logic Programmimg; Constraint store; Constraint-logic programming; Finite constraint; Finite domain constraint; Constraint Logic Programming; CLP(FD)

constraint functional programming      
<programming> (CFP) functional programming plus constraints. (2002-06-08)
pure functional language         
PROGRAMMING PARADIGM THAT TREATS ALL COMPUTATION AS THE EVALUATION OF MATHEMATICAL FUNCTIONS
Pure functional language; Functional purity; Purely functional language; Purely functional programming language; Pure functional programming; Pure functional
purely functional language         
PROGRAMMING PARADIGM THAT TREATS ALL COMPUTATION AS THE EVALUATION OF MATHEMATICAL FUNCTIONS
Pure functional language; Functional purity; Purely functional language; Purely functional programming language; Pure functional programming; Pure functional
<language> A language that supports only {functional programming} and does not allow functions to have side-effects. Program execution consists of evaluation of an expression and all subexpressions are {referentially transparent}. (2003-03-25)

Wikipedia

Constraint logic programming

Constraint logic programming is a form of constraint programming, in which logic programming is extended to include concepts from constraint satisfaction. A constraint logic program is a logic program that contains constraints in the body of clauses. An example of a clause including a constraint is A(X,Y) :- X+Y>0, B(X), C(Y). In this clause, X+Y>0 is a constraint; A(X,Y), B(X), and C(Y) are literals as in regular logic programming. This clause states one condition under which the statement A(X,Y) holds: X+Y is greater than zero and both B(X) and C(Y) are true.

As in regular logic programming, programs are queried about the provability of a goal, which may contain constraints in addition to literals. A proof for a goal is composed of clauses whose bodies are satisfiable constraints and literals that can in turn be proved using other clauses. Execution is performed by an interpreter, which starts from the goal and recursively scans the clauses trying to prove the goal. Constraints encountered during this scan are placed in a set called constraint store. If this set is found out to be unsatisfiable, the interpreter backtracks, trying to use other clauses for proving the goal. In practice, satisfiability of the constraint store may be checked using an incomplete algorithm, which does not always detect inconsistency.