Constraint programming — Programming paradigms Agent oriented Automata based Component based Flow based Pipelined Concatenative Concurrent computin … Wikipedia
Constraint logic programming — Programming paradigms Agent oriented Automata based Component based Flow based Pipelined Concatenative Concurrent computing … Wikipedia
Constraint Handling Rules — (CHR) is a declarative programming language extension introduced in 1991[1][2] by Thom Frühwirth. Originally designed for developing (prototypes of) constraint programming systems, CHR is increasingly used as a high level general purpose… … Wikipedia
Constraint satisfaction problem — Constraint satisfaction problems (CSP)s are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite… … Wikipedia
Constraint satisfaction — In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution to a set of constraints that impose conditions that the variables must satisfy. A solution is therefore a vector of variables that… … Wikipedia
Constraint inference — In constraint satisfaction, constraint inference is a relationship between constraints and their consequences. A set of constraints D entails a constraint C if every solution to D is also a solution to C. In other words, if V is a valuation of… … Wikipedia
Constraint Composite Graph — The constraint composite graph is a node weighted undirected graph associated with a given combinatorial optimization problem posed as a weighted constraint satisfaction problem. Developed and introduced by Satish Kumar Thittamaranahalli (T. K.… … Wikipedia
Constraint graph — For a graph in electronic design automation, see Constraint graph (layout). In constraint satisfaction research of artificial intelligence and operations research, constraint graphs and hypergraphs are used to represent relations among… … Wikipedia
Constraint learning — In constraint satisfaction backtracking algorithms, constraint learning is a technique for improving efficiency. It works by recording new constraints whenever an inconsistency is found. This new constraint may reduce the search space, as future… … Wikipedia
Constraint satisfaction dual problem — The dual problem is a reformulation of a constraint satisfaction problem expressing each constraint of the original problem as a variable. Dual problems only contain binary constraints, and are therefore solvable by algorithms tailored for such… … Wikipedia
Constraint optimization — In constraint satisfaction, constrained optimization (also called constraint optimization) seeks for a solution maximizing or minimizing a cost function. Contents 1 Definition 2 Solution methods 2.1 Branch and bound … Wikipedia