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Constraint programming. Constraint programming (CP) [1] is a paradigm for solving combinatorial problems that draws on a wide range of techniques from artificial intelligence, computer science, and operations research. In constraint programming, users declaratively state the constraints on the feasible solutions for a set of decision variables.
Hary Gunarto at Ritsumeikan Asia Pacific University, Japan in 2016. Hary Gunarto is an Indonesian computer engineer and scientist, writer, researcher and professor emeritus at Ritsumeikan Asia Pacific University in Japan who is recognized for his research and major publications ranging from computer network, computer programming/computer simulation and applications of ICT (digital media ...
Constraint satisfaction. In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution through a set of constraints that impose conditions that the variables must satisfy. [1] A solution is therefore an assignment of values to the variables that satisfies all constraints—that is, a point in ...
Constraint (mathematics) In mathematics, a constraint is a condition of an optimization problem that the solution must satisfy. There are several types of constraints—primarily equality constraints, inequality constraints, and integer constraints. The set of candidate solutions that satisfy all constraints is called the feasible set.
Simulation in manufacturing systems is the use of software to make computer models of manufacturing systems, so to analyze them and thereby obtain important information. It has been syndicated as the second most popular management science among manufacturing managers. [1] [2] However, its use has been limited due to the complexity of some ...
A constraint in computer-aided design (CAD) software is a limitation or restriction imposed by a designer or an engineer upon geometric properties [1] : 203 of an entity of a design model that maintains its structure as the model is manipulated. These properties can include relative length, angle, orientation, size, shift, and displacement. [2]
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 partial evaluations may be found inconsistent without further search.
The algorithm works by creating the constraint satisfied by these evaluations and incorporating this new constraint in the second node. When all constraints have been propagated from the leaves to the root and back to the root, all nodes contain all constraints that are relevant to them. The problem can therefore be solved in each node.