![]() ![]() You can change your choices at any time by visiting Cookie Preferences, as described in the Cookie Notice. Click ‘Customise Cookies’ to decline these cookies, make more detailed choices, or learn more. Third parties use cookies for their purposes of displaying and measuring personalised ads, generating audience insights, and developing and improving products. This includes using first- and third-party cookies, which store or access standard device information such as a unique identifier. If you agree, we’ll also use cookies to complement your shopping experience across the Amazon stores as described in our Cookie Notice. ![]() We also use these cookies to understand how customers use our services (for example, by measuring site visits) so we can make improvements. ![]() 2000.We use cookies and similar tools that are necessary to enable you to make purchases, to enhance your shopping experiences and to provide our services, as detailed in our Cookie Notice. on Principles and Practices of Constraint Programming (CP-2000), pages 441–456. of ECAI’2000 Workshop on Modelling and Solving Problems with Constraints, 2000. on Principles and Practice of Constraint Programming (CP2000), pages 384–395. A global constraint combining a sum constraint and difference constraints. of the 12th National Conference on AI, pages 362–367. A filtering algorithm for constraints of difference in CSPs. on Principles and Practices of Constraint Programming (CP-2000), pages 353–368. A Generic Arc Consistency Algorithm and its Specializations. Some practicable filtering techniques for the constraint satisfaction problem. of Computing Science, University of Alberta, 2000. A Theoretical Comparison of Selected CSP Solving and ModellingTechniques. Increasing constraint propagation by redundant modeling: an experience report. on Principles and Practice of Constraint Programming (CP99), pages 88–102. On forward checking for nonbinary constraint satisfaction. of 17th National Conference on Artificial Intelligence, pages 262–266. Solving the round robin problem using propositional logic. of 17th National Conference on Artificial Intelligence, pages 256–261. Generating satisfiable problems instances. This process is experimental and the keywords may be updated as the learning algorithm improves.ĭimitris Achlioptas, Carla P. These keywords were added by machine and not by the authors. They also illustrate a general methodology for comparing different constraint models. Our results will aid constraint programmers to choose a model for a permutation problem. In this paper, we perform an extensive theoretical and empirical study of these different models. By means of channelling constraints, a combined model can have both primal and dual variables. ![]() In the dual representation, dual variables stand for the primal values, whilst dual values stand for the primal variables. For example, with permutation problems, we can choose between a primal and a dual representation. In many cases, there is considerable choice for the decision variables. When writing a constraint program, we have to decide what to make the decision variable, and how to represent the constraints on these variables. ![]()
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