A non-binary Constraint Satisfaction Problem (CSP) can be solved directly using extended versions of binary techniques. Alternatively, the non-binary problem can be translated into an equivalent binary one. In this case, it is generally accepted that the translated problem can be solved by applying well-established techniques for binary CSPs. In this paper we evaluate the applicability of the latter approach. We demonstrate that the use of standard techniques for binary CSPs in the encodings of non-binary problems is problematic and results in models that are very rarely competitive with the non-binary representation. To overcome this, we propose specialized arc consistency and search algorithms for binary encodings, and we evaluate them theoretically and empirically. We consider three binary representations; the hidden variable encoding, the dual encoding, and the double encoding. Theoretical and empirical results show that, for certain classes of non-binary constraints, binary encodings are a competitive option, and in many cases, a better one than the non-binary representation.
Visual LinProg is an educational tool that solves linear problems (LPs), using animation and visualization techniques. The core of the proposed software includes the wellknown class of simplex type algorithms. This tool is a Web-based software and hence platform independent. Visual LinProg was initially invented to be used in mathematical programming courses to supplement the teaching. The user-student can solve his own general LPs, view the solution process step-by-step, and import or export his own examples in an easy way to read format. The solution process is covered scholastically through textual information and also the necessary steps from the pseudo code are depicted, using multiple views. In this study, we describe Visual LinProg and how it is used in educational purposes. Finally, we present an evaluation of the proposed educational tool.ß2007 Wiley Periodicals, Inc. Comput Appl Eng Educ 15:
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