Constraint propagation with interval labels
Artificial Intelligence
Arc-consistency for continuous variables
Artificial Intelligence
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Solving Polynomial Systems Using a Branch and Prune Approach
SIAM Journal on Numerical Analysis
Approximation Techniques for Non-linear Problems with Continuum of Solutions
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Universally Quantified Interval Constraints
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Structural Constraint-Based Modeling and Reasoning with Basic Configuration Cells
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
A Branch and Bound Algorithm for Numerical MAX-CSP
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Improving the computational efficiency in symmetrical numeric constraint satisfaction problems
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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This paper introduces a new framework for extending consistent domains of numeric CSP. The aim is to offer the greatest possible freedom of choice for one variable to the designer of a CAD application. Thus, we provide here an efficient and incremental algorithm which computes the maximal extension of the domain of one variable. The key point of this framework is the definition, for each inequality, of an univariate extrema function which computes the left most and right most solutions of a selected variable (in a space delimited by the domains of the other variables). We show how these univariate extrema functions can be implemented efficiently. The capabilities of this approach are illustrated on a ballistic example.