Constraint Programming and Safe Global Optimization

International audienceWe investigate the capabilities of constraints programming techniques in rigor- ous global optimization methods. We introduce different constraint programming techniques to reduce the gap between efficient but unsafe systems like Baron1, and safe but slow global optimization approaches. We show how constraint program- ming filtering techniques can be used to implement optimality-based reduction in a safe and efficient way, and thus to take advantage of the known bounds of the ob- jective function to reduce the domain of the variables, and to speed up the search of a global optimum. We describe an efficient strategy to compute very accurate approximations of feasible points. This strategy takes advantage of the Newton method for under-constrained systems of equalities and inequalities to compute efficiently a promising upper bound. Experiments on the COCONUT benchmarks demonstrate that these different techniques drastically improve the performances

Revisiting the upper bounding process in a safe Branch and Bound algorithm

Finding feasible points for which the proof succeeds is a critical issue in safe Branch and Bound algorithms which handle continuous problems. In this paper, we introduce a new strategy to compute very accurate approximations of feasible points. This strategy takes advantage of the Newton method for under-constrained systems of equations and inequalities. More precisely, it exploits the optimal solution of a linear relaxation of the problem to compute efficiently a promising upper bound. First experiments on the Coconuts benchmarks demonstrate that this approach is very effective.Comment: Optimization, continuous domains, nonlinear constraint problems, safe constraint based approaches; 14th International Conference on Principles and Practice of Constraint Programming, Sydney : Australie (2008

Vers une Théorie du Test des programmes à contraintes

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Cost Function Networks to Solve Large Computational Protein Design Problems

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Closed-Pattern : Une contrainte globale pour l’extraction de motifs fréquents fermés

National audienceL’extraction de motifs fréquents fermés est un des défis majeurs en fouille de données. Les travaux entrepris récemment en extraction de motifs ont mis en avant l’intérêt d’utiliser les contraintes pour une fouille déclarative. Ces approches se sont montrées très attractives par leurs flexibilité, mais l’utilisation d’un nombre important de contraintes réifiées et de variables auxiliaires posent un sérieux problème quant au traitement des bases de grandes tailles. Dans ce papier, nous présentons une contrainte globale nommée ClosedPattern, qui capture la sémantique particulière des motifs fermés pour résoudre efficacement ce problème, sans faire appel aux contraintes réifiées. Nous proposons un algorithme de filtrage pour la contrainte ClosedPattern, qui maintient la consistance de domaine DC en un temps et espace polynomial

Cost Function Networks to Solve Large Computational Protein Design Problems

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Interval graph mining

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A CP-based approach for mining sequential patterns with quantities

This paper addresses the problem of mining sequential patterns (SPM) from data represented as a set of sequences. In this work, we are interested in sequences of items in which each item is associated with its quantity. To the best of our knowledge, existing approaches don’t allow to handle this kind of sequences under constraints. In the other hand, several proposals show the efficiency of constraint programming (CP) to solve SPM problem dealing with several kind of constraints. However, in this paper, we propose the global constraint QSPM which is an extension of the two CP-based approaches proposed in [5] and [7]. Experiments on real-life datasets show the efficiency of our approach allowing to specify many constraints like size, membership and regular expression constraints