Counting solutions from finite samplings

We formulate the solution counting problem within the framework of inverse Ising problem and use fast belief propagation equations to estimate the entropy whose value provides an estimate on the true one. We test this idea on both diluted models (random 2-SAT and 3-SAT problems) and fully-connected model (binary perceptron), and show that when the constraint density is small, this estimate can be very close to the true value. The information stored by the salamander retina under the natural movie stimuli can also be estimated and our result is consistent with that obtained by Monte Carlo method. Of particular significance is sizes of other metastable states for this real neuronal network are predicted.Comment: 9 pages, 4 figures and 1 table, further discussions adde

Choco: an Open Source Java Constraint Programming Library

International audienceChoco is a java library for constraint satisfaction problems (CSP), constraint programming (CP) and explanation-based constraint solving (e-CP). It is built on a event-based propagation mechanism with backtrackable structures

Study of Global Change Impacts on the Inland Navigation Management: Application on the Nord-Pas de Calais Network

AbstractIn a global change context, governments in Europe want to promote alternative transports as inland navigation or railway instead of road transport. As example, in north of France, a shift of 20% from road transport to these alternative transport solutions is expected by 2050. Reaching this goal requires not only the delivery of new infrastructures and equipment, but also the design of efficient management strategies. By focusing on waterborne transport, it is thus necessary to improve the management of the inland navigation networks particularly the water resource. Indeed, the waterborne transport accommodation is strongly linked to the available water resource. This will be a challenging point in a global change context.The paper deals with the global change impacts on inland navigation networks. It aims at proposing new contributions as compared to past and current results of European projects on climate change and inland navigation. It appeared that the multi-scale modeling approach for inland navigation networks that was proposed during the last TRA Conference in Paris in 2014 is useful to determine the resilience of these networks and their ability to guarantee the navigation conditions during drought and flood periods. The proposed tools are developed to consider two space and time scales. The first approach is used to determine the water quantity that is necessary to accommodate the navigation during half a day, and the second allows the efficient control of the gates to keep the water level of each navigation reach close to its setpoint by rejecting disturbances and compensating the waves due to the lock operations. One example based on the real inland navigation network of the north of France is used to highlight the contributions of the multi-scale modeling approach

Entropy: a Consolidation Manager for Clusters

Clusters provide powerful computing environments, but in practice much of this power goes to waste, due to the static allocation of tasks to nodes, regardless of their changing computational requirements. Consolidation is an approach that migrates tasks within a cluster as their computational requirements change, both to reduce the number of nodes that need to be active and to eliminate temporary overload situations. Previous consolidation strategies have relied on task placement heuristics that use only local optimization and typically do not take migration overhead into account. However, heuristics based on only local optimization may miss the globally optimal solution, resulting in unnecessary resource usage, and the overhead for migration may nullify the benefits of consolidation. In this paper, we propose the Entropy resource manager for homogeneous clusters, which performs consolidation based on constraint programming and takes migration overhead into account. The use of constraint programming allows Entropy to find mappings of tasks to nodes that are better than those found by heuristics based on local optimizations, and that are frequently globally optimal in the number of nodes. Because migration overhead is taken into account, Entropy chooses migrations that can be implemented efficiently, incurring a low performance overhead

Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop problem

International audienceConstraint Programming (CP) solvers classically explore the solution space using tree search-based heuristics. Monte-Carlo Tree-Search (MCTS), a tree-search based method aimed at sequential decision making under uncertainty, simultaneously estimates the reward associated to the sub-trees, and gradually biases the exploration toward the most promising regions. This paper examines the tight combination of MCTS and CP on the job shop problem (JSP). The contribution is twofold. Firstly, a reward function compliant with the CP setting is proposed. Secondly, a biased MCTS node-selection rule based on this reward is proposed, that is suitable in a multiple-restarts context. Its integration within the Gecode constraint solver is shown to compete with JSP-specific CP approaches on difficult JSP instances

Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning

Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter set for a given optimization algorithm and (set of) problem instances. When the objective of the optimization is some scalar quality of the solution given by the target algorithm, this quality is also used as the basis for the quality of parameter sets. But in the case of multi-objective optimization by aggregation, the set of solutions is given by several single-objective runs with different weights on the objectives, and it turns out that the hypervolume of the final population of each single-objective run might be a better indicator of the global performance of the aggregation method than the best fitness in its population. This paper discusses this issue on a case study in multi-objective temporal planning using the evolutionary planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how ParamILS makes a difference between both approaches, and demonstrate that indeed, in this context, using the hypervolume indicator as ParamILS target is the best choice. Other issues pertaining to parameter tuning in the proposed context are also discussed.Comment: arXiv admin note: substantial text overlap with arXiv:1305.116

Prefix-Projection Global Constraint for Sequential Pattern Mining

Sequential pattern mining under constraints is a challenging data mining task. Many efficient ad hoc methods have been developed for mining sequential patterns, but they are all suffering from a lack of genericity. Recent works have investigated Constraint Programming (CP) methods, but they are not still effective because of their encoding. In this paper, we propose a global constraint based on the projected databases principle which remedies to this drawback. Experiments show that our approach clearly outperforms CP approaches and competes well with ad hoc methods on large datasets

Changement de contexte pour tâches virtualisées à l'échelle des grappes

National audienceDe nos jours, la gestion des ressources d'une grappe est effectuée en allouant des tranches de temps aux applications, spécifiées par les utilisateurs et de manière statique. Pour un utilisateur, soit les ressources demandées sont sur-estimées et la grappe est sous-utilisée, soit sous-dimensionnées et ses calculs sont dans la plupart des cas perdus. L'apparition de la virtualisation a apporté une certaine flexibilité quant à la gestion des applications et des ressources des grappes. Cependant, pour optimiser l'utilisation de ces ressource, et libérer les utilisateurs d'estimations hasardeuses, il devient nécessaire d'allouer dynamiquement les ressources en fonction des besoins réels des applications : Être capable de démarrer dynamiquement une application lorsqu'une ressource se libère ou la suspendre lorsque la ressource doit être ré-attribuée. En d'autres termes, être capable de développer un système comparable au changement de contexte sur les ordinateurs standards pour les applications s'exécutant sur une grappe. En s'appuyant sur la virtualisation, développer un tel mécanisme de manière générique devient envisageable. Dans cet article nous proposons une infrastructure offrant la notion de changement de contexte d'applications virtualisées appliquée aux grappes. Cette solution a permis de développer un ordonnanceur exécutant simultanément un maximum d'applications virtualisées. Nous montrons qu'une telle solution augmente le taux d'occupation de notre grappe et réduit le temps de traitement des applications