Data Mining-Based Decomposition for Solving the MAXSAT Problem: Toward a New Approach

This article explores advances in the data mining arena to solve the fundamental MAXSAT problem. In the proposed approach, the MAXSAT instance is first decomposed and clustered by using data mining decomposition techniques, then every cluster resulting from the decomposition is separately solved to construct a partial solution. All partial solutions are merged into a global one, while managing possible conflicting variables due to separate resolutions. The proposed approach has been numerically evaluated on DIMACS instances and some hard Uniform-Random-3-SAT instances, and compared to state-of-the-art decomposition based algorithms. The results show that the proposed approach considerably improves the success rate, with a competitive computation time that's very close to that of the compared solutions

Generalized Hypertree Decomposition for solving non binary CSP with compressed table constraints

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When the decomposition meets the constraint satisfaction problem

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is that the set of constraints are first clustered using a decomposition algorithm in which highly correlated constraints are grouped together. Next, parallel search of variables is performed on the produced clusters in a way that is friendly for parallel computing. In particular, for the first step, we propose the adaptation of two well-known clustering algorithms ( k -means and DBSCAN). For the second step, we develop a GPU-based approach to efficiently explore the clusters. The results from the extensive experimental evaluation show that the PD-CSP provides competitive results in terms of accuracy and runtime

Distributed backtracking algorithm based on tree decomposition over wireless sensor networks

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A Forward-Checking algorithm based on a Generalised Hypertree Decomposition for solving non-binary constraint satisfaction problems

International audienceMethods exploiting hypertree decompositions are considered as the best approach for solving extensional constraint satisfaction problems (CSPs) on finite domains, with regard to theoretical time complexity when fixed widths are considered. However, this result has not been confirmed in practice because of the memory explosion problem. In this article, a new approach for efficient solving extensional non-binary CSPs is proposed. It is a combination of an enumerative search algorithm which is memory efficient and a Generalised Hypertree Decomposition (GHD) that is time efficient. This new approach is a cluster-oriented Forward-Checking algorithm. It considers the solutions of the subproblems deriving from the decomposition, as the values to be assigned rather than the values associated with the variables of the initial problem. In addition, the algorithm is guided by an order induced by the clusters deriving from the GHD. Moreover, two improved versions of this algorithm are proposed. The first version uses nogoods and the second one improves it again by a dynamic reordering of subtrees. All these algorithms have been implemented and the experimental results are promising

A FAST PARALLEL TREE CONTRACTION FOR THE RAY- OBJECT CALCULUS USING OPEN-MP

Ray tracing is a well known algorithm in the visualization area for its image quality and its simplicity. Unfortunately, it is computationally expensive. The basic operation of ray tracing is the calculus of the intersection points between rays and objects. This operation represents a large part of this algorithm. The goal of this work is mainly the proposition of an optimal parallel algorithm performing a ray-CSG intersection in O(log n log log n) time complexity and O(n) processors on a PRAM CREW model. It is based on the Contract tree algorithms developed in [1, 8] . Finally, the parallel tree contraction algorithm is implemented on a parallel machine

Intelligent mapping between GPU and cluster computing for discovering big association rules

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Approches de résolution multiobjective séquentielle et parallèle pour les réseaux de transports multimodaux

Dans cette thèse, nous nous intéressons à la problématique de transport usager dans un contexte multimodal, multi-objectif et dépendant du temps. Notre première contribution porte sur la définition du graphe de transfert, un modèle de représentation des réseaux multimodaux. Sur base de ce modèle, cette thèse propose plusieurs algorithmes de calculs d itinéraires multimodaux et dépendants du temps mais simplement mono-objectifs. Toujours dans le souci de faire face aux exigences des usagers, nous nous intéressons dans une deuxième partie de cette au problème multi-objectif. Nous avons expérimenté dans un premier temps, la version dépendante du temps de l algorithme exact de Martins, ensuite proposé une solution basée sur les algorithmes génétiques. Ces deux approches restent limitées faute de temps ou d espace. L algorithme hybride combinant la rapidité des méta-heuristiques et la complétude des méthodes exactes a donné de meilleurs résultatsThe focus of this thesis is about multi-modal, multi-objective and time-dependent in passengers transport networks. We propose itineraries processing solutions that satisfy the user needs, as much as possible. The first part of our contributions begins with the definition of the transfer-graph model that is consistent with the distributed nature of multi-modal transport networks. Based on this model, we propose several itineraries processing algorithms. We have been interested, in a second part of this thesis, in developing multi-objective solutions to satisfy more constraints at the same time. We first experimented the time-dependent version of an exact algorithm based on Martins. We then proposed a solution based on a genetic algorithm. Both of these approaches are limited because of either excessive time response or memory space limit. The hybrid algorithm which combines the speed of meta-heuristics and completeness of exact methods, provide better resultsMETZ-SCD (574632105) / SudocSudocFranceF