Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament

In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the VoteWatch Europe Website for the whole 7 th term (2009-2014), by considering voting similarities between Members of the EP to define weighted signed links. We then apply a selection of community detection algorithms, designed to process only positive links, to these data. We also apply Parallel Iterative Local Search (Parallel ILS), an algorithm recently proposed to identify balanced partitions in signed networks. Our results show that, contrary to the conclusions of a previous study focusing on other data, the partitions detected by ignoring or considering the negative links are indeed remarkably different for these networks. The relevance of negative links for graph partitioning therefore is an open question which should be further explored.Comment: in 2nd European Network Intelligence Conference (ENIC), Sep 2015, Karlskrona, Swede

Resolution Search et problèmes d'optimisation discrète

Les problèmes d optimisation discrète sont pour beaucoup difficiles à résoudre, depar leur nature combinatoire. Citons par exemple les problèmes de programmationlinéaire en nombres entiers. Une approche couramment employée pour les résoudreexactement est l approche de Séparation et Évaluation Progressive. Une approchedifférente appelée Resolution Search a été proposée par Chvátal en 1997 pourrésoudre exactement des problèmes d optimisation à variables 0-1, mais elle restemal connue et n a été que peu appliquée depuis.Cette thèse tente de remédier à cela, avec un succès partiel. Une première contributionconsiste en la généralisation de Resolution Search à tout problème d optimisationdiscrète, tout en introduisant de nouveaux concepts et définitions. Ensuite,afin de confirmer l intérêt de cette approche, nous avons essayé de l appliquer enpratique pour résoudre efficacement des problèmes bien connus. Bien que notrerecherche n ait pas abouti sur ce point, elle nous a amené à de nouvelles méthodespour résoudre exactement les problèmes d affectation généralisée et de localisationsimple. Après avoir présenté ces méthodes, la thèse conclut avec un bilan et desperspectives sur l application pratique de Resolution Search.The combinatorial nature of discrete optimization problems often makes them difficultto solve. Consider for instance integer linear programming problems, which arecommonly solved using a Branch-and-Bound approach. An alternative approach,Resolution Search, was proposed by Chvátal in 1997 for solving 0-1 optimizationproblems, but remains little known to this day and as such has seen few practicalapplications.This thesis attempts to remedy this state of affairs, with partial success. Itsfirst contribution consists in the generalization of Resolution Search to any discreteoptimization problem, while introducing new definitions and concepts. Next, wetried to validate this approach by attempting to solve well-known problems efficientlywith it. Although our research did not succeed in this respect, it lead usto new methods for solving the generalized assignment and uncapacitated facilitylocation problems. After presenting these methods, this thesis concludes with asummary of our attempts at practical application of Resolution Search, along withfurther perspectives on this matter.AVIGNON-Bib. numérique (840079901) / SudocSudocFranceF

A multi-level search strategy for the 0–1 Multidimensional Knapsack Problem

AbstractWe propose an exact method based on a multi-level search strategy for solving the 0–1 Multidimensional Knapsack Problem. Our search strategy is primarily based on the reduced costs of the non-basic variables of the LP-relaxation solution. Considering that the variables are sorted in decreasing order of their absolute reduced cost value, the top level branches of the search tree are enumerated following Resolution Search strategy, the middle level branches are enumerated following Branch & Bound strategy and the lower level branches are enumerated according to a simple Depth First Search enumeration strategy. Experimentally, this cooperative scheme is able to solve optimally large-scale strongly correlated 0–1 Multidimensional Knapsack Problem instances. The optimal values of all the 10 constraint, 500 variable instances and some of the 30 constraint, 250 variable instances of the OR-Library were found. These values were previously unknown

Methodes duales pour problemes d'optimisation avec variables entieres

SIGLEINIST T 73262 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc