Inexact Uzawa algorithms for solving nonlinear saddle-point problems are proposed. A simple sufficient condition for the convergence of the inexact Uzawa algorithms is obtained. Numerical experiments show that the inexact Uzawa algorithms are convergent.

The equation modelling the evolution of a foam (a complex porous medium consisting of a set of gas bubbles surrounded by liquid films) is solved numerically. This model is described by the reaction–diffusion differential equation with a free boundary. Two numerical methods, namely the fixed-point and the averaging in time and forward differences in space (the Crank–Nicolson scheme), both in combination with Newton’s method, are proposed for solving the governing equations. The solution of Burgers’ equation is considered as a special case. We present the Crank–Nicolson scheme combined with Newton’s method for the reaction–diffusion differential equation appearing in a foam breaking phenomenon.

This paper considers an optimal control problem for a class of controlled hybrid dynamical systems (HDSs) with prescribed switchings. By using Ekeland’s variational principle and a matrix cost functional, a minimum principle for HDSs is derived, which provides a necessary condition of the aforementioned problem. The results given in this paper include both pure continuous systems and pure discrete-time systems as special cases.

We address the problem of simultaneous localization and mapping (SLAM) by combining visual loop-closure detection with metrical information given by a robot odometry. The proposed algorithm extends a purely appearance-based loop-closure detection method based on bags of visual words [A. Angeli, D. Filliat, S. Doncieux and J.-A. Meyer, IEEE Transactions On Robotics, Special Issue on Visual SLAM 24 (2008) 1027–1037], which is able to detect when the robot has returned back to a previously visited place. An efficient optimization algorithm is used to integrate odometry information and to generate a consistent topo-metrical map much more usable for global localization and path planning. The resulting algorithm which only requires a monocular camera and robot odometry data, is real-time, incremental (i.e. it does not require any a priori information on the environment), and can be easily embedded on medium platforms.

This article focuses on data aggregation in vehicular ad hoc networks. In such networks, sensor data are usually produced and exchanged between vehicles in order to warn or inform the drivers when an event is detected (e.g., accident, emergency braking, parking space released, vehicle with non-functioning brake lights, etc.).In the following, we present a solution to aggregate and store these data in order to have a history of past events. We therefore use Flajolet-Martin sketches. Our goal is to generate additional knowledge to assist drivers by providing them useful information even if no event is transmitted by vehicles in the vicinity.

This paper addresses an ongoing experience in the design of an artificial agenttaking decisionsand combining them with the decisions taken by human agents.The context is a serious game research project,aimed at computer-based support for participatory management of protected areas(and more specifically national parks)in order to promote biodiversity conservation and social inclusion.Its objective isto help various stakeholders (e.g., environmentalist, tourism operator)to collectively understand conflict dynamicsandexplore negotiationstrategies for the management of parks.In this paper,after introducing the design of our serious game, named SimParc,we will describe the architecture of the decision making agent playing the role of the park manager.In the game, the park manager makes final decisions based on its own analysisand also on the votes of the stakeholders.It includes two modules:1) individual decision –based on a model of argumentation,which also provides a basis to justify and explain the decision;2) participatory decision –to take into account the preferences/votes from the stakeholders.

Information quality is crucial to any information fusion system as combining unreliable or partially credible pieces of information may lead to erroneous results. In this paper, Dempster-Shafer theory of evidence is being used as a framework for representing and combining uncertain pieces of information. We propose a method of dynamic estimation of evidence discounting rates based on the credibility of pieces of information. The credibility of a piece of information Cre(I n ) is evaluated through a measure of consensus (corroboration degree) between a set of belief functions, and this measure serves as a basis for quantifying the credibility of the source (sensor or fusion node) itself, Cre(S k ), used then as a discounting factor for all further belief functions provided by S k . The process is dynamic in the sense that the credibility of the source is revisited in the light of new incoming piece of information. The method proposed relies on a hybrid fusion topology in which the sensors are grouped according to the feature they measure (similar and dissimilar sensors), allowing to select different kinds of measure for estimating the corroboration degrees. Through simulations, we compare (a) the hybrid-combination using the source credibility and the robust combination rule (RCR-L) accounting automatically for sensors's credibility; (b) the hybrid-combination, with different membership degrees and corroboration degrees used to estimate the sources credibility. We show that the new hybrid topology together with the credibility-based evidence discounting estimation algorithm provide a faster identification of the observed object.

Combat Management System training uses simulation of an overall tactical situation. This involves the real-time management of numerous and diverse entities to keep the simulation scenario consistent in a highly dynamic environment. To address this difficult problem, we propose an adaptive multi-agent system in which each entity is considered as a smart sensor/effector mobile. The autonomy and the dynamic behaviour offered to each entity leads the simulation to self-adapt to inevitable disturbances of the user. According to the cooperation paradigm, this approach also allows the mobiles to highlights a coherent global behaviour with mutual helping. Finally, the system shows the relevance of the Emergence Technologies in the elaboration of a new generation of sensors. This software is currently under development in GATES, a project of the DCNS company.

In this paper, we propose a new method to generate a continuousbelief functions from a multimodal probability distribution function definedover a continuous domain. We generalize Smets' approach in the sense thatfocal elements of the resulting continuous belief function can be disjoint setsof the extended real space of dimension n. We then derive the continuousbelief function from multimodal probability density functions using the leastcommitment principle. We illustrate the approach on two examples of probabilitydensity functions (unimodal and multimodal). On a case study of Search AndRescue (SAR), we extend the traditional probabilistic framework of search theoryto continuous belief functions theory. We propose a new optimization criterionto allocate the search effort as well as a new rule to update the informationabout the lost object location in this latter framework. We finally compare theallocation of the search effort using this alternative uncertaintyrepresentation to the traditional probabilistic representation.

A network of mobile cooperative sensors is considered. The followingproblems are studied:(1) the “optimal" deployment of the sensors on a given territory;(2) the detection of local anomalies in the noisy data measured by thesensors.In absence of an information fusion center in the network, from “local" interactions between sensors “global" solutions of these problems are found.

Criteria for guaranteeing the existence, uniqueness and asymptotic stability (in the sense of Liapunov) of periodic solutions of a forced Liénard-type equation under certain assumptions are presented. These criteria are obtained by application of the Manásevich–Mawhin continuation theorem, Floquet theory, Liapunov stability theory and some analysis techniques. An example is provided to demonstrate the applicability of our results.

The partial inverse minimum cut problem is to minimally modify the capacities of a digraph such that there exists a minimum cut with respect to the new capacities that contains all arcs of a prespecified set. Orlin showed that the problem is strongly NP-hard if the amount of modification is measured by the weighted L 1-norm. We prove that the problem remains hard for the unweighted case and show that theNP-hardness proof of Yang [RAIRO-Oper. Res.35 (2001) 117–126] for this problem with additionalbound constraints is notcorrect.

In this paper we propose a primal-dual interior-point algorithm forconvex quadratic semidefinite optimization problem. The searchdirection of algorithm is defined in terms of a matrix function andthe iteration is generated by full-Newton step. Furthermore, wederive the iteration bound for the algorithm with small-updatemethod, namely, O( $\sqrt{n}$ log $\frac{n}{\varepsilon}$ ), which isbest-known bound so far.

La finance de marché est devenue un des domaines d'ap- plication privilégiés de la recherche opérationnelle. D'un autrecôté, rares sont les applications touchant la banque de détail, tournée vers le grand public. Dans ce papier, nousabordons un problème d'actualité dans le secteur bancaire français : l'optimisation de plans de financementimmobiliers. Le travail que nous présentons a été effectué dans le cadre du développement par la sociétéExperian-Prologia d'une nouvelle application d'instruction de prêts immobiliers pour une grande banque française. Notremodule d'optimisation, au cœur de cette application, est aujourd'hui déployé dans les 2200 agences de cette banqueet permet la simulation et le montage de plusieurs milliers de plans de financement immobiliers chaque mois.

Recently, [Y.Q. Bai, M. El Ghami and C. Roos,SIAM J. Opt. 15 (2004) 101–128]investigated a new class of kernel functions which differs from theclass of self-regular kernel functions. The class is defined by somesimple conditions on the growth and the barrier behavior of thekernel function. In this paper we generalize theanalysis presented in the above paper for P * (κ) LinearComplementarity Problems (LCPs). The analysis for LCPs deviates significantly from the analysisfor linear optimization. Several new tools and techniques are derived in this paper.

In this paper, we present an Uzawa-based heuristic that is adapted to certain type of stochastic optimal control problems. More precisely, we consider dynamical systems that can be divided into small-scale subsystems linked through a static almost sure coupling constraint at each time step. This type of problem is common in production/portfolio management where subsystems are, for instance, power units, and one has to supply a stochastic power demand at each time step. We outline the framework of our approach and present promising numerical results on a simplified power management problem.

This paper investigates the linear minimum mean-square error estimation for discrete-time Markovian jump linear systems with delayed measurements. The key technique applied for treating the measurement delay is reorganization innovation analysis, by which the state estimation with delayed measurements is transformed into a standard linear mean-square filter of an associated delay-free system. The optimal filter is derived based on the innovation analysis method together with geometric arguments in an appropriate Hilbert space. The solution is given in terms of two Riccati difference equations. Finally, a simulation example is presented to illustrate the efficiency of the proposed method.