GENERALIZED DISTRIBUTED CONSENSUS-BASED ALGORITHMS FOR UNCERTAIN SYSTEMS AND NETWORKS

We address four problems related to multi-agent optimization, filtering and agreement. First, we investigate collaborative optimization of an objective function expressed as a sum of local convex functions, when the agents make decisions in a distributed manner using local information, while the communication topology used to exchange messages and information is modeled by a graph-valued random process, assumed independent and identically distributed. Specifically, we study the performance of the consensusbased multi-agent distributed subgradient method and show how it depends on the probability distribution of the random graph. For the case of a constant stepsize, we first give an upper bound on the difference between the objective function, evaluated at the agents' estimates of the optimal decision vector, and the optimal value. In addition, for a particular class of convex functions, we give an upper bound on the distances between the agents' estimates of the optimal decision vector and the minimizer and we provide the rate of convergence to zero of the time varying component of the aforementioned upper bound. The addressed metrics are evaluated via their expected values. As an application, we show how the distributed optimization algorithm can be used to perform collaborative system identification and provide numerical experiments under the randomized and broadcast gossip protocols. Second, we generalize the asymptotic consensus problem to convex metric spaces. Under minimal connectivity assumptions, we show that if at each iteration an agent updates its state by choosing a point from a particular subset of the generalized convex hull generated by the agents current state and the states of its neighbors, then agreement is achieved asymptotically. In addition, we give bounds on the distance between the consensus point(s) and the initial values of the agents. As an application example, we introduce a probabilistic algorithm for reaching consensus of opinion and show that it in fact fits our general framework. Third, we discuss the linear asymptotic consensus problem for a network of dynamic agents whose communication network is modeled by a randomly switching graph. The switching is determined by a finite state, Markov process, each topology corresponding to a state of the process. We address both the cases where the dynamics of the agents are expressed in continuous and discrete time. We show that, if the consensus matrices are doubly stochastic, average consensus is achieved in the mean square and almost sure senses if and only if the graph resulting from the union of graphs corresponding to the states of the Markov process is strongly connected. Fourth, we address the consensus-based distributed linear filtering problem, where a discrete time, linear stochastic process is observed by a network of sensors. We assume that the consensus weights are known and we first provide sufficient conditions under which the stochastic process is detectable, i.e. for a specific choice of consensus weights there exists a set of filtering gains such that the dynamics of the estimation errors (without noise) are asymptotically stable. Next, we develop a distributed, sub-optimal filtering scheme based on minimizing an upper bound on a quadratic filtering cost. In the stationary case, we provide sufficient conditions under which this scheme converges; conditions expressed in terms of the convergence properties of a set of coupled Riccati equations. We continue by presenting a connection between the consensus-based distributed linear filter and the optimal linear filter of a Markovian jump linear system, appropriately defined. More specifically, we show that if the Markovian jump linear system is (mean square) detectable, then the stochastic process is detectable under the consensus-based distributed linear filtering scheme. We also show that the optimal gains of a linear filter for estimating the state of a Markovian jump linear system, appropriately defined, can be used to approximate the optimal gains of the consensus-based linear filter

REENGINEERING AS AN EFFICIENT SOLUTION TO REDESIGN ACTIVITIES AND PROCESSES OF AN ENTERPRISE

The paper shows a series of opportunities linked to the technological, human and economical reengineering of activities and technological processes developed in a modern enterprise, adding new dimensions in the efficient development, on the market principles, in argument with the desiderate of the durable development of the society. Starting with these considerations we designed aspects linked to: business reengineering implementation, stages of this process, accentuating the management methods, delimitation on areas of activity of the effects on reengineering action.business reengineering process, reengineering enterprise, models and methods for reengineering project

Reengineering as an Efficient Solution to Redesign Activities and Processes of an Enterprise

The paper shows a series of opportunities linked to the technological, human and economical reengineering of activities and technological processes developed in a modern enterprise, adding new dimensions in the efficient development, on the market principles, in argument with the desiderate of the durable development of the society. Starting with these considerations we designed aspects linked to: business reengineering implementation, stages of this process, accentuating the management methods, delimitation on areas of activity of the effects on reengineering action.business reengineering process, reengineering enterprise, models and methods for reengineering project

Offenses of Human Trafficking in the European Union

Within this paper there were briefly examined the trafficking in persons offenses the two European normative acts, namely the Framework Decision 2002/629/JHA and the Directive 2011/36UE, which repealed the first instrument, and some aspects of legal content of offenses of the two acts. The innovation and interest elements are represented by the comparative examination and highlighted by the incriminations development, thus identifying new ways to prevent and combat them. The work can be useful to those practicing in this field and in the academic environment or to the European or Romanian legislator

ROMANIA’S ECONOMIC GROWTH IN THE CURRENT INTERNATIONAL CONTEXT

Present and future economic growth is increasingly dependent on trends in the global economy. They lead to an intertwining of the economic progress at a national-regional-global level. In particular to bearing economic integration, globalization, globalization of economic, political multipolarity, expanding economy on a continental and global level, etc. The effects of these changes can be both beneficial and negative, so we proposed a study on the evolution of parameters characterizing both economic growth and the evolution of this growth in Romania in the current international context

2D Density Control of Micro-Particles using Kernel Density Estimation

We address the problem of 2D particle density control. The particles are immersed in dielectric fluid and acted upon by manipulating an electric field. The electric field is controlled by an array of electrodes and used to bring the particle density to a desired pattern using dielectrophoretic forces. We use a lumped, 2D, capacitive-based, nonlinear model describing the motion of a particle. The spatial dependency of the capacitances is estimated using electrostatic COMSOL simulations. We formulate an optimal control problem, where the loss function is defined in terms of the error between the particle density at some final time and a target density. We use a kernel density estimator (KDE) as a proxy for the true particle density. The KDE is computed using the particle positions that are changed by varying the electrode potentials. We showcase our approach through numerical simulations, where we demonstrate how the particle positions and the electrode potentials vary when shaping the particle positions from a uniform to a Gaussian distribution

The corruption - an economic and social analysis

The current edition in English language turns into account and completes the studies published in the previous edition. Carrying on the research and publication activities on the topic of corruption is based on its novelty and on the special interest for the Romanian language edition. At the same time, we hope that the current volume will provide greater opportunities for foreign access and thus the admittance in the European and international flows of information in this field. The core ideas of the book focus on social perception, modelled through statistic analyses, on the specificity of corruption in the public administration or the public health system in close correlation to the processes of decentralization and performance of health services. The analysis of the corruption topic is in interference with the effects of the European integration processes, globalization, being correlated to adjacent developments concerning the public integrity, national or regional economic freedom and development. This book represents the result of the scientific collaboration between teams of teaching staff and students from the National School of Political Studies and Public Administration and Academy of Economic Studies, Bucharest. The book is structured on nine chapters, organised according to a didactic logic, in view to provide the reader a profound overview of the mechanisms and methodology of research as well as the conclusions and the economic and social impact of corruption. Data processing in view to estimate the correlations and parameters of regression has been achieved through SPSS and Eviews statistic programs

A performance comparison between two consensus-based distributed optimization algorithms

In this paper we address the problem of multi-agent optimization for convex functions expressible as sums of convex functions. Each agent has access to only one function in the sum and can use only local information to update its current estimate of the optimal solution. We consider two consensus-based iterative algorithms, based on a combination between a consensus step and a subgradient decent update. The main difference between the two algorithms is the order in which the consensus-step and the subgradient descent update are performed. We show that updating first the current estimate in the direction of a subgradient and then executing the consensus step ensures better performance than executing the steps in reversed order. In support of our analytical results, we give some numerical simulations of the algorithms as well