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Generalized Nash Games are a powerful modelling tool, first introduced in the 1950's. They have seen some important developments in the past two decades. Separately, Evolutionary Games were introduced in the 1960's and seek to describe how natural selection can drive phenotypic changes in interacting populations. In this paper, we show how the dynamics of these two independently formulated models can be linked under a common framework and how this framework can be used to expand Evolutionary Games. At the center of this unified model is the Replicator Equation and the relationship we establish between it and the lesser known Projected Dynamical System.

In this paper, we investigate the projected dynamical system associated with the generalized variational inequality with hesitant fuzzy relation. We establish the equivalence between the generalized variational inequality with hesitant fuzzy relation and the fuzzy fixed point problem. And we analyze the existence theorem and iterative algorithm of solutions to such problem. Furthermore, using the projection method, we propose a projection neural network for solving the generalized variational inequality with hesitant fuzzy relation and discuss the stability of the proposed projected dynamical system.

The class of projected dynamical systems (PDS) provides a powerful framework for modeling dynamical systems of which the trajectories are constrained to a set by means of projection. This work is concerned with establishing equivalence results among two recent variations of PDS. These are (i) extended PDS (ePDS), which enable partial projection of dynamics, and (ii) oblique PDS, (oPDS) where projections can be done with respect to non-Euclidean norms. We present two sets of sufficient conditions for equivalence among these two system classes. These results enable the transfer of system theoretical properties and tools from one class to the other, which we illustrate in this paper. As an application, we study hybrid integrator-gain systems (HIGS), which are recently introduced hybrid control elements aiming at overcoming fundamental limitations of linear time-invariant control, and are formally described in the ePDS framework. We use our results to also describe these control elements as oPDS, thereby enabling the study of HIGS-controlled systems in this framework.

In this paper, a projection-based recurrent neural network is proposed to solve convex quadratic bilevel programming problems (CQBPP). The Karush–Kuhn–Tucker optimal conditions (KKT) of the lower level problem are used to obtain identical one-level optimization problem. A projected dynamical system which its equilibrium point coincides with the global optimal solution of the corresponding optimization problem is presented. Compared to existing models, the proposed model has the least number of variables and a simple structure with low complexity. Analytically, it is demonstrated that the state vector of the suggested neural network model is stable in the sense of Lyapunov and globally convergent to an optimal solution of CQBPP in finite time. Some numerical examples, a supply chain model and an application deals with an environmental problem are discussed in order to confirm the efficiency of the theoretical results and the performance of the model.

In this paper, strongly pseudomonotone quasi-variational inequalities are investigated. We provide sufficient conditions for existence and uniqueness of solutions of strongly pseudomonotone quasi-variational inequalities. We present some error bounds in terms of residual and regularized gap functions. The global exponential stability of equilibrium solutions of a projected dynamical system for strongly pseudomonotone quasi-variational inequalities is investigated. Strong convergence and error estimates for sequence generated by the (modified) gradient projection method with suitable choices of stepsizes are also studied. Some examples and numerical experiments are provided to support our main results.

This paper develops a dynamic model of competition for the diffusion of online contents in a two-layer network consisting of content providers and viewers. Each content provider seeks to maximize the profit by determining the optimal views and quality levels. We assume that there is a known and fixed limit to the number of times each viewer can access a content. This requirement generates shared constraints for all the providers. The problem is expressed as a Generalized Nash equilibrium with shared constraints that is then formulated via a variational inequality. We construct the locally projected dynamical system model, which provides a continuous-time evolution of views and quality levels, and whose set of stationary points coincides with the set of solutions to the variational inequality. We discuss some stability conditions using a monotonicity approach, and, finally, we present some numerical examples.

Analysing the properties of a network equilibrium can help to have a better view about network state, robustness, and the effect of any variation in the network. This study investigated the impacts of network design history on day-to-day multimodal user equilibrium. In particular, we analyze the long-term evolution of the network, including opening new multimodal options and its impacts on the final network equilibrium. First, the analysis focuses on static network loading with different successive configurations. Then, a more realistic setting is studied by simulation. A large-scale multimodal network with the flexible opening over time of three possible transport facilities shows that the final equilibrium is not unique; more importantly, significant differences can be observed in public transportation occupancy, while user equilibrium is enforced in all situations. Some solutions prove to be better from the collective viewpoint (shorter total travel time), thus giving new insight into public transport planning.

The globally projected dynamical system is a type of dynamical system constructed by the projection operator, which can be applied to solve some nonmonotone variational inequalities and constrained optimization problems. In this paper we extend the globally projected dynamical system to the complex domain and study the dynamical behaviors of the complex-valued globally projected dynamical system. By using energy-like function, Lyapunov stability method, space decomposition approach and inequality technique, the stability of linear and nonlinear complex-valued globally projected dynamical systems is studied. The simulation result shows the effectiveness of the obtained results.

In this paper, we study the stability and convergence of continuous-time Lagrangian saddle flows to solutions of a convex constrained optimization problem. Convergence of these flows is well-known when the underlying saddle function is either strictly convex in the primal or strictly concave in the dual variables. In this paper, we show convergence under non-strict convexity when a simple, unilateral augmentation term is added. For this purpose, we establish a novel, non-trivial characterization of the limit set of saddle-flow trajectories that allows us to preclude limit cycles. With our presentation we try to unify several existing problem formulations as a projected dynamical system that allows projection of both the primal and dual variables, thus complementing results available in the recent literature., IFAC-PapersOnLine, 53 (2), ISSN:2405-8963, 21th IFAC World Congress. Proceedings

This thesis focuses on the modeling chemical kinetics for reactions involving pure phases. In the first chapter, a discontinuous ODEs model for reactions with appearance and disappearance of species for any number of minerals is proposed. A regularized version of the model can prove positivity and existence. An explicit analysis in the case containing an intermediate reactive species is investigated. In the second chapter, a reformulation of the chemical kinetics model using Filippov's theory is proposed. A proof of the existence and the positivity of the solutions is given. In addition, in the case of discontinuity surfaces of codimension 1, a study of the configurations of the vector fields provides a result of uniqueness and a characterization of the trajectories. In the third chapter, a model of chemical kinetics of the projected dynamical system is proposed. An analysis of the existence of solutions of this model, links with other types of formulations and an adapted numerical resolution method are provided. An illustration of the numerical results obtained is made for chemical kinetic systems.; Cette thèse concerne la modélisation mathématique des réactions cinétiques comprenant des phases pures. Dans le premier chapitre, un modèle de type EDOs discontinues pour la cinétique avec apparitions et disparitions d'espèces pour un nombre quelconque de minéraux est proposé. Une version régularisée du modèle permet de prouver la positivité et l’existence. Une analyse explicite plus approfondie dans le cas contenant une espèce réactive intermédiaire est menée. Dans le deuxième chapitre, une reformulation du modèle de cinétique chimique utilisant la théorie de Filippov est proposée. Une preuve de l'existence et de la positivité des solutions est réalisée. De plus, dans le cas des surfaces de discontinuité de codimension 1, une étude des configurations des champs fournit un résultat d'unicité et de caractérisation des trajectoires. Dans le troisième chapitre, un modèle de cinétique chimique de type systèmes dynamiques projetés est proposé. Une analyse de l'existence des solutions de ce modèle, des liens avec d'autres types de formulations et une méthode de résolution numérique adaptée sont proposés. Enfin, une illustration des résultats numériques obtenus est réalisée pour des systèmes de cinétique chimique.

While projected dynamical systems (PDS) are mathematically well-defined and useful for modeling dynamics for constrained optimization and variational inequalities, their physical realization is not immediate. In this paper, we study how anti-windup schemes, which are ubiquitous in feedback control to deal with saturation, can be used to approximate PDS. Namely, we show that a particular class of anti-windup control loops are perturbations of PDS. This allows us to establish results about uniform convergence and robust stability of high-gain anti-windup schemes. We also establish how these insights apply in the context of feedback optimization, popularized recently for power systems and communication networks, and how our results introduce a new class of optimization dynamics that is particularly suited for this application. © 2020 AACC.

We consider continuous-time equilibrium seeking in a class of aggregative games with strongly convex cost functions and affine coupling constraints. We propose simple semidecentralized integral dynamics and prove their global asymptotic convergence to a variational generalized aggregative or Nash equilibrium. The proof is based on Lyapunov arguments and invariance techniques for differential inclusions.

International audience; This survey article addresses the class of continuous-time systems where a system modeled by ordinary differential equations (ODEs) is coupled with a static and time-varying set-valued operator in the feedback. Interconnections of this form model certain classes of nonsmooth systems including sweeping processes, differential inclusions with maximal monotone right-hand side, complementarity systems, differential and evolution variational inequalities, projected dynamical systems, some piecewise linear switching systems. Such mathematical models have seen applications in electrical circuits, mechanical systems, hysteresis effects, and many more. When we impose a passivity assumption on the open-loop system, and regard the set-valued operator in the feedback as maximally monotone, we obtain a set-valued Lur'e dynamical system. In this article we review the mathematical formalisms, their relationships, main application fields, well-posedness (existence, uniqueness, continuous dependence of solutions), and stability of equilibria. An exhaustive bibliography is provided.

This thesis focuses on the modeling chemical kinetics for reactions involving pure phases. In the first chapter, a discontinuous ODEs model for reactions with appearance and disappearance of species for any number of minerals is proposed. A regularized version of the model can prove positivity and existence. An explicit analysis in the case containing an intermediate reactive species is investigated. In the second chapter, a reformulation of the chemical kinetics model using Filippov's theory is proposed. A proof of the existence and the positivity of the solutions is given. In addition, in the case of discontinuity surfaces of codimension 1, a study of the configurations of the vector fields provides a result of uniqueness and a characterization of the trajectories. In the third chapter, a model of chemical kinetics of the projected dynamical system is proposed. An analysis of the existence of solutions of this model, links with other types of formulations and an adapted numerical resolution method are provided. An illustration of the numerical results obtained is made for chemical kinetic systems., Cette thèse concerne la modélisation mathématique des réactions cinétiques comprenant des phases pures. Dans le premier chapitre, un modèle de type EDOs discontinues pour la cinétique avec apparitions et disparitions d'espèces pour un nombre quelconque de minéraux est proposé. Une version régularisée du modèle permet de prouver la positivité et l’existence. Une analyse explicite plus approfondie dans le cas contenant une espèce réactive intermédiaire est menée. Dans le deuxième chapitre, une reformulation du modèle de cinétique chimique utilisant la théorie de Filippov est proposée. Une preuve de l'existence et de la positivité des solutions est réalisée. De plus, dans le cas des surfaces de discontinuité de codimension 1, une étude des configurations des champs fournit un résultat d'unicité et de caractérisation des trajectoires. Dans le troisième chapitre, un modèle de cinétique chimique de type systèmes dynamiques projetés est proposé. Une analyse de l'existence des solutions de ce modèle, des liens avec d'autres types de formulations et une méthode de résolution numérique adaptée sont proposés. Enfin, une illustration des résultats numériques obtenus est réalisée pour des systèmes de cinétique chimique.

Projected dynamical systems (PDS) are discontinuous dynamical systems obtained by projecting a vector field on the tangent cone of a given constraint set. As such, PDS provide a convenient formalism to model constrained dynamical systems. When dealing with vector fields, which satisfy certain monotonicity properties, but not necessarily with respect to usual Euclidean norm, the resulting PDS does not necessarily inherit this monotonicity, as we will show. However, we demonstrate that if the projection is carried out with respect to a well-chosen norm, then the resulting 'oblique PDS' preserves the monotonicity of the unconstrained dynamics. This feature is especially desirable as monotonicity allows to guarantee important (incremental) stability properties and stability of periodic solutions (under periodic excitation). These properties can now be guaranteed based on the unconstrained dynamics using 'smart' projection instead of having to carry out a difficult a posteriori analysis on a constrained discontinuous dynamical system. To illustrate this, an application in the context of observer re-design is presented, which guarantees that the state estimate lies in the same state set as the observed state trajectory.

In this paper, we construct an evolutionary (time-dependent) split variational inequality problem and show how to reformulate equilibria of the dynamic traffic network models of two cities as such problem. We also establish existence result for the proposed model. Primary numerical results of equilibria illustrate the validity and applicability of our results.

In this paper, a distributed resource allocation problem with nonsmooth local cost functions is considered, where the interaction among agents is depicted by strongly connected and weight-balanced digraphs. Here the decision variable of each agent is within a local feasibility constraint described as a convex set, and all the decision variables have to satisfy a network resource constraint, which is the sum of available resources. To solve the problem, a distributed continuous-time algorithm is developed by virtue of differentiated projection operations and differential inclusions, and its convergence to the optimal solution is proved via the set-valued LaSalle invariance principle. Furthermore, the exponential convergence of the proposed algorithm can be achieved when the local cost functions are differentiable with Lipschitz gradients and there are no local feasibility constraints. Finally, numerical examples are given to verify the effectiveness of the proposed algorithms.

In this paper, we develop a dynamic equilibrium model of oligopolistic closed-loop supply chain network to account for the seasonality of demand. In this model, demands and returns are uncertain and time-dependent. The dynamic Cournot-Nash equilibrium of the oligopolistic network is constructed by evolutionary variational inequality and projected dynamical systems. The existence and monotonicity results are established and a computational method is developed for finding the dynamic Cournot-Nash equilibrium. Numerical examples are solved to illustrate the performance of our model. The results show that the firms’ optimal production and transaction quantities are strongly affected by the time-dependent demands.

The class of projected dynamical systems (PDS) has proven to be a powerful framework for modeling dynamical systems of which the trajectories are constrained to a set by means of projection. However, PDS fall short in modeling systems in which the constraint set does not satisfy certain regularity conditions and only part of the dynamics can be projected. This poses limitations in terms of the phenomena that can be described in this framework especially in the context of systems and control. Motivated by hybrid integrator-gain systems (HIGS), which are recently proposed control elements in the literature that aim at overcoming fundamental limitations of linear time-invariant feedback control, a new class of discontinuous dynamical systems referred to as extended projected dynamical systems (ePDS) is introduced in this paper. Extended projected dynamical systems include PDS as a special case and are well-defined for a wider variety of constraint sets as well as partial projections of the dynamics. In this paper, the ePDS framework is connected to the classical PDS literature and is subsequently used to provide a formal mathematical description of a HIGS-controlled system, which was lacking in the literature so far. Based on the latter result, HIGS-controlled systems are shown to be well-posed, in the sense of global existence of solutions.

The dynamic traffic equilibrium problem has been a pivotal area of research in transportation for a few decades. The current state-of-the-art research in this area generally considers simultaneous departure time and route choice. In this paper, we theoretically prove that, in the simplest standard bottleneck model with a sufficient number of commuters, a dynamic user equilibrium (DUE) cannot be reached through a day-to-day evolution process of travelers’ departure rate—a central idea behind the iterative dynamic traffic equilibrium algorithms prevalent in the literature. We carry out our exploration with the proportional swap system and then extend our analysis to other typical dynamic equilibrium algorithms such as the network tatonnement process, the simplex gravity flow dynamics, the projected dynamical system, and the evolutionary traffic dynamics. Our investigation indicates that any algorithm analogous to the five categories of systems must be cautious in terms of convergence when it is applied to solve the DUE problem with a departure time choice, and hence puts forward a general question: Are we really solving the DUE problem with a departure time choice?

We investigate the global exponential stability of equilibrium solutions of a projected dynamical system for variational inequalities. Under strong pseudomonotonicity and Lipschitz continuity assumptions, we prove that the dynamical system has a unique equilibrium solution. Moreover, this solution is globally exponentially stable. Some examples are given to analyze the effectiveness of the theoretical results. The numerical results confirm that the trajectory of the dynamical system globally exponentially converges to the unique solution of the considered variational inequality. The results established in this paper improve and extend some recent works.

We propose a multi-time generalized Nash equilibrium problem and prove its equivalence with a multi-time quasi-variational inequality problem. Then, we establish the existence of equilibria. Furthermore, we demonstrate that our multi-time generalized Nash equilibrium problem can be applied to solving traffic network problems, the aim of which is to minimize the traffic cost of each route and to solving a river basin pollution problem. Moreover, we also study the proposed multi-time generalized Nash equilibrium problem as a projected dynamical system and numerically illustrate our theoretical results.

This paper examines the performance of the 3rd and 4th order implicit Adams methods in the framework of the two-loop implicit sparse matrix numerical integration method in solving the differential/algebraic equations of heavily constrained dynamical systems. The variable-step size two-loop implicit sparse matrix numerical integration/Adams method proposed in this investigation avoids numerical force differentiation, ensures satisfying the nonlinear algebraic constraint equations at the position, velocity, and acceleration levels, and allows using sparse matrix techniques for efficiently solving the dynamical equations. The iterative outer loop of the two-loop implicit sparse matrix numerical integration/Adams method is aimed at achieving the convergence of the implicit integration formulae used to solve the independent differential equations of motion, while the inner loop is used to ensure the convergence of the iterative procedure used to satisfy the algebraic constraint equations. To solve the independent differential equations, two different implicit Adams integration formulae are examined in this investigation; a 3rd order implicit Adams-Moulton formula with a 2nd order explicit predictor Adams Bashforth formula, and a 4th order implicit Adams-Moulton formula with a 3rd order explicit predictor Adams Bashforth formula. A standard Newton–Raphson algorithm is used to satisfy the nonlinear algebraic constraint equations at the position level. The constraint equations at the velocity and acceleration levels are linear, and therefore, there is no need for an iterative procedure to solve for the dependent velocities and accelerations. The algorithm used for the error check and step-size change is described. The performance of the two-loop implicit sparse matrix numerical integration/Adams algorithm developed in this investigation is evaluated by comparison with the explicit predictor-corrector Adams method which has a variable-order and variable-step size. Simple and heavily constrained dynamical systems are used to evaluate the accuracy, robustness, damping characteristics, and effect of the outer-loop iterations of the proposed implicit schemes. The results obtained in this investigation show that the two-loop implicit sparse matrix numerical integration methods proposed in this study can be more efficient for stiff systems because of their ability to damp out high-frequency oscillations. Explicit integration methods, on the other hand, can be more efficient in the case of non-stiff systems.

In this paper, we propose a novel, simple, and unified approach to explore sufficient and necessary conditions, i.e., invariance conditions, under which four classic families of convex sets, namely, polyhedra, polyhedral cones, ellipsoids, and Lorenz cones, are invariant sets for a linear discrete or continuous dynamical system.For discrete dynamical systems, we use the Theorems of Alternatives, i.e., Farkas lemma and S-lemma, to obtain simple and general proofs to derive invariance conditions. This novel method establishes a solid connection between optimization theory and dynamical system. Also, using the S -lemma allows us to extend invariance conditions to any set represented by a quadratic inequality. Such sets include nonconvex and unbounded sets.For continuous dynamical systems, we use the forward or backward Euler method to obtain the corresponding discrete dynamical systems while preserves invariance. This enables us to develop a novel and elementary method to derive invariance conditions for continuous dynamical systems by using the ones for the corresponding discrete systems.Finally, some numerical examples are presented to illustrate these invariance conditions.

The method of integration of dynamical systems admitting non-linear superpositions is applied to four-dimensional non-linear dynamical systems. All four-dimensional dynamical systems admitting non-linear superpositions with four-dimensional Vessiot-Guldberg-Lie algebras are classified into 160 standard forms. The integration method is described and illustrated.

In this paper some properties of solutions are obtained for semi-linear fuzzy differential equations understood as differential inclusions, i.e., semi-linear uncertain dynamical systems. The definitions of the solution to the semi-linear uncertain dynamical system and its corresponding big solution are given. The existence of the two kinds of solutions is established, and the inclusion relation between them is also obtained.

In this paper, the distributed resource allocation optimization problem is investigated. The allocation decisions are made to minimize the sum of all the agents' local objective functions while satisfying both the global network resource constraint and the local allocation feasibility constraints. Here the data corresponding to each agent in this separable optimization problem, such as the network resources, the local allocation feasibility constraint, and the local objective function, is only accessible to individual agent and cannot be shared with others, which renders new challenges in this distributed optimization problem. Based on either projection or differentiated projection, two classes of continuous-time algorithms are proposed to solve this distributed optimization problem in an initialization-free and scalable manner. Thus, no re-initialization is required even if the operation environment or network configuration is changed, making it possible to achieve a "plug-and-play" optimal operation of networked heterogeneous agents. The algorithm convergence is guaranteed for strictly convex objective functions, and the exponential convergence is proved for strongly convex functions without local constraints. Then the proposed algorithm is applied to the distributed economic dispatch problem in power grids, to demonstrate how it can achieve the global optimum in a scalable way, even when the generation cost, or system load, or network configuration, is changing., Comment: 13 pages, 7 figures

We consider strongly monotone games with convex separable coupling constraints, played by dynamical agents, in a partial-decision information scenario. We start by designing continuous-time fully distributed feedback controllers, based on consensus and primal-dual gradient dynamics, to seek a generalized Nash equilibrium in networks of single-integrator agents. Our first solution adopts a fixed gain, whose choice requires the knowledge of some global parameters of the game. To relax this requirement, we conceive a controller that can be tuned in a completely decentralized fashion, thanks to the use of uncoordinated integral adaptive weights. We further introduce algorithms specifically devised for generalized aggregative games. Finally, we adapt all our control schemes to deal with heterogeneous multi-integrator agents and, in turn, with nonlinear feedback-linearizable dynamical systems. For all the proposed dynamics, we show convergence to a variational equilibrium, by leveraging monotonicity properties and stability theory for projected dynamical systems., Comment: Accepted in Automatica

We consider a system of single- or double integrator agents playing a generalized Nash game over a network, in a partial-information scenario. We address the generalized Nash equilibrium seeking problem by designing a fully-distributed dynamic controller, based on continuous-time consensus and primal-dual gradient dynamics. Our main technical contribution is to show convergence of the closed-loop system to a variational equilibrium, under strong monotonicity and Lipschitz continuity of the game mapping, by leveraging monotonicity properties and stability theory for projected dynamical systems., Comment: Accepted to the ECC2020

This paper is concerned with the study of continuous-time, non-smooth dynamical systems which arise in the context of time-varying non-convex optimization problems, as for example the feedback-based optimization of power systems. We generalize the notion of projected dynamical systems to time-varying, possibly non-regular, domains and derive conditions for the existence of so-called Krasovskii solutions. The key insight is that for trajectories to exist, informally, the time-varying domain can only contract at a bounded rate whereas it may expand discontinuously. This condition is met, in particular, by feasible sets delimited via piecewise differentiable functions under appropriate constraint qualifications. To illustrate the necessity and usefulness of such a general framework, we consider a simple yet insightful power system example, and we discuss the implications of the proposed conditions for the design of feedback optimization schemes.

In [Y. Kifer, Averaging in difference equations driven by dynamical systems, Asterisque287 (2003) 103–123] a general averaging principle for slow-fast discrete dynamical systems was presented. In this paper we extend this method to weakly coupled slow-fast systems. For this setting we obtain sharper estimates than in the mentioned paper.

We study a dynamical system generalizing continuous iterated function systems and stochastic differential equations disturbed by Poisson noise. The aim of this paper is to study stochastic processes whose paths follow deterministic dynamics between random times, jump times, at which they change their position randomly. Continuous random dynamical systems can be used as a description of many physical and biological phenomena. We prove the existence of an exponentially attractive invariant measure and the strong law of large numbers for continuous random dynamical systems. We illustrate the usefulness of our criteria for asymptotic stability by considering a general d -dimensional model for the intracellular biochemistry of a generic cell with a probabilistic division hypothesis (see Lasota and Mackey, 1999).

Large-scale systems of nonlinear equations appear in many applications. In various applications, the solution of the nonlinear equations should also be in a certain interval. A typical application is a discretized system of reaction diffusion equations. It is well known that chemical species should be positive otherwise the solution is not physical and in general blow up occurs. Recently, a projected Newton method has been developed, which can be used to solve this type of problems. A drawback is that the projected Newton method is not globally convergent. This motivates us to develop a new feasible projected Newton–Krylov algorithm for solving a constrained system of nonlinear equations. Combined with a projected gradient direction, our feasible projected Newton–Krylov algorithm circumvents the non-descent drawback of search directions which appear in the classical projected Newton methods. Global and local superlinear convergence of our approach is established under some standard assumptions. Numerical experiments are used to illustrate that the new projected Newton method is globally convergent and is a significate complementarity for Newton–Krylov algorithms known in the literature.

In this paper, we consider a class of optimal control problem for the singularly perturbed hybrid dynamical systems. By means of variational method, we obtain the necessary conditions of the hybrid dynamical systems. Meanwhile, the existence of solution for the hybrid dynamical system is proved by the sewing method and the uniformly valid asymptotic expansion of the optimal trajectory is constructed by the boundary function method. Finally, an example is presented to illustrate the result.

In this paper we want to show the existence of a form of asymptotic stability of random dynamical systems in the sense of L. Arnold using arguments analogous to those presented by T. Szarek in [6], that is showing it using conditions generalizing the notion of tightness of measures. In order to do that we use tightness theory for random measures as developed by H. Crauel in [2].