1,245 research outputs found
Distributed averaging integral Nash equilibrium seeking on networks
Continuous-time gradient-based Nash equilibrium seeking algorithms enjoy a
passivity property under a suitable monotonicity assumption. This feature has
been exploited to design distributed algorithms that converge to Nash
equilibria and use local information only. We further exploit the passivity
property to interconnect the algorithms with distributed averaging integral
controllers that tune on-line the weights of the communication graph. The main
advantage is to guarantee convergence to a Nash equilibrium without requiring a
strong coupling condition on the algebraic connectivity of the communication
graph over which the players exchange information, nor a global high-gain
On the linear convergence of distributed Nash equilibrium seeking for multi-cluster games under partial-decision information
This paper considers the distributed strategy design for Nash equilibrium
(NE) seeking in multi-cluster games under a partial-decision information
scenario. In the considered game, there are multiple clusters and each cluster
consists of a group of agents. A cluster is viewed as a virtual noncooperative
player that aims to minimize its local payoff function and the agents in a
cluster are the actual players that cooperate within the cluster to optimize
the payoff function of the cluster through communication via a connected graph.
In our setting, agents have only partial-decision information, that is, they
only know local information and cannot have full access to opponents'
decisions. To solve the NE seeking problem of this formulated game, a
discrete-time distributed algorithm, called distributed gradient tracking
algorithm (DGT), is devised based on the inter- and intra-communication of
clusters. In the designed algorithm, each agent is equipped with strategy
variables including its own strategy and estimates of other clusters'
strategies. With the help of a weighted Fronbenius norm and a weighted
Euclidean norm, theoretical analysis is presented to rigorously show the linear
convergence of the algorithm. Finally, a numerical example is given to
illustrate the proposed algorithm
Continuous-time integral dynamics for Aggregative Game equilibrium seeking
In this paper, we consider continuous-time semi-decentralized dynamics for
the equilibrium computation in a class of aggregative games. Specifically, we
propose a scheme where decentralized projected-gradient dynamics are driven by
an integral control law. To prove global exponential convergence of the
proposed dynamics to an aggregative equilibrium, we adopt a quadratic Lyapunov
function argument. We derive a sufficient condition for global convergence that
we position within the recent literature on aggregative games, and in
particular we show that it improves on established results
A Feedback Control Algorithm to Steer Networks to a Cournot-Nash Equilibrium
We propose a distributed feedback control that steers a dynamical network to
a prescribed equilibrium corresponding to the so-called Cournot-Nash
equilibrium. The network dynamics considered here are a class of passive
nonlinear second-order systems, where production and demands act as external
inputs to the systems. While productions are assumed to be controllable at each
node, the demand is determined as a function of local prices according to the
utility of the consumers. Using reduced information on the demand, the proposed
controller guarantees the convergence of the closed loop system to the optimal
equilibrium point dictated by the Cournot-Nash competition
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