5 research outputs found
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
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
Continuous-time integral dynamics for aggregative game equilibrium seeking
In this paper, we consider continuous-time semidecentralized 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