Equilibrium seeking in games under partial-decision information

The topic of this dissertation is the distributed computation of Generalized Nash Equilibria (GNEs) in multi-agent games with network structure. In particular, we design and analyze algorithms in the partial-decision information scenario (also named fully-distributed algorithms), where each agent can only rely on the information received by some...

The Distributed Dual Ascent Algorithm is Robust to Asynchrony

The distributed dual ascent is an established algorithm to solve strongly convex multi-agent optimization problems with separable cost functions, in the presence of coupling constraints. In this letter, we study its asynchronous counterpart. Specifically, we assume that each agent only relies on the outdated information received from some...

Fast generalized Nash equilibrium seeking under partial-decision information

We address the generalized Nash equilibrium seeking problem in a partial-decision information scenario, where each agent can only exchange information with some neighbors, although its cost function possibly depends on the strategies of all agents. The few existing methods build on projected pseudo-gradient dynamics, and require either double...

Nash equilibrium seeking under partial-decision information: monotonicity, smoothness and proximal-point algorithms

We consider Nash equilibrium problems in a partial-decision information scenario, where each agent can only exchange information with some neighbors, while its cost function possibly depends on the strategies of all agents. We characterize the relation between several monotonicity and smoothness assumptions postulated in the literature....

Fully distributed Nash equilibrium seeking over time-varying communication networks with linear convergence rate

We design a distributed algorithm for learning Nash equilibria over time-varying communication networks in a partial-decision information scenario, where each agent can access its own cost function and local feasible set, but can only observe the actions of some neighbors. Our algorithm is based on projected pseudo-gradient dynamics, augmented...

Continuous-time fully distributed generalized Nash equilibrium seeking for multi-integrator agents

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...

A fully-distributed proximal-point algorithm for Nash equilibrium seeking with linear convergence rate

We address the Nash equilibrium problem in a partial-decision information scenario, where each agent can only observe the actions of some neighbors, while its cost possibly depends on the strategies of other agents. Our main contribution is the design of a fully-distributed, single-layer, fixed-step algorithm, based on a proximal best...

Nash equilibrium seeking under partial-decision information over directed communication networks

We consider the Nash equilibrium problem in a partial-decision information scenario. Specifically, each agent can only receive information from some neighbors via a communication network, while its cost function depends on the strategies of possibly all agents. In particular, while the existing methods assume undirected or balanced...

A continuous-time distributed generalized Nash equilibrium seeking algorithm over networks for double-integrator agents

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...