Distributed GNE seeking over networks in aggregative games with coupled constraints via forward-backward operator splitting

Gadjov, Dian; Pavel, Lacra

doi:10.1109/cdc40024.2019.9029369

Cited by 16 publications

(9 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually, A : R n ⇒ R n and B : R n → R n are a set valued and a single valued monotone operator, respectively. Inclusions as the above arise systematically in convex optimization [5,19,35,90] and generalized Nash equilibrium problems in convex-monotone games [11,14,21,107,108,109].…”

Section: Applications Of Convergent Deterministic Sequencesmentioning

confidence: 99%

Convergence of sequences: a survey

Franci¹,

Grammatico²

2021

Preprint

View full text Add to dashboard Cite

Convergent sequences of real numbers play a fundamental role in many different problems in system theory, e.g., in Lyapunov stability analysis, as well as in optimization theory and computational game theory. In this survey, we provide an overview of the literature on convergence theorems and their connection with Féjer monotonicity in the deterministic and stochastic settings, and we show how to exploit these results.

show abstract

Section: Applications Of Convergent Deterministic Sequencesmentioning

confidence: 99%

Convergence of sequences: a survey

Franci¹,

Grammatico²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…dual and auxiliary variables. The latter algorithmic setup is also called partialdecision information [20], [21], because the agents do not have direct access to the aggregative effect on their cost functions, thus they should estimate it via reliable, truthful peer-to-peer communications, e.g. via cooperative consensus protocols.…”

Section: A Aggregative Gamesmentioning

confidence: 99%

Semi-decentralized generalized Nash equilibrium seeking in monotone aggregative games

Belgioioso,

Grammatico

2020

Preprint

View full text Add to dashboard Cite

We address the generalized Nash equilibrium seeking problem for a population of agents playing aggregative games with affine coupling constraints. We focus on semi-decentralized communication architectures, where there is a central coordinator able to gather and broadcast signals of aggregative nature to the agents. By exploiting the framework of monotone operator theory and operator splitting, we first critically review the most relevant available algorithms and then design two novel schemes: (i) a single-layer, fixed-step algorithm with convergence guarantee for general (non-cocoercive, non-strictly) monotone aggregative games and (ii) a single-layer proximal-type algorithm for a class of monotone aggregative games with linearly coupled cost functions. We also design novel accelerated variants of two algorithms via (alternating) inertial steps. Finally, we show via numerical simulations that the proposed algorithms outperform those in the literature in terms of convergence speed.

show abstract

“…Due to the aforementioned coupling, information on other agents must be communicated, observed, or measured in order to compute a GNE algorithmically. The nature of this information can vary from knowing everything (full knowledge of the agent actions) [48], estimates based on distributed consensus between the agents [12], to payoff-based estimates [32], [11]. The latter is of special interest as it requires no dedicated inter-agent communication infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Learning generalized Nash equilibria in monotone games: A hybrid adaptive extremum seeking control approach

Krilašević¹,

Grammatico²

2021

Preprint

View full text Add to dashboard Cite

In this paper, we solve the problem of learning a generalized Nash equilibrium (GNE) in merely monotone games. First, we propose a novel continuous semi-decentralized solution algorithm without projections that uses first-order information to compute a GNE with a central coordinator. As the second main contribution, we design a gain adaptation scheme for the previous algorithm in order to alleviate the problem of improper scaling of the cost functions versus the constraints. Third, we propose a data-driven variant of the former algorithm, where each agent estimates their individual pseudogradient via zeroth-order information, namely, measurements of their individual cost function values. Finally, we apply our method to a perturbation amplitude optimization problem in oil extraction engineering.

show abstract

Distributed GNE seeking over networks in aggregative games with coupled constraints via forward-backward operator splitting

Cited by 16 publications

References 38 publications

Convergence of sequences: a survey

Convergence of sequences: a survey

Semi-decentralized generalized Nash equilibrium seeking in monotone aggregative games

Learning generalized Nash equilibria in monotone games: A hybrid adaptive extremum seeking control approach

Contact Info

Product

Resources

About