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  1. ; ; ( , Automatica)
    Free, publicly-accessible full text available January 1, 2025
  2. ; ( , Proc. 2023 American Control Conference)
  3. ; ; ( , SIAM Journal on Control and Optimization)
    Free, publicly-accessible full text available June 30, 2024
  4. ; ( , IEEE Transactions on Control of Network Systems)
    Free, publicly-accessible full text available June 1, 2024
  5. ; ( , IEEE Open Journal of Control Systems)
    Full Text Available 
  6. ; ( , Proc. Machine Learning Research)
    Accepted Manuscript Full Text Available
  7. ; ( , Proc. Machine Learning Research)
    Accepted Manuscript Full Text Available
  8. ; ( , Proc. 2022 American Control Conference)
    We consider the stochastic generalized Nash equilibrium problem (SGNEP) where a set of self-interested players, subject to certain global constraints, aim to optimize their local objectives that depend on their own decisions and the decisions of others and are influenced by some random factors. A distributed stochastic generalized Nash equilibrium seeking algorithm is proposed based on the Douglas-Rachford operator splitting scheme, which only requires local communications among neighbors. The proposed scheme significantly relaxes assumptions on co-coercivity and contractiveness in the existing literature, where the projected stochastic subgradient method is applied to provide approximate solutions to the augmented best-response subproblems for each player. Finally, we illustrate the validity of the proposed algorithm through a Nash-Cournot production game. 
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    Full Text Available 
  9. ( , 2022 American Control Conference)
    We consider the stochastic generalized Nash equilibrium problem (SGNEP) where a set of self-interested players, subject to certain global constraints, aim to optimize their local objectives that depend on their own decisions and the decisions of others and are influenced by some random factors. A distributed stochastic generalized Nash equilibrium seeking algorithm is proposed based on the Douglas-Rachford operator splitting scheme, which only requires local communications among neighbors. The proposed scheme significantly relaxes assumptions on co-coercivity and contractiveness in the existing literature, where the projected stochastic subgradient method is applied to provide approximate solutions to the augmented best-response subproblems for each player. Finally, we illustrate the validity of the proposed algorithm through a Nash-Cournot production game. 
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    Accepted Manuscript Full Text Available
  10. ; ; ( , IEEE Access)