$\mu _{k} \propto 1/\sqrt{k}$, along with other assumptions, the regret bound of the proposed algorithm is

$O(\sqrt{K})$

(

$k$

and

$K$

represent the current round of online algorithm and the total rounds of games played, respectively). Finally, we conduct numerical simulations on the parameter identification of a Nash–Cournot problem to demonstrate that the performance of our proposed online algorithm is comparable to that of the offline setting.">

Online Parameter Identification of Cost Functions in Generalized Nash Games (original) (raw)

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