This work provides a deterministic dynamic stepsize schedule that accelerates the convergence rate of EG to $\mathcal{O}(T^{-2/3+\varepsilon})$ for any $\varepsilon>0$ and shows that this rate is nearly tight when the extrapolation and update steps of EG use the same stepsize.
Yue Wu, Weiqiang Zheng, Yang Cai et al.· arXiv.org· 0 citations
The authors prove that no instance-independent last-iterate rate exists for the broad algorithmic family and establish strong lower bounds for random iterates and suggest that practitioners should distinguish carefully between the latest strategy, a randomly selected strategy, and the best observed strategy when evalua...
Yang Cai, Gabriele Farina, J. Grand-Clément et al.· Operational Research· 1 citation
This work proposes a family of symmetric multi-player alignment games and proves that any symmetric Nash equilibrium policy of the $(k+1)-player alignment game achieves the optimal $(k,\frac{k}{k+1})$-robust alignment.
The complexity of IVF(C)CEs in succinct games, which model more realistic strategic interactions that feature either many players or exponentially many pure strategies, is examined, showing that it is at least as hard as the P-matrix linear complementarity problem, and hence as hard as simple stochastic games.
The minimax-optimal alternating regret is settled, a regret notion motivated by alternating learning dynamics in games, for both online linear optimization (OLO) and online convex optimization (OCO) and a matching lower bound is proved of the $\Omega(\log T)$ factor.
Yixin Tao, Weiqiang Zheng· 2 citations
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