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Sum-of-Squares meets Nash: Optimal Lower Bounds for Finding any Equilibrium
[article]
2018
arXiv
pre-print
Several works have shown unconditional hardness (via integrality gaps) of computing equilibria using strong hierarchies of convex relaxations. Such results however only apply to the problem of computing equilibria that optimize a certain objective function and not to the (arguably more fundamental) task of finding any equilibrium. We present an algorithmic model based on the sum-of-squares (SoS) hierarchy that allows escaping this inherent limitation of integrality gaps. In this model,
arXiv:1806.09426v1
fatcat:bxqoxselcbednpo3i64ehflu5m