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Since this is obviously unrealistic, we explored milder assumptions.
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Note that we did not use this assumption in deriving the role of the adaptation conductance (Claim 1 and following text), but only the milder assumption that (langle x rangle) decreases monotonically with z.
(1-1/e)-competitive 1-1/e -competitiveme 1-1/e -competitive
A moment restriction model is semiparametric and distribution-free, therefore it imposes mild assumptions.
Global convergence of the proposed algorithm is established under two mild assumptions.
Convergence to a stationary solution of the social non-convex problem is guaranteed under mild assumptions.
After some mild assumptions, a set of Pfaffian constraints is established.
We also propose the design method for control gains under some mild assumptions.
Convergence proof of the stochastic gradient algorithm is derived making mild assumptions.
We show that the proposed algorithm satisfies an important ergodicity condition under some mild assumptions.
We prove the global convergence properties of the algorithm under mild assumptions.
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