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Making the gradients small is a fundamental optimization problem that has eluded unifying and simple convergence arguments in first-order optimization, so far primarily reserved for other convergence criteria, such as reducing the optimality gap.
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Last iterate is slower than averaged iterate in smooth convex-concave saddle point problems
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