For stochastic convex optimization, a randomized proximal-point algorithm achieves asymptotic ε-stationarity — the subdifferential genuinely contains a small element — without smoothness or Lipschitz-gradient assumptions.
Distribution free M-estimation
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abstract
The basic question of delineating those statistical problems that are solvable without making any assumptions on the underlying data distribution has long animated statistics and learning theory. This paper characterizes when a convex M-estimation or stochastic optimization problem is solvable in such an assumption-free setting, providing a precise dividing line between solvable and unsolvable problems. The conditions we identify show, perhaps surprisingly, that Lipschitz continuity of the loss being minimized is not necessary for distribution free minimization, and they are also distinct from classical characterizations of learnability in machine learning.
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Finding a stationary point of a stochastic convex problem
For stochastic convex optimization, a randomized proximal-point algorithm achieves asymptotic ε-stationarity — the subdifferential genuinely contains a small element — without smoothness or Lipschitz-gradient assumptions.