9 citations · 20 across the 22 of their papers we have counts for
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math.OC2017
Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity
Benjamin Grimmer
We extend the classic convergence rate theory for subgradient methods to apply to non-Lipschitz functions. For the deterministic projected subgradient method, we present a global $…
math.OC2017
Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems
Damek Davis, Benjamin Grimmer
In this paper, we introduce a stochastic projected subgradient method for weakly convex (i.e., uniformly prox-regular) nonsmooth, nonconvex functions---a wide class of functions wh…
math.OC2017★ 9 cited
Radial Subgradient Method
Benjamin Grimmer
We present a subgradient method for minimizing non-smooth, non-Lipschitz convex optimization problems. The only structure assumed is that a strictly feasible point is known. We ext…