REVIEW 1 cited by
Complexity of finding near-stationary points of convex functions stochastically
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
abstract
In a recent paper, we showed that the stochastic subgradient method applied to a weakly convex problem, drives the gradient of the Moreau envelope to zero at the rate $O(k^{-1/4})$. In this supplementary note, we present a stochastic subgradient method for minimizing a convex function, with the improved rate $\widetilde O(k^{-1/2})$.
Forward citations
Cited by 1 Pith paper
-
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
A proximally constrained subgradient method finds a nearly stationary point for weakly convex objectives with weakly convex constraints in O(1/epsilon^4) deterministic and O~(1/epsilon^6) stochastic iterations.
Discussion (0). Continue with ORCID to comment.