Complexity of finding near-stationary points of convex functions stochastically
Not on the map. Why?
Abstract (arXiv)
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).
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Feb 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1.
4 papers.
- 2023Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimizationcited by 11
- 2022On the Complexity of Finding Small Subgradients in Nonsmooth Optimizationcited by 7
- 2021Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sumscited by 4
- 2024Causal Invariance Learning via Efficient Nonconvex Optimizationcited by 2
- 2024Causal Invariance Learning via Efficient Nonconvex Optimization16 Dec 2024
- 2023Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization30 Jan 2023
- 2022On the Complexity of Finding Small Subgradients in Nonsmooth Optimization21 Sep 2022
- 2021Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums25 May 2021