mlsanityData to 14 Oct 2026

Convergence of a Stochastic Subgradient Method with Averaging for Nonsmooth Nonconvex Constrained Optimization

arXiv 1912.07580 ↗First posted 16 Dec 2019math.OCcited by 10

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Abstract (arXiv)

We prove convergence of a single time-scale stochastic subgradient method with subgradient averaging for constrained problems with a nonsmooth and nonconvex objective function having the property of generalized differentiability. As a tool of our analysis, we also prove a chain rule on a path for such functions.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 3; 2025: 0; 2026: 1.

10 papers.

  1. 2022Policy Gradient Method For Robust Reinforcement Learningcited by 69
  2. 2021A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learningcited by 24
  3. 2022Langevin dynamics based algorithm e-THvarepsilonO POULA for stochastic optimization problems with discontinuous stochastic gradientcited by 5
  4. 2023Adam-family Methods with Decoupled Weight Decay in Deep Learningcited by 5
  5. 2024A Functional Model Method for Nonconvex Nonsmooth Conditional Stochastic Optimizationcited by 4
  6. 2023Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimizationcited by 3
  7. 2024Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimizationcited by 2
  8. 2023Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learningcited by 1
  9. 2024Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functionscited by 1
  10. 2026Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraintscited by 0