mlsanityData to 14 Oct 2026

Pathological subgradient dynamics

arXiv 1910.13604 ↗First posted 30 Oct 2019math.OCcited by 9

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Abstract (arXiv)

We construct examples of Lipschitz continuous functions, with pathological subgradient dynamics both in continuous and discrete time. In both settings, the iterates generate bounded trajectories, and yet fail to detect any (generalized) critical points of the function.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2019.

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Cited by year (2026 to 14 Oct)

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

9 papers.

  1. 2022Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimizationcited by 43
  2. 2023Deterministic Nonsmooth Nonconvex Optimizationcited by 26
  3. 2021Stochastic Subgradient Descent Escapes Active Strict Saddles on Weakly Convex Functionscited by 9
  4. 2023Adam-family Methods with Decoupled Weight Decay in Deep Learningcited by 5
  5. 2024Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimizationcited by 2
  6. 2024On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimizationcited by 0
  7. 2025Permutation Randomization on Nonsmooth Nonconvex Optimization: A Theoretical and Experimental Studycited by 0
  8. 2026Lipschitzian SLLNs for random functionscited by 0
  9. 2026The Exponential Price of Determinism in Nonsmooth Nonconvex Optimizationcited by 0