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Tuning-Free Decentralized Nonconvex Stochastic Optimization

Li Jiaxiang et al.

Pacific Journal of Optimization2025https://doi.org/10.61208/pjo-2025-009article
AJG 1
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0.50

What the paper says

Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually require the knowledge of Lipschitz constant of the global gradient or topological information of the communication networks, which are usually not accessible in practice. In this paper, we propose D-NASA, the first algorithm for decentralized nonconvex stochastic optimization that requires no prior knowledge of any problem parameters. We show that D-NASA has the optimal rate of convergence for nonconvex objectives under very mild conditions and enjoys the linear-speedup effect, i.e. the computation becomes faster as the number of nodes in the system increases. Extensive numerical experiments are conducted to support our findings.

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https://doi.org/https://doi.org/10.61208/pjo-2025-009

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@article{li2025,
  title        = {{Tuning-Free Decentralized Nonconvex Stochastic Optimization}},
  author       = {Li Jiaxiang et al.},
  journal      = {Pacific Journal of Optimization},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.61208/pjo-2025-009},
}

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Tuning-Free Decentralized Nonconvex Stochastic Optimization

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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