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A Note on the Convergence Rate of the Nonmonotone Adaptive Cubic Regularization Method

Bai Jianchao & Sun Qixuan

Pacific Journal of Optimization2026https://doi.org/10.61208/pjo-2026-002article
AJG 1
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0.50

What the paper says

(Communicated by Xinmin Yang) This note is to analyze the linear convergence rate of the algorithm proposed in [Li, Q., Zheng, B., Zheng, Y.T.: An efficient nonmonotone adaptive cubic regularization method with line search for unconstrained optimization problem. Appl. Math. Lett. 98, 74-80 (2019)] under the assumption that the objective function is strongly convex.

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https://doi.org/https://doi.org/10.61208/pjo-2026-002

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@article{bai2026,
  title        = {{A Note on the Convergence Rate of the Nonmonotone Adaptive Cubic Regularization Method}},
  author       = {Bai Jianchao & Sun Qixuan},
  journal      = {Pacific Journal of Optimization},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.61208/pjo-2026-002},
}

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A Note on the Convergence Rate of the Nonmonotone Adaptive Cubic Regularization Method

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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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