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Parallel block coordinate descent methods with identification strategies

Ronaldo Lopes et al.

Computational Optimization and Applications2026https://doi.org/10.1007/s10589-026-00773-5article
AJG 3
Weight
0.37

What the paper says

This work presents a parallel variant of the algorithm introduced in [ Acceleration of block coordinate descent methods with identification strategies , Comput. Optim. Appl. 72(3):609–640, 2019] to minimize the sum of a partially separable smooth convex function and a possibly nonsmooth block-separable convex function under simple constraints. The proposed method achieves higher efficiency by using a strategy to identify nonzero coordinates, thereby allowing the computational effort to be focused via a nonuniform probability distribution in block selection. Parallelization is achieved by extending theoretical results from Richtárik and Takáč [ Parallel coordinate descent methods for big data optimization , Math. Prog. Ser. A 156:433–484, 2016]. We present convergence results and comparative numerical experiments on regularized regression problems using both synthetic and real datasets.

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https://doi.org/https://doi.org/10.1007/s10589-026-00773-5

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@article{ronaldo2026,
  title        = {{Parallel block coordinate descent methods with identification strategies}},
  author       = {Ronaldo Lopes et al.},
  journal      = {Computational Optimization and Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10589-026-00773-5},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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