Parallel block coordinate descent methods with identification strategies
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915426500345856 |
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| author | Lopes, Ronaldo Santos, Sandra A. Silva, Paulo J. S. |
| author_facet | Lopes, Ronaldo Santos, Sandra A. Silva, Paulo J. S. |
| contents | 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 non-smooth block-separable convex function under simple constraints. It achieves better efficiency by using a strategy to identify the nonzero coordinates that allows the computational effort to be focused on using a nonuniform probability distribution in the selection of the blocks. Parallelization is achieved by extending the 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 data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parallel block coordinate descent methods with identification strategies Lopes, Ronaldo Santos, Sandra A. Silva, Paulo J. S. Optimization and Control 60K05, 65Y05, 49M37, 90C30, 90C06, 90C25 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 non-smooth block-separable convex function under simple constraints. It achieves better efficiency by using a strategy to identify the nonzero coordinates that allows the computational effort to be focused on using a nonuniform probability distribution in the selection of the blocks. Parallelization is achieved by extending the 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 data. |
| title | Parallel block coordinate descent methods with identification strategies |
| topic | Optimization and Control 60K05, 65Y05, 49M37, 90C30, 90C06, 90C25 |
| url | https://arxiv.org/abs/2507.22277 |