{"ID":2889433,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.22277","arxiv_id":"2507.22277","title":"Parallel block coordinate descent methods with identification strategies","abstract":"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.","short_abstract":"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 u...","url_abs":"https://arxiv.org/abs/2507.22277","url_pdf":"https://arxiv.org/pdf/2507.22277v2","authors":"[\"Ronaldo Lopes\",\"Sandra A. Santos\",\"Paulo J. S. Silva\"]","published":"2025-07-29T23:02:26Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}