{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:46:02Z","timestamp":1773391562591,"version":"3.50.1"},"reference-count":47,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","funder":[{"name":"Major Projects of Xiangjiang Laboratory","award":["22xj01011"],"award-info":[{"award-number":["22xj01011"]}]},{"name":"Key Program of National Natural Science Foundation of China","award":["U21A20461"],"award-info":[{"award-number":["U21A20461"]}]},{"name":"Key Program of National Natural Science Foundation of China","award":["92055213"],"award-info":[{"award-number":["92055213"]}]},{"name":"Key Program of National Natural Science Foundation of China","award":["62227808"],"award-info":[{"award-number":["62227808"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2026,5,30]]},"abstract":"<jats:p>The combination of iterative solvers and preconditioners is the mainstream approach for solving sparse linear systems of the form [Formula: see text], which are fundamental to many scientific and engineering applications. However, the automatic selection (auto-selection) of the optimal solver\u2013preconditioner combination remains a challenging task. Although machine learning or deep learning methods have been explored for this purpose, their performance has been limited due to the lack of standardized, large-scale datasets. To address this issue, we introduce a large-scale benchmark dataset called SolverSet, designed for the auto-selection of the optimal combination of iterative solvers and preconditioners. We develop a matrix generation tool to produce a wide variety of large-scale sparse matrices, based on which we construct the SolverSet dataset. This dataset now comprises 12,651 large-scale matrices and their corresponding sparse linear systems, effectively overcoming the limitations of prior work\u00a0\u2014 namely, limited size and a small number of systems\u00a0\u2014 making it highly suitable for automatic selection modeling. We evaluate several baseline methods on SolverSet to validate its effectiveness and usability. Furthermore, we analyze key features of sparse linear systems and propose a deep learning based model named DL-Solver to predict the optimal solver\u2013preconditioner combination for a given system. Experimental results demonstrate that SolverSet serves as a valuable benchmark for auto-selection research, and DL-Solver outperforms state-of-the-art method in predictive performance on the test set, achieving improvements of 2.14%, 3.47%, 2.71% and 3.16% in accuracy, macro-precision, macro-recall and macro-F1 score, respectively.<\/jats:p>","DOI":"10.1142\/s0218126626500209","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T06:16:23Z","timestamp":1760681783000},"source":"Crossref","is-referenced-by-count":0,"title":["SolverSet: A Large-scale Benchmark Dataset for the Auto-selection of the Optimal Combination of Iterative Solvers and Preconditioners"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5972-4199","authenticated-orcid":false,"given":"Hantao","family":"Xiong","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2681-7898","authenticated-orcid":false,"given":"Wangdong","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3329-0924","authenticated-orcid":false,"given":"Shengle","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5219-6289","authenticated-orcid":false,"given":"Weiqing","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York, New Paltz, New York 12561, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-7716","authenticated-orcid":false,"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"S0218126626500209BIB001","volume-title":"Numerical Methods for Partial Differential Equations","author":"Ames W. 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