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Matrix Anal. Appl."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Abstract.<\/jats:p>\n                  <jats:p>Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known to be effective in handling high-dimensional computational problems, providing high-quality empirical performance as well as strong probabilistic guarantees. However, their practical application is complicated by the fact that the user needs to set various algorithm-specific tuning parameters which are different from those used in traditional NLA. This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In particular, we provide a detailed investigation of surrogate-based autotuning for sketch-and-precondition (SAP)\u2013based randomized least squares methods, which have been one of the great success stories in modern RandNLA. Empirical results show that our surrogate-based autotuning approach can achieve near-optimal performance with much less tuning cost than a random search (up to about 7.6x fewer trials of different parameter configurations). Moreover, while our experiments focus on least squares, our results demonstrate a general-purpose autotuning pipeline applicable to any kind of RandNLA algorithm.<\/jats:p>\n                  <jats:p>Reproducibility of computational results. This paper has been awarded the \u201cSIAM Reproducibility Badge: code and data available\u201d, as a recognition that the authors have followed reproducibility principles valued by SISC and the scientific computing community. Code and data that allow readers to reproduce the results in this paper are available at https:\/\/gptune.lbl.gov\/ . 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University of California, Berkeley, CA 94720 USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micha\u0142","family":"Derezi\u0144ski","sequence":"additional","affiliation":[{"name":"The University of Michigan, Ann Arbor, MI 48109 USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyun","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Berkeley, CA 94720 USA. Current address: Georgia Institute of Technology, Atlanta, GA 30332 USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengrui","family":"Luo","sequence":"additional","affiliation":[{"name":"Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA. Current address: Rice University, Houston, TX 77005 USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Mahoney","sequence":"additional","affiliation":[{"name":"University of California at Berkeley, Lawrence Berkeley National Laboratory, and International Computer Science Institute."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1461-6458","authenticated-orcid":true,"given":"Riley","family":"Murray","sequence":"additional","affiliation":[{"name":"University of California, Berkeley, CA 94720 USA. Current address: Sandia National Laboratories, Livermore, CA 94550 USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-0000(03)00025-4"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"N. Ailon and B. 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