{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:13:01Z","timestamp":1719619981851},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research","award":["DE-AC02-06CH11357"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Math. Softw."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"\n In ACM TOMS Algorithm 1012, the\n DELAUNAYSPARSE<\/jats:monospace>\n software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set,\n DELAUNAYSPARSE<\/jats:monospace>\n calls the nonnegative least squares solver\n DWNNLS<\/jats:monospace>\n to compute projections onto the convex hull. However,\n DWNNLS<\/jats:monospace>\n and many other available sum-of-squares optimization solvers were not intended for usage with many variable problems, which result from the large training sets that are typical in machine learning applications. Thus, a new\n PROJECT<\/jats:monospace>\n subroutine is given, based on the highly customizable quadratic program solver\n BQPD<\/jats:monospace>\n . This solution is shown to be as robust as\n DELAUNAYSPARSE<\/jats:monospace>\n for projection onto both synthetic and real-world datasets, where other available solvers frequently fail. Although it is intended as an update for\n DELAUNAYSPARSE<\/jats:monospace>\n , due to the difficulty and prevalence of the problem, this solution is likely to be of external interest as well.\n <\/jats:p>","DOI":"10.1145\/3656581","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T14:46:02Z","timestamp":1713797162000},"page":"1-8","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Remark on Algorithm 1012: Computing Projections with Large Datasets"],"prefix":"10.1145","volume":"50","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9541-7041","authenticated-orcid":false,"given":"Tyler H.","family":"Chang","sequence":"first","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2009-107X","authenticated-orcid":false,"given":"Layne T.","family":"Watson","sequence":"additional","affiliation":[{"name":"Virginia Polytechnic Institute and State University, Blacksburg, VA, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8839-5876","authenticated-orcid":false,"given":"Sven","family":"Leyffer","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1858-4724","authenticated-orcid":false,"given":"Thomas C. H.","family":"Lux","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7738-5864","authenticated-orcid":false,"given":"Hussain M. J.","family":"Almohri","sequence":"additional","affiliation":[{"name":"Kuwait University, Kuwait City, Kuwait"}]}],"member":"320","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3422818"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3190645.3190680"},{"issue":"83","key":"e_1_3_1_4_2","first-page":"1","article-title":"CVXPY: A Python-embedded modeling language for convex optimization","volume":"17","author":"Diamond Steven","year":"2016","unstructured":"Steven Diamond and Stephen Boyd. 2016. CVXPY: A Python-embedded modeling language for convex optimization. Journal of Machine Learning Research 17, 83 (2016), 1\u20135. Retrieved from http:\/\/jmlr.org\/papers\/v17\/15-408.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2013.6669541"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/77635.77639"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02023102"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s101070050113"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/356004.356010"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11075-020-01040-2"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-016-0892-3"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12532-020-00179-2"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CISDA.2009.5356528"}],"container-title":["ACM Transactions on Mathematical Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3656581","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T18:10:52Z","timestamp":1719598252000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":12,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3656581"],"URL":"https:\/\/doi.org\/10.1145\/3656581","relation":{},"ISSN":["0098-3500","1557-7295"],"issn-type":[{"value":"0098-3500","type":"print"},{"value":"1557-7295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"2023-10-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-12","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}