{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T02:16:39Z","timestamp":1780107399864,"version":"3.54.0"},"reference-count":128,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Operations Research"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>On Sinkhorn\u2019s Algorithm and Choice Modeling<\/jats:p>\n                  <jats:p>Choice modeling is an important topic that underlies a wide range of applications involving human decision making, and it traces its roots to the 1920s. Matrix balancing has an equally long history and wide applicability (e.g., in transportation and mobility networks). Recently, its celebrated Sinkhorn\u2019s algorithm has been instrumental in the efficient approximation of optimal transport distances. However, the two topics have largely developed independently. In \u201cOn Sinkhorn\u2019s Algorithm and Choice Modeling,\u201d Qu, Galichon, Gao, and Ugander establish extensive connections between a class of Luce choice models and a common matrix-balancing problem. They leverage these connections to resolve open problems on the convergence of Sinkhorn\u2019s algorithm for nonnegative matrices, characterizing its global linear convergence rate in terms of the algebraic connectivity and deriving the sharp asymptotic rate. The connections established in this paper between two seemingly unrelated topics help the transmission of ideas and lead to further interesting results.<\/jats:p>","DOI":"10.1287\/opre.2023.0596","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:47:30Z","timestamp":1753883250000},"page":"1456-1475","source":"Crossref","is-referenced-by-count":0,"title":["On Sinkhorn\u2019s Algorithm and Choice Modeling"],"prefix":"10.1287","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1484-1217","authenticated-orcid":false,"given":"Zhaonan","family":"Qu","sequence":"first","affiliation":[{"name":"Data Science Institute, Columbia University, New York, New York 10027"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfred","family":"Galichon","sequence":"additional","affiliation":[{"name":"Department of Mathematics, New York University, New York, New York 10012; and Department of Economics, New York University, New York, New York 10012; and Department of Economics, Sciences Po, 75337 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzhi","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5655-4086","authenticated-orcid":false,"given":"Johan","family":"Ugander","sequence":"additional","affiliation":[{"name":"Department of Management Science and Engineering, Stanford University, Stanford, California 94305"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"issue":"6","key":"B1","first-page":"1411","volume":"67","author":"Abowd JM","year":"1999","journal-title":"Econometrica"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(93)90131-7"},{"key":"B3","unstructured":"Agarwal A, Negahban S, Wainwright MJ (2010) Fast global convergence rates of gradient methods for high-dimensional statistical recovery.\n                      Adv. 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