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However, computing the GSVD for large\u2010scale problems is challenging. Motivated by applications in hyper\u2010differential sensitivity analysis (HDSA), we propose new randomized algorithms for computing the GSVD which use randomized subspace iteration and weighted QR factorization. Detailed error analysis is given which provides insight into the accuracy of the algorithms and the choice of the algorithmic parameters. We demonstrate the performance of our algorithms on test matrices and a large\u2010scale model problem where HDSA is used to study subsurface flow.<\/jats:p>","DOI":"10.1002\/nla.2364","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T03:07:05Z","timestamp":1614222425000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis"],"prefix":"10.1002","volume":"28","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8698-6100","authenticated-orcid":false,"given":"Arvind K.","family":"Saibaba","sequence":"first","affiliation":[{"name":"Department of Mathematics North Carolina State University Raleigh North Carolina USA"}]},{"given":"Joseph","family":"Hart","sequence":"additional","affiliation":[{"name":"Optimization and Uncertainty Quantification Sandia National Laboratories Albuquerque New Mexico USA"}]},{"given":"Bart","family":"van Bloemen Waanders","sequence":"additional","affiliation":[{"name":"Optimization and Uncertainty Quantification Sandia National Laboratories Albuquerque New Mexico USA"}]}],"member":"311","published-online":{"date-parts":[[2021,2,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1137\/090771806"},{"key":"e_1_2_9_3_1","unstructured":"MartinssonPG TroppJ. 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