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dc.contributor.authorDragomir, Dakota
dc.date.accessioned2023-10-19T01:18:48Z
dc.date.available2023-10-19T01:18:48Z
dc.date.issued2022-12
dc.identifier.urihttp://hdl.handle.net/11122/14732
dc.descriptionMaster's Project (M.S.) University of Alaska Fairbanks, 2022en_US
dc.description.abstractWhen dealing with sufficiently large integers, even the most cutting-edge existing algorithms for computing prime factorizations are impractically slow. In this paper, we explore the possibility of using neural networks to approximate prime factorizations in the hopes of providing an alternative factorization method which trades accuracy for speed. Due to the intrinsic difficulty associated with this task, the focus of this paper is largely concentrated on the obstacles encountered in the training of the neural net, rather than on the viability of the method itself.en_US
dc.language.isoen_USen_US
dc.subject.otherMaster of Science in Statisticsen_US
dc.titleComputing prime factorizations with neural networksen_US
dc.typeMaster's Projecten_US
dc.type.degreemsen_US
dc.identifier.departmentDepartment of Mathematics and Statisticsen_US
dc.contributor.chairGoddard, Scott
dc.contributor.committeeShort, Margaret
dc.contributor.committeeBarry, Ronald
refterms.dateFOA2023-10-19T01:18:49Z


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