The Best Writing on Mathematics 2021

The year's finest mathematical writing from around the worldThis annual anthology brings together the year's finest mathematics writing from around the world - and you don't need to be a mathematician to enjoy the pieces collected here. These essays - from leading names and fresh new...

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Bibliografiske detaljer
Hovedforfatter: Pitici, Mircea, 1965-
Format: Livre numérique
Sprog:Anglais
Udgivet: Princeton, NJ : Princeton University Press 2022.
Paris : Cyberlibris
Online adgang:Accès Université d'Orléans et IFPM
Accès INSA CVL
Kommentar: Couverture. https://static2.cyberlibris.com/books_upload/136pix/9780691225722.jpg
Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur
Cyberlibris (ScholarVox) corpus Sciences de l'ingénieur
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• The Best Writing on Mathematics 2021, Mircea Pitici, Princeton, NJ, Princeton University Press, 2022, 1 vol. (320 p.), 978-06-9122-570-8
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520 |a The year's finest mathematical writing from around the worldThis annual anthology brings together the year's finest mathematics writing from around the world - and you don't need to be a mathematician to enjoy the pieces collected here. These essays - from leading names and fresh new voices - delve into the history, philosophy, teaching, and everyday aspects of math, offering surprising insights into its nature, meaning, and practice, and taking readers behind the scenes of today's hottest mathematical debates.Here, Viktor Blåsjö gives a brief history of "lockdown mathematics"; Yelda Nasifoglu decodes the politics of a seventeenth-century play in which the characters are geometric shapes; and Andrew Lewis-Pye explains the basic algorithmic rules and computational procedures behind cryptocurrencies. In other essays, Terence Tao candidly recalls the adventures and misadventures of growing up to become a leading mathematician; Natalie Wolchover shows how old math gives new clues about whether time really flows; and David Hand discusses the problem of "dark data" - information that is missing or ignored. And there is much, much more 
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