Metric Algebraic Geometry

Zusammenfassung: Metric algebraic geometry combines concepts from algebraic geometry and differential geometry. Building on classical foundations, it offers practical tools for the 21st century. Many applied problems center around metric questions, such as optimization with respect to distances. After a short dive into 19th-century geometry of plane curves, we turn to problems expressed by polynomial equations over the real numbers. The solution sets are real algebraic varieties. Many of our metric problems arise in data science, optimization and statistics. These include minimizing Wasserstein distances in machine learning, maximum likelihood estimation, computing curvature, or minimizing the Euclidean distance to a variety. This book addresses a wide audience of researchers and students and can be used for a one-semester course at the graduate level. The key prerequisite is a solid foundation in undergraduate mathematics, especially in algebra and geometry. This is an open access book

Location
Deutsche Nationalbibliothek Frankfurt am Main
ISBN
9783031514623
Extent
Online-Ressource, XIV, 215 p.
Edition
1st ed. 2024
Language
Englisch
Notes
online resource.

Bibliographic citation
Oberwolfach Seminars ; 53

Event
Veröffentlichung
(where)
Cham
(who)
Springer Nature Switzerland, Imprint: Birkhäuser
(when)
2024
Creator
Contributor
SpringerLink (Online service)

DOI
10.1007/978-3-031-51462-3
URN
urn:nbn:de:101:1-2024022803142028648915
Table of contents
Preface -- Historical Snapshot -- Critical Equations -- Computations -- Polar Degrees -- Wasserstein Distance -- Curvature -- Reach and Offset -- Voronoi Cells -- Condition Numbers -- Machine Learning -- Maximum Likelihood -- Tensors -- Computer Vision -- Volumes of Semialgebraic Sets -- Sampling -- References
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
25.03.2025, 1:55 PM CET

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Time of origin

  • 2024

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