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Breiding P. Metric Algebraic Geometry 2024

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Breiding P. Metric Algebraic Geometry 2024

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Category: Other
Total size: 7.75 MB
Added: 2025-03-10 23:38:51

Share ratio: 2 seeders, 2 leechers
Info Hash: DDE592472D8DE3197F514B1A04A6FA2D171033F2
Last updated: 4.9 hours ago

Description:

Textbook in PDF format 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. Preface. About the Authors. Acknowledgements. Historical Snapshot. Critical Equations. Computations. Polar Degrees. Wasserstein Distance. Curvature. Reach and Off set. Voronoi Cells. Condition Numbers. Machine Learning. Maximum Likelihood. Tensors. Computer Vision. Volumes of Semialgebraic Sets. Sampling. References