Titlebook: Metric Algebraic Geometry; Paul Breiding,Kathlén Kohn,Bernd Sturmfels Textbook‘‘‘‘‘‘‘‘ 2024 The Editor(s) (if applicable) and The Author(s

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書目名稱Metric Algebraic Geometry影響因子(影響力)




書目名稱Metric Algebraic Geometry影響因子(影響力)學(xué)科排名




書目名稱Metric Algebraic Geometry網(wǎng)絡(luò)公開度




書目名稱Metric Algebraic Geometry網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Metric Algebraic Geometry被引頻次




書目名稱Metric Algebraic Geometry被引頻次學(xué)科排名




書目名稱Metric Algebraic Geometry年度引用




書目名稱Metric Algebraic Geometry年度引用學(xué)科排名




書目名稱Metric Algebraic Geometry讀者反饋




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頂點(diǎn)

carbohydrate

Maximum Likelihood,ty distributions, and we used the Wasserstein metric to measure the distance from . to .. Finally, in Chapter 9, we considered the distance from a polynomial system . to the discriminant in the context of numerical analysis.

Expressly

DOLT

名字

吝嗇性

Polar Degrees,cognized this point for Euclidean distance optimization in Section 2.3, and we will see it again in Theorem 5.5 for polyhedral norms, with focus on theWasserstein metric from optimal transport theory. The punchline is that polar degrees govern linear programming over real algebraic varieties.

Substitution

Curvature, curvature. Our main point is to explore how curvature connects to algebraic geometry. We start out with plane curves, and we then turn to more general real algebraic varieties. The third section addresses the fundamental question of how to compute the volume of a tubular neighborhood of a variety.

Control-Group

entice