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Gradient Flows : in Metric Spaces and in the Space of Probability Measures
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Introduction
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Chapter 2
Notation
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Chapter 3
Curves and Gradients in Metric Spaces
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Chapter 4
Existence of Curves of Maximal Slope and their Variational Approximation
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Chapter 5
Proofs of the Convergence Theorems
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Chapter 6
Uniqueness, Generation of Contraction Semigroups, Error Estimates
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Chapter 7
Preliminary Results on Measure Theory
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Chapter 8
The Optimal Transportation Problem
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Chapter 9
The Wasserstein Distance and its Behaviour along Geodesics
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Chapter 10
Absolutely Continuous Curves in P p ( X ) and the Continuity Equation
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Chapter 11
Convex Functionals in P p ( X )
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Chapter 12
Metric Slope and Subdifferential Calculus in P p ( X )
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Chapter 13
Gradient Flows and Curves of Maximal Slope in P p ( X )
Overall attention for this book and its chapters
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Mentioned by
twitter
4
X users
syllabi
1
institution with syllabi
wikipedia
20
Wikipedia pages
Citations
dimensions_citation
230
Dimensions
Readers on
mendeley
108
Mendeley
Book overview
1. Introduction
2. Notation
3. Curves and Gradients in Metric Spaces
4. Existence of Curves of Maximal Slope and their Variational Approximation
5. Proofs of the Convergence Theorems
6. Uniqueness, Generation of Contraction Semigroups, Error Estimates
7. Preliminary Results on Measure Theory
8. The Optimal Transportation Problem
9. The Wasserstein Distance and its Behaviour along Geodesics
10. Absolutely Continuous Curves in P p ( X ) and the Continuity Equation
11. Convex Functionals in P p ( X )
12. Metric Slope and Subdifferential Calculus in P p ( X )
13. Gradient Flows and Curves of Maximal Slope in P p ( X )
Summary
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Syllabi
Wikipedia
Dimensions citations
This data is correct as of December 2015 - for more up to date information, please visit
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So far, Altmetric has seen this research output assigned in
2
syllabi from
1
institutions on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
New York University
2
Unknown