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==Applications of Optimal Transport== | ==Applications of Optimal Transport== | ||
* Machine Learning [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6024256/ Kolouri, et al, ''Optimal Mass Transport: Signal processing and machine-learning applications''] | * Machine Learning [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6024256/ Kolouri, et al, ''Optimal Mass Transport: Signal processing and machine-learning applications''] | ||
==Mathematical Foundations: Optimization== | ==Mathematical Foundations: Optimization== |
Revision as of 21:33, 25 May 2020
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The Optimal Transport Problem
- Optimal Transport in one dimension; Villani (73-78); Santambrogio (59-67)
- 1-Wasserstein metric, duality, and measures with unequal mass; Piccoli, Rossi, and Tournus A Wasserstein norm for signed measures, with application to nonlocal transport equation with source term
The 2-Wasserstein Metric
- Geodesics and generalized geodesics; Santambrogio (202-207), Ambrosio, Gilgi, Savaré (158-160)
- Displacement convexity; Santambrogio (249-251,271-276); Villani (150-154)
- Asymptotic equivalence of and ; Santambrogio (209-211); Villani (233-235)
- Formal Riemannian Structure of the Wasserstein metric; Villani (245-247, 250-251); Ambrosio, Gigli, Savaré (189-191)
Numerical Methods for Optimal Transport
- Semidiscrete Optimal Transport (for ); Santambrogio (242-248); Peyré Cuturi (85-89)
- Computing OT via Benamou-Brenier; Santambrogio (220-225); Peyré, Cuturi (102-108)
- Sliced Wasserstein Distance; Santambrogio (214-215); Peyré, Cuturi (166-169)
- Wasserstein Barycenters; Santambrogio (215-218); Peyré, Cuturi (138-144)
Applications of Optimal Transport
- Machine Learning Kolouri, et al, Optimal Mass Transport: Signal processing and machine-learning applications
Mathematical Foundations: Optimization
- Fenchel-Rockafellar and Linear Programming; Brezis (15-17); Rockafellar, Variational Analysis (505-507)
Mathematical Foundations: Differential Equations
- Gradient flows in metric spaces; Santambrogio (285-290) more refs