Optimal Transport: Theory and Applications

Related lectures (53)

Explores proof concepts, techniques, and applications in logic, mathematics, and algorithms.

Explores unconstrained optimization theory, covering global and local minima, convexity, and gradient concepts.

Covers the generalization of the TAF theorem, lateral derivatives, limits of derivatives, and indeterminacy.

Delves into the concept of proof in mathematics, emphasizing the importance of evidence and logical reasoning.

Introduces the key concepts of convexity and its applications in different fields.

Explores the theory and application of quadrature formulas for numerical analysis.

Explores the lower bound on total variation distance in Markov chains and its implications on mixing time.

Explores fundamental groups, homotopy classes, and coverings in connected manifolds.

Explores modular arithmetic, emphasizing inverses and equations in Z/mZ, with practical examples and exercises.

Introduces Coq, covering defining propositions, proving theorems, and using tactics.

Covers the basic theory for continuous time Markov chains and discusses communication, hitting probabilities, recurrence, and transience.

Explores geodesic convexity in metric spaces and its applications, discussing properties and the stability of inequalities.

Covers cyclically monotone sets in optimal transport theory and their properties.

Covers the approximation of functions in Sobolev spaces using smooth functions.

Covers the Theoreus Chain Role for Lipschitz functions and its practical applications.

Explores sub/super harmonic functions and their applications in a theoretical context.

Explores Lagrange multipliers, minimax theorems, and convex subsets in optimal transport theory.

Covers the Meyers-Serrin theorem in analysis, discussing the conditions for functions in different spaces.

Explores convex sets, their properties, and applications in optimization.

Explores the theory of implicit functions, their properties, and illustrative examples.

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