Math 2.0

Calculus of variations and optimal control; optimization Optimal transport Theorem

Brenier's theorem

Builds on Optimal transport and the Monge problemCouplingPushforward measureConvex functionAbsolutely continuous measureGradient

Statement

Let μ\mu and ν\nu be Borel probability measures on ℝn\R^n with finite second moments, ∫∣x∣2 dμ<∞\int \abs{x}^2 \, d\mu < \infty and ∫∣y∣2 dν<∞\int \abs{y}^2 \, d\nu < \infty. Suppose that μ\mu is absolutely continuous with respect to Lebesgue measure. Then:

  1. There is a convex function φ ⁣:ℝn→ℝ∪{+∞}\varphi \colon \R^n \to \R \cup \set{+\infty}, differentiable at μ\mu-almost every point, such that the pushforward of μ\mu by its gradient is ν\nu: (∇φ)#μ=ν(\nabla \varphi)_\# \mu = \nu.
  2. The coupling π=(id⁡,∇φ)#μ\pi = (\id, \nabla \varphi)_\# \mu is the unique coupling of μ\mu and ν\nu that minimizes ∫∣x−y∣2 dπ(x,y)\int \abs{x - y}^2 \, d\pi(x, y).
  3. The map ∇φ\nabla \varphi is unique: if ψ\psi is another convex function with (∇ψ)#μ=ν(\nabla \psi)_\# \mu = \nu, then ∇ψ=∇φ\nabla \psi = \nabla \varphi μ\mu-almost everywhere.

Remarks

Yann Brenier proved the theorem in work on the polar factorization of vector fields, published in 1987 and 1991. The proof uses Kantorovich duality: since ∣x−y∣2=∣x∣2+∣y∣2−2⟨x,y⟩\abs{x - y}^2 = \abs{x}^2 + \abs{y}^2 - 2 \ip{x}{y}, an optimal plan maximizes ∫⟨x,y⟩ dπ\int \ip{x}{y} \, d\pi, and its support lies in the subdifferential of a convex function φ\varphi. A convex function is differentiable outside a Lebesgue null set. So absolute continuity of μ\mu makes the subdifferential single-valued μ\mu-almost everywhere, and the plan is carried by the graph of ∇φ\nabla \varphi. Example: if ν\nu is the image of μ\mu under x↦Ax+bx \mapsto A x + b with AA symmetric positive definite, then φ(x)=12⟨Ax,x⟩+⟨b,x⟩\varphi(x) = \frac12 \ip{Ax}{x} + \ip{b}{x} works, and the affine map is optimal. Absolute continuity cannot be dropped: if μ=δ0\mu = \delta_0 and ν\nu is not a Dirac mass, no map pushes μ\mu to ν\nu. When μ\mu and ν\nu have densities ff and gg, the potential φ\varphi solves the Monge–Ampère equation g(∇φ)det⁡D2φ=fg(\nabla \varphi) \det D^2 \varphi = f in a weak sense.

Going deeper

For a student

The theorem solves the Monge problem for the quadratic cost. It gives the polar factorization: a nondegenerate map uu pushing μ\mu to ν\nu factors as u=∇φ∘su = \nabla \varphi \circ s, where φ\varphi is convex and ss preserves μ\mu. It is the basis of the Wasserstein geometry of probability measures, of displacement convexity, and of regularity theory for the Monge–Ampère equation through Caffarelli's work.

Reading path

To understand this page from the ground up, read these 20 pages in order. The foundations come first.

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Each page uses the pages above it. The colour of the bar on a box shows how strong the evidence is, and the box also gives it as text. The list has the same pages.
  • no evidence level
  1. Absolutely continuous measure concept
  2. Borel sigma-algebra concept
  3. Convex function concept
  4. Coupling concept
  5. Euclidean space object
  6. Function concept
  7. Measure concept
  8. Monge–Ampère equation concept
  9. Probability measure concept
  10. Pushforward measure concept
  11. Topological space concept
  12. Wasserstein metric concept
  13. Total derivative concept
  14. Function of several variables concept
  15. Borel measure definition
  16. Differentiable function of several variables concept
  17. Partial derivative concept
  18. Optimal transport and the Monge problem problem
  19. Gradient concept
  20. Brenier's theorem statement

Where this leads

1 page builds on this one.

Further reading

  1. Web https://en.wikipedia.org/wiki/Transportation_theory_(mathematics)
About this page
TagCVQVBMHH4X
URIm2://CVQVBMHH4X
Kindstatement, theorem
WritingW0 stub
MSC49Q22, 35J96, 60A10
StatusThis copy of the site has no build data. Run m2 status CVQVBMHH4X.
AboutOptimal transport and the Monge problem
UsesOptimal transport and the Monge problem, Coupling, Pushforward measure, Convex function, Absolutely continuous measure, Gradient

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