Skip to main content
Log in

Stochastic optimal transport revisited

  • Original Paper
  • Published:
SN Partial Differential Equations and Applications Aims and scope Submit manuscript

Abstract

We prove the Duality Theorems for the stochastic optimal transportation problems with a convex cost function without a regularity assumption that is often supposed in the proof of the lower semicontinuity of an action integral. In our new approach, we prove that the stochastic optimal transportation problems with a convex cost function are equivalent to a class of variational problems for the Fokker–Planck equation, which lets us revisit them. It is done by the so-called superposition principle and by an idea from the Mather theory. The superposition principle is the construction of a semimartingale from the Fokker–Planck equation and can be considered a class of the so-called marginal problems that construct stochastic processes from given marginal distributions. It was first considered in stochastic mechanics by Nelson, called Nelson’s problem, and was proved by Carlen first. The semimartingale is called the Nelson process, provided it is Markovian. We also consider the Markov property of a minimizer of the stochastic optimal transportation problem with a nonconvex cost in a one-dimensional case. In the proof, the superposition principle and the minimizer of an optimal transportation problem with a concave cost function play crucial roles. Lastly, we prove the semiconcavity and the Lipschitz continuity of Schrödinger’s problem that is a typical example of the stochastic optimal transportation problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ambrosio, L.: Transport equation and Cauchy problem for BV vector fields. Invent. Math. 158, 227–260 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ambrosio, L., Trevisan, D.: Well-posedness of Lagrangian flows and continuity equations in metric measure spaces. Anal. PDE 7(5), 1179–1234 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  3. Aronson, D.G.: Bounds on the fundamental solution of a parabolic equation. Bull. Am. Math. Soc. 73, 890–896 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bogachev, V.I., Krylov, N.V., Röckner, M.: Elliptic and parabolic equations for measures. Russ. Math. Surv. 64(6), 973–1078 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bogachev, V. I., Röckner, M., Shaposhnikov, S. V.: On the Ambrosio–Figalli–Trevisan superposition principle for probability solutions to Fokker–Planck–Kolmogorov equations. J. Dyn. Differ. Equ. (2020)

  6. Cacoullos, T., Papathanasiou, V., Utev, S.A.: Another characterization of the normal law and a proof of the central limit theorem connected with it. Theory Probab. Appl. 37, 581–588 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cacoullos, T., Papathanasiou, V., Utev, S.A.: Variational inequalities with examples and an application to the central limit theorem. Ann. Probab. 22, 1607–1618 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  8. Carlen, E.A.: Conservative diffusions. Commun. Math. Phys. 94, 293–315 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  9. Carlen, E. A.: Existence and sample path properties of the diffusions in Nelson’s stochastic mechanics. In: Albeverio, S., Blanchard, Ph., Streit, L. (eds.) Stochastic processes-Mathematics and Physics, Bielefeld 1984, Lecture Notes in Math., Vol. 1158, pp. 25-51. Springer, Heidelberg (1986)

  10. Carmona, R.: Probabilistic construction of Nelson processes. In: Itô, K., Ikeda, N. (eds.) Proc. Probabilistic Methods in Mathematical Physics, Katata 1985, pp. 55–81. Kinokuniya, Tokyo (1987)

  11. Cattiaux, P., Léonard, C.: Minimization of the Kullback information of diffusion processes. Ann. Inst. H Poincaré Probab. Stat. 30, 83–132 (1994)

    MathSciNet  MATH  Google Scholar 

  12. Cattiaux, P., Léonard, C.: Correction to: Minimization of the Kullback information of diffusion processes [Ann. Inst. H. Poincaré Probab. Statist. 30 (1994), no. 1, 83–132]. Ann Inst H Poincaré Probab Statist 31, 705–707 (1995)

  13. Cattiaux, P., Léonard, C.: Large deviations and Nelson processes. Forum Math. 7, 95–115 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  14. Cattiaux, P., Léonard, C.: Minimization of the Kullback information for some Markov processes. In: Azema, J. et al. (eds.) Séminaire de Probabilités, XXX, Lecture Notes in Math., Vol. 1626, pp. 288–311. Springer, Heidelberg (1996)

  15. Crandall, M.G., Ishii, H., Lions, P.L.: User’s guide to viscosity solutions of second order partial differential equations. Bull. Am. Math. Soc. 27, 1–67 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  16. Dai Pra, P.: A stochastic control approach to reciprocal diffusion processes. Appl. Math. Optim. 23, 313–329 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  17. Dall’Aglio, G.: Sugli estremi dei momenti delle funzioni di ripartizione doppie. Ann. Scuola Normale Superiore Di Pisa, Cl. Sci. 3(1), 33–74 (1956)

    MATH  Google Scholar 

  18. Dupuis, P., Ellis, R.S.: A Weak Convergence Approach to the Theory of Large Deviations. John Wiley & Sons, New York (1997)

    Book  MATH  Google Scholar 

  19. Figalli, A.: Existence and uniqueness of martingale solutions for SDEs with rough or degenerate coefficients. J. Funct. Anal. 254, 109–153 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Fleming, W.H., Soner, H.M.: Controlled Markov Processes and Viscosity Solutions. Springer, New York (1993)

    MATH  Google Scholar 

  21. Föllmer, H.: Random fields and diffusion processes. In: Hennequin, PL (ed.) École d’Été de Probabilités de Saint-Flour XV–XVII, 1985–87, Lecture Notes in Math., Vol. 1362, pp. 101–203. Springer, Heidelberg (1988)

  22. Friedman, A.: Partial Differential Equations of Parabolic Type. Dover Publications, New York (2013)

    Google Scholar 

  23. Gomes, D.A.: A stochastic analogue of Aubry-Mather theory. Nonlinearity 15, 581–603 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  24. Gomes, D. A., Mitake, H, Tran, H. V.: The large time profile for Hamilton–Jacobi–Bellman equations. arXiv:2006.04785

  25. Ikeda, N., Watanabe, S.: Stochastic Differential Equations and Diffusion Processes. North-Holland/Kodansha, Tokyo (1981)

    MATH  Google Scholar 

  26. Ioffe, A.D., Tihomirov, V.M.: Theory of Extremal Problems. North-Holland, Amsterdam (1979)

    MATH  Google Scholar 

  27. Jamison, B.: Reciprocal processes. Z. Wahrsch. Verw. Gebiete 30, 65–86 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  28. Jamison, B.: The Markov process of Schrödinger. Z. Wahrsch. Verw. Gebiete 32, 323–331 (1975)

    Article  MATH  Google Scholar 

  29. Koike, S.: A beginner’s guide to the theory of viscosity solutions. MSJ Memoirs, Vol. 13. Math. Soc. Japan., Tokyo (2004)

  30. Léonard, C. : A survey of the Schrödinger problem and some of its connections with optimal transport. Special Issue on Optimal Transport and Applications. Discr. Contin. Dyn. Syst. 34, 1533–1574 (2014)

  31. Liptser, R.S., Shiryaev, A.N.: Statistics of Random Processes I. Springer, Heidelberg (1977)

    Book  Google Scholar 

  32. McCann, R.J.: A convexity principle for interacting gases. Adv. Math. 128, 153–179 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  33. Mikami, T.: Variational processes from the weak forward equation. Commun. Math. Phys. 135, 19–40 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  34. Mikami, T.: Equivalent conditions on the central limit theorem for a sequence of probability measures on \(\mathbb{R}\). Stat. Probab. Lett. 37, 237–242 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  35. Mikami, T.: Markov marginal problems and their applications to Markov optimal control. In: McEneaney, W. M. etal. (eds.) Stochastic Analysis, Control, Optimization and Applications, A Volume in Honor of W. H. Fleming, pp. 457-476. Birkhäuser, Boston (1999)

  36. Mikami, T.: Dynamical systems in the variational formulation of the Fokker–Planck equation by the Wasserstein metric. Appl. Math. Optim. 42, 203–227 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  37. Mikami, T.: Optimal control for absolutely continuous stochastic processes and the mass transportation problem. Elect. Commun. Probab. 7, 199–213 (2002)

    MathSciNet  MATH  Google Scholar 

  38. Mikami, T.: Monge’s problem with a quadratic cost by the zero-noise limit of \(h\)-path processes. Probab. Theory Related Fields 129, 245–260 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  39. Mikami, T.: Covariance kernel and the central limit theorem in the total variation distance. J. Multivar. Anal. 90, 257–268 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  40. Mikami, T.: Semimartingales from the Fokker–Planck equation. Appl. Math. Optim. 53, 209–219 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  41. Mikami, T.: Marginal problem for semimartingales via duality. In: Giga, Y., Ishii, K., Koike, S. et al. (eds) International Conference for the 25th Anniversary of Viscosity Solutions, Gakuto International Series. Mathematical Sciences and Applications 30, pp. 133–152. Gakkotosho, Tokyo (2008)

  42. Mikami, T.: Regularity of Schrödinger’s functional equation and mean field PDEs for h-path processes. Osaka J. Math. 56, 831–842 (2019)

    MathSciNet  MATH  Google Scholar 

  43. Mikami, T.: Regularity of Schrödinger’s functional equation in the weak topology and moment measures. J. Math. Soc. Jpn. 73, 99–123 (2021)

    Article  MATH  Google Scholar 

  44. Mikami, T.: Stochastic optimal transportation. A book in preparation

  45. Mikami, T., Thieullen, M.: Duality theorem for stochastic optimal control problem. Stoc. Proc. Appl. 116, 1815–1835 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  46. Nagasawa, M.: Transformations of diffusion and Schrödinger process. Probab. Theory Related Fields 82, 109–136 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  47. Nagasawa, M.: Stochastic Processes in Quantum Physics (Monographs in Mathematics 94). Birkhaüser, Basel (2000)

    Book  MATH  Google Scholar 

  48. Nelsen, R.B.: An Introduction to Copulas, 2nd edn. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  49. Nelson, E.: Dynamical Theories of Brownian Motion. Princeton University Press, Princeton (1967)

    Book  MATH  Google Scholar 

  50. Nelson, E.: Quantum Fluctuations. Princeton University Press, Princeton (1984)

    MATH  Google Scholar 

  51. Rachev, S. T., Rüschendorf, L.: Mass transportation problems, Vol. I: Theory, Vol. II: Application. Springer, Heidelberg (1998)

  52. Röckner, M., Xie, L., Zhang, X.: Superposition principle for non-local Fokker-Planck operators. Probab. Theory Related Fields 178, 699–733 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  53. Rüschendorf, L., Thomsen, W.: Note on the Schrödinger equation and \(I\)-projections. Statist. Probab. Lett. 17, 369–375 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  54. Santambrogio, F.: Dealing with moment measures via entropy and optimal transport. J. Funct. Anal. 271, 418–436 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  55. Schrödinger, E.: Ueber die Umkehrung der Naturgesetze. Sitz. Ber. der Preuss. Akad. Wissen., Berlin, Phys. Math. pp. 144–153 (1931)

  56. Schrödinger, E.: Théorie relativiste de l’electron et l’interprétation de la mécanique quantique. Ann. Inst. H. Poincaré 2, 269–310 (1932)

    MathSciNet  MATH  Google Scholar 

  57. Schweizer, B., Sklar, A.: Probabilistic Metric Space. Dover Publications, New York (2005)

    MATH  Google Scholar 

  58. Sheu, S.J.: Some estimates of the transition density of a nondegenerate diffusion Markov processes. Ann. Probab. 19, 538–561 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  59. Tan, X., Touzi, N.: Optimal transportation under controlled stochastic dynamics. Ann. Probab. 41, 3201–3240 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  60. Trevisan, D.: Well-posedness of multidimensional diffusion processes with weakly differentiable coefficients. Electron J. Probab. 21, 1–41 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  61. Villani, C.: Topics in Optimal Transportation. American Mathematics Society, Providence, RI (2003)

    Book  MATH  Google Scholar 

  62. Zambrini, J. C.: Variational processes. In: Albeverio, S. etal. (eds.) Stochastic processes in classical and quantum systems, Ascona 1985, Lecture Notes in Phys., Vol. 262., pp. 517–529. Springer, Heidelberg (1986)

  63. Zheng, W.A.: Tightness results for laws of diffusion processes application to stochastic mechanics. Ann. Inst. Henri Poincaré 21, 103–124 (1985)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Toshio Mikami.

Additional information

This article is part of the topical collection Viscosity solutions, Dedicated to Hitoshi Ishii on the award of the 1st Kodaira Kunihiko Prize edited by Kazuhiro Ishige, Shigeaki Koike, Tohru Ozawa, and Senjo Shimizu.

Partially supported by JSPS KAKENHI Grant Numbers JP26400136 and 19K03548.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mikami, T. Stochastic optimal transport revisited. SN Partial Differ. Equ. Appl. 2, 5 (2021). https://doi.org/10.1007/s42985-020-00059-3

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s42985-020-00059-3

Keywords

Mathematics Subject Classification

Navigation