Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
We consider the Langevin dynamics on Rd with an overdamped vector field and driven by multiplicative Brownian noise of small amplitude √ϵ, ϵ>0. Under suitable assumptions on the vector field and the diffusion coefficient, it is well-known that it has a unique invariant probability measure μ ϵ . We prove that as ε tends to zero, the probability measure ϵd/2μ ϵ(√ϵdx) converges in the p--Wasserstein distance for p∈[1,2] to a Gaussian measure with zero-mean vector and non-degenerate covariance matrix which solves a Lyapunov matrix equation. Moreover, the error term is estimated. We emphasize that generically no explicit formula for μϵ can be found.
Czasopismo
Rocznik
Tom
Strony
143--162
Opis fizyczny
Bibliogr. 35 poz.
Twórcy
autor
- Department of Mathematical and Statistical Sciences, University of Helsinki, PL 68, Pietari Kalmin katu 5, Postal Code: 00560. Helsinki, Finland
Bibliografia
- 1. A. Arapostathis, A. Biswas, and V. Borkar, Controlled equilibrium selection in stochastically perturbed dynamics, Ann. Probab. 46 (2018), 2749-2799.
- 2. K. Athreya and C. Hwang, Gibbs measures asymptotics, Sankhyã A 72 (2018), 191-207.
- 3. G. Barrera and M. Jara, Thermalisation for small random perturbations of dynamical systems, Ann. Appl. Probab. 30 (2020), 1164-1208.
- 4. P. Benner, J. Li, and T. Penzl, Numerical solution of large-scale Lyapunov equations, Riccati equations, and linear-quadratic optimal control problems, Numer. Linear Algebra Appl. 15 (2008), 755-777.
- 5. F. Bolley, I. Gentil, and A. Guillin, Convergence to equilibrium in Wasserstein distance for Fokker-Planck equations, J. Funct. Anal. 263 (2012), 2430-2457.
- 6. A. Biswas and V. Borkar, Small noise asymptotics for invariant densities for a class of diffusions: a control theoretic view, J. Math. Anal. Appl. 360 (2009), 476-484.
- 7. W. Coffey and Y. Kalmykov, The Langevin Equation: with Applications in Physics, Chemistry and Electrical Engineering, 3rd ed., World Sci., 2012.
- 8. A.Dalalyan, Theoretical guarantees for approximate sampling from smooth and log-concave densities, J. Roy. Statist. Soc. Ser. B. Statist. Methodol. 79 (2017), 651-676.
- 9. M. Day, Recent progress on the small parameter exit problem, Stochastics 20 (1987), 121-150.
- 10. M. Day and T. Darden, Some regularity results on the Ventcel-Freidlin quasi-potential function, Appl. Math. Optim. 13 (1985), 259-282.
- 11. A. Duncan, N. Nüsken, and G. Pavliotis, Using perturbed underdamped Langevin dynamics to efficiently sample from probability distributions, J. Statist. Phys. 169 (2017), 1098-1131.
- 12. A. Durmus and È Moulines, Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm, Statist. Comput. 25 (2015), 5-19.
- 13. A. Eberle, A. Guillin, and R. Zimmer, Couplings and quantitative contraction rates for Langevin dynamics, Ann. Probab. 47 (2019), 1982-2010.
- 14. A. Eberle, A. Guillin, and R. Zimmer, Quantitative Harris-type theorems for diffusions and McKean-Vlasov processes, Trans. Amer. Math. Soc. 371 (2019), 7135-7173.
- 15. M. Freidlin and A. Wentzell, Random Perturbations of Dynamical Systems, 3rd ed., Springer, 2012.
- 16. R. Gareth and J. Rosenthal, Hitting time and convergence rate bounds for symmetric Langevin diffusions, Methodol. Comput. Appl. Probab. 21 (2019), 921-929.
- 17. W. Huang, M. Ji, Z. Liu, and Y. Yi, Concentration and limit behaviors of stationary measures, Phys. D 369 (2018), 1-17.
- 18. C. Hwang, Laplace's method revisited: weak convergence of probability measures, Ann. Probab. 8 (1980), 1177-1182.
- 19. C. Hwang, S. Hwang-Ma, and S. Sheu, Accelerating Gaussian diffusions, Ann. Appl. Probab. 3 (1993), 897-913.
- 20. M. Ji, Z. Shen, and Y. Yi, Quantitative concentration of stationary measures, Phys. D 399 (2019), 73-85.
- 21. Y Kabanov, R. Liptser, and A. Shiryaev, On the variation distance for probability measures defined on a filtered space, Probab. Theory Related Fields 71 (1986), 19-35.
- 22. A. Kulik, Ergodic Behavior of Markov Processes with Applications to Limit Theorems, De Gruyter Stud. Math. 67, De Gruyter, 2018.
- 23. P. Lancaster and M. Tismenetsky, The Theory of Matrices, 2nd ed., Academic Press, 1985.
- 24. T. Lelièvre, F. Nier, and G. Pavliotis, Optimal non-reversible linear drift for the convergence to equilibrium of a diffusion, J. Statist. Phys. 152 (2013), 237-274.
- 25. N. Madras and D. Sezer, Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances, Bernoulli 16 (2010), 882-908.
- 26. X. Mao, Stochastic Differential Equations and Applications, 2nd ed., Horwood, 2008.
- 27. T. Mikami, Asymptotic expansions of the invariant density of a Markov process with a small parameter, Ann. Inst. H. Poincaré Probab. Statist. 24 (1988), 403-424.
- 28. T. Mikami, Asymptotic analysis of invariant density of randomly perturbed dynamical systems, Ann. Probab. 18 (1990), 524-536.
- 29. V. Panaretos and Y. Zemel, An Invitation to Statistics in Wasserstein Space, Springer, 2020.
- 30. G. Pavliotis, Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations, Springer, 2014.
- 31. Y. Pomeau and J. Piasecki, The Langevin equation, C. R. Phys. 18 (2017), 570-582.
- 32. A. Scottedward, B. Tenison, and K. Poolla, Numerical solution of the Lyapunov equation by approximate power iteration, Linear Algebra Appl. 236 (1996), 205-230.
- 33. S. Sheu, Asymptotic behavior of the invariant density of a diffusion Markov process with small diffusion, SIAM J. Math. Anal. 17 (1986), 451-460.
- 34. C. Villani, Optimal Transport: Old and New, Grundlehren Math. Wiss. 338, Springer, 2009.
- 35. S. Wu, C. Hwang, and M. Chu, Attaining the optimal Gaussian diffusion acceleration, J. Statist. Phys. 155 (2014), 571-590.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d4010cb6-a559-422c-a68d-f783da942a39