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Hypocoercivity in metastable settings and kinetic simulated annealing

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Abstract

Combining classical arguments for the analysis of the simulated annealing algorithm with the more recent hypocoercive method of distorted entropy, we prove the convergence for large time of the kinetic Langevin annealing with logarithmic cooling schedule.

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Notes

  1. Allowing large steps is not a reasonable solution, since in applied problems the dimension is large and the reasonable configurations (i.e. the points where U is not too large) lie in a very small area in view of the Lebesgue measure. A uniformly-generated jump proposal will always be absurd, and rejected.

References

  1. Ané, C., Blachère, S., Chafaï, D., Fougères, P., Gentil, I., Malrieu, F., Roberto, C., Scheffer, G.: Sur les inégalités de Sobolev logarithmiques. Panoramas et synthèses. Société mathématique de France, Paris. http://opac.inria.fr/record=b1097031 (2000)

  2. Arnold, A., Erb, J.: Sharp entropy decay for hypocoercive and non-symmetric Fokker–Planck equations with linear drift. ArXiv e-prints (2014)

  3. Bakry, D., Gentil, I., Ledoux, M.: Analysis and geometry of Markov diffusion operators. In: Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], vol. 348. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-00227-9

    Book  Google Scholar 

  4. Baudoin, F.: Bakry–Emery meet Villani. ArXiv e-prints (2013)

  5. Bolley, F., Gentil, I.: Phi-entropy inequalities for diffusion semigroups. J. Math. Pures Appl. (9) 93(5), 449–473 (2010). https://doi.org/10.1016/j.matpur.2010.02.004

    Article  MathSciNet  MATH  Google Scholar 

  6. Catoni, O.: Rough large deviation estimates for simulated annealing: application to exponential schedules. Ann. Probab. 20(3), 1109–1146 (1992). http://links.jstor.org/sici?sici=0091-1798(199207)20:3<1109:RLDEFS>2.0.CO;2-Z&origin=MSN

    Article  MathSciNet  Google Scholar 

  7. Chiang, T.S., Hwang, C.R., Sheu, S.: Diffusion for global optimization in \({\mathbb{R}}^n\). SIAM J. Control Optim. 25(3), 737–753 (1987). https://doi.org/10.1137/0325042

    Article  MathSciNet  MATH  Google Scholar 

  8. Dolbeault, J., Mouhot, C., Schmeiser, C.: Hypocoercivity for kinetic equations with linear relaxation terms. C. R. Math. Acad. Sci. Paris 347(9–10), 511–516 (2009). https://doi.org/10.1016/j.crma.2009.02.025

    Article  MathSciNet  MATH  Google Scholar 

  9. Fort, G., Jourdain, B., Kuhn, E., Lelièvre, T., Stoltz, G.: Efficiency of the Wang–Landau algorithm: a simple test case. AMRX 2, 275–311 (2014)

    MathSciNet  MATH  Google Scholar 

  10. Gadat, S., Miclo, L.: Spectral decompositions and \(\mathbb{L}^2\)-operator norms of toy hypocoercive semi-groups. Kinet. Relat. Models 6(2), 317–372 (2013)

    Article  MathSciNet  Google Scholar 

  11. Gadat, S., Panloup, F.: Long time behaviour and stationary regime of memory gradient diffusions. Ann. Inst. Henri Poincaré Probab. Stat. 50(2), 564–601 (2014). https://doi.org/10.1214/12-AIHP536

    Article  MathSciNet  MATH  Google Scholar 

  12. Guillin, A., Monmarché, P.: Optimal linear drift for an hypoelliptic diffusion. Electron. Commun. Probab. 21, 1–14 (2016)

    Article  MathSciNet  Google Scholar 

  13. Holley, R.A., Kusuoka, S., Stroock, D.W.: Asymptotics of the spectral gap with applications to the theory of simulated annealing. J. Funct. Anal. 83(2), 333–347 (1989). https://doi.org/10.1016/0022-1236(89)90023-2

    Article  MathSciNet  MATH  Google Scholar 

  14. Holley, R.A., Stroock, D.W.: Simulated annealing via Sobolev inequalities. Commun. Math. Phys. 115(4), 553–569 (1988). http://projecteuclid.org/euclid.cmp/1104161084

    Article  MathSciNet  Google Scholar 

  15. Kuwada, K.: Duality on gradient estimates and Wasserstein controls. J. Funct. Anal. 258(11), 3758–3774 (2010). https://doi.org/10.1016/j.jfa.2010.01.010

    Article  MathSciNet  MATH  Google Scholar 

  16. Lelièvre, T., Rousset, M., Stoltz, G.: Long-time convergence of an adaptive biasing force method. Nonlinearity 21(6), 1155–1181 (2008). https://doi.org/10.1088/0951-7715/21/6/001

    Article  MathSciNet  MATH  Google Scholar 

  17. Menz, G., Schlichting, A.: Poincaré and logarithmic Sobolev inequalities by decomposition of the energy landscape. Ann. Probab. 42(5), 1809–1884 (2014). https://doi.org/10.1214/14-AOP908

    Article  MathSciNet  MATH  Google Scholar 

  18. Miclo, L.: Recuit simulé sur \(R^n\). Étude de l’évolution de l’énergie libre. Ann. Inst. H. Poincaré Probab. Statist. 28(2), 235–266 (1992). http://www.numdam.org/item?id=AIHPB_1992__28_2_235_0

  19. Monmarché, P.: Generalized \(\Gamma \) calculus and application to interacting particles on a graph. ArXiv e-prints (2015)

  20. Monmarché, P.: Piecewise deterministic simulated annealing. ALEA Lat. Am. J. Probab. Math. Stat. 13(1), 357–398 (2016)

    MathSciNet  MATH  Google Scholar 

  21. Robbe, V.: Small eigenvalues of the low temperature linear relaxation boltzmann equation with a confining potential. Annales Henri Poincaré 17(4), 937–952 (2016). https://doi.org/10.1007/s00023-015-0410-4

    Article  MathSciNet  MATH  Google Scholar 

  22. Scemama, A., Lelièvre, T., Stoltz, G., Caffarel, M.: An efficient sampling algorithm for variational Monte Carlo. J. Chem. Phys. 125, 114105 (2006)

    Article  Google Scholar 

  23. Sun, Y., Garcia, A.: Interactive diffusions for global optimization. J. Optim. Theory Appl. 163(2), 491–509 (2014). https://doi.org/10.1007/s10957-013-0394-5

    Article  MathSciNet  MATH  Google Scholar 

  24. Talay, D.: Stochastic Hamiltonian systems: exponential convergence to the invariant measure, and discretization by the implicit Euler scheme. Markov Process. Relat. Fields 8(2), 163–198 (2002)

    MathSciNet  MATH  Google Scholar 

  25. Taniguchi, S.: Applications of Malliavin’s calculus to time-dependent systems of heat equations. Osaka J. Math. 22(2), 307–320 (1985). http://projecteuclid.org/euclid.ojm/1200778261

  26. Villani, C.: Hypocoercivity, vol. 202, no. 950. Memoirs of the American Mathematical Society, Providence (2009). https://doi.org/10.1090/S0065-9266-09-00567-5

    Book  MATH  Google Scholar 

  27. Wu, L.: Large and moderate deviations and exponential convergence for stochastic damping Hamiltonian systems. Stoch. Process. Appl. 91(2), 205–238 (2001). https://doi.org/10.1016/S0304-4149(00)00061-2

    Article  MathSciNet  MATH  Google Scholar 

  28. Zitt, P.A.: Annealing diffusions in a potential function with a slow growth. Stoch. Process. Appl. 118(1), 76–119 (2008). https://doi.org/10.1016/j.spa.2007.04.002

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The author would like to thank Laurent Miclo, who initiated this work, for numerous fruitful discussions. This work has been supported by EFI Project ANR-17-CE40-0030 of the French National Research Agency (ANR).

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Correspondence to Pierre Monmarché.

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This work has been supported by ANR STAB.

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Monmarché, P. Hypocoercivity in metastable settings and kinetic simulated annealing. Probab. Theory Relat. Fields 172, 1215–1248 (2018). https://doi.org/10.1007/s00440-018-0828-y

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