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The dynamic system method and the traps

Published online by Cambridge University Press:  01 July 2016

Odile Brandière*
Affiliation:
Université de Marne-la-Vallée
*
Postal address: Université de Marne-la-Vallée, Equipe d'Analyse et de Mathématiques Appliquées, 2, rue de la Butte Verte, 93166 Noisy-le-Grand, France.

Abstract

We transpose the ordinary differential equation method (used for decreasing stepsize stochastic algorithms) to a dynamical system method to study dynamical systems disturbed by a noise decreasing to zero. We prove that such an algorithm does not fall into a regular trap if the noise is exciting in an unstable direction.

MSC classification

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 1998 

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References

[1] Benaïm, M., (1996). A dynamical system approach to stochastic approximations. SIAM J. Control Optim. 34, 437472.CrossRefGoogle Scholar
[2] Benaïm, M. and Hirsch, M.W. (1995). Chain recurrence in surface flows. Discrete Cont. Dynam. Syst. 1, 117.Google Scholar
[3] Benveniste, A., Métivier, M. and Priouret, P. (1990). Adaptive Algorithms and Stochastic Approximation. Springer, Berlin.CrossRefGoogle Scholar
[4] Biscarat, J.C. (1994). Almost sure convergence of a class of stochastic algorithms. Stoch. Proc. Appl. 50, 8399.Google Scholar
[5] Brandière, O., (1996). Autour des pièges des algorithmes stochastiques. Thesis. Université de Marne-la-Vallée, France.Google Scholar
[6] Brandière, O. and Duflo, M. (1996). Les algorithmes stochastiques contournent-ils les pièges? Ann. Inst. Henri Poincaré 32, 395427.Google Scholar
[7] Celeux, G. and Diebolt, J. (1993). A stochastic approximation type EM algorithm for mixture problem. Stoch. Stoch. Rep. 41, 119134.CrossRefGoogle Scholar
[8] Chen, H.-F., Guo, L. and Gao, A.J. (1988). Convergence and robustness of the Robbins–Monro algorithm truncated at randomly varying bounds. Stoch. Proc. Appl. 27, 217231.CrossRefGoogle Scholar
[9] Delyon, B. (1996). General convergence results on stochastic approximation IEEE Trans. Automatic Control 41, 9.CrossRefGoogle Scholar
[10] Duflo, M. (1996). Algorithmes stochastiques. In Collection Math. et Appl. Vol. 23. Springer, Berlin.Google Scholar
[11] Fort, J.C. and Pagès, G. (1996). Convergence of stochastic algorithms: from Kushner–Clark theorem to the Lyapounov functional method. Adv. Appl. Prob. 28, 10721094.CrossRefGoogle Scholar
[12] Hale, J.K. (1988). Asymptotic behavior of dissipative systems. In Mathematical Surveys and Monographs. Vol. 25. American Mathematical Society, Providence, RI.Google Scholar
[13] Haraux, A. (1991). Systèmes dynamiques dissipatifs et applications. Masson, Paris.Google Scholar
[14] Hartman, P. (1982). Ordinary Differential Equations. 2nd edn, Wiley, New York.Google Scholar
[15] Hirsch, M.W. (1993). Asymptotic phase, shadowing and reaction diffusion, control theory, dynamical systems and geometry of dynamics. ed Elworthy, K.D. and Everitts, W.N.. Dekker, New York. pp. 8799.Google Scholar
[16] Kesten, H. (1972). Limit theorems for stochastic growth models I and II. Adv. Appl. Prob. 4, 193232, 393–428.CrossRefGoogle Scholar
[17] Kushner, H.J. and Clark, D.S. (1978). Stochastic approximation for constrained and unconstrained systems. Applied Math. Science Series. Vol. 26, Springer, Berlin.Google Scholar
[18] Lai, T.Z. and Wei, C.Z. (1983). A note on martingale difference sequences satisfying the local Marcinkie-wicz–Zygmund condition. Bull. Inst. Math. Acad. Sinica 11, 113.Google Scholar
[19] Lazarev, V.A. (1992). Convergence of stochastic approximation procedures in case of regression equation with several roots. Prob. Peredachi Inform. 28, 7588.Google Scholar
[20] Ljung, L. (1977). Analysis of recursive stochastic algorithms. IEEE Trans. Automatic Control 22, 551575.Google Scholar
[21] Pierre Loti Viaud, D. (1995). Random perturbations of recursive sequences with an application to an epidemic model. J. Appl. Prob. 32, 559578.Google Scholar