Dynamic programming principle and associated Hamilton-Jacobi-Bellman equation for stochastic recursive control problem with non-Lipschitz aggregator
ESAIM: Control, Optimisation and Calculus of Variations, Tome 24 (2018) no. 1, pp. 355-376.

In this work we study the stochastic recursive control problem, in which the aggregator (or generator) of the backward stochastic differential equation describing the running cost is continuous but not necessarily Lipschitz with respect to the first unknown variable and the control, and monotonic with respect to the first unknown variable. The dynamic programming principle and the connection between the value function and the viscosity solution of the associated Hamilton-Jacobi-Bellman equation are established in this setting by the generalized comparison theorem for backward stochastic differential equations and the stability of viscosity solutions. Finally we take the control problem of continuous-time Epstein−Zin utility with non-Lipschitz aggregator as an example to demonstrate the application of our study.

Reçu le :
Accepté le :
DOI : 10.1051/cocv/2017016
Classification : 93E20, 90C39, 35K10
Mots-clés : Stochastic recursive control problem, non-Lipschitz aggregator, dynamic programming principle, Hamilton-Jacobi-Bellman equation, continuous-time Epstein−Zin utility, viscosity solution
Pu, Jiangyan 1 ; Zhang, Qi 1

1
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     title = {Dynamic programming principle and associated {Hamilton-Jacobi-Bellman} equation for stochastic recursive control problem with {non-Lipschitz} aggregator},
     journal = {ESAIM: Control, Optimisation and Calculus of Variations},
     pages = {355--376},
     publisher = {EDP-Sciences},
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Pu, Jiangyan; Zhang, Qi. Dynamic programming principle and associated Hamilton-Jacobi-Bellman equation for stochastic recursive control problem with non-Lipschitz aggregator. ESAIM: Control, Optimisation and Calculus of Variations, Tome 24 (2018) no. 1, pp. 355-376. doi : 10.1051/cocv/2017016. http://www.numdam.org/articles/10.1051/cocv/2017016/

[1] A. Bensoussan, Maximum principle and dynamic programming approaches of the optimal control of partially observed diffusions. Stochastics 9 (1983) 169–222. | DOI | MR | Zbl

[2] J.M. Bismut, Conjugate convex functions in optimal stochastic control. J. Math. Anal. Appl. 44 (1973) 384–404. | DOI | MR | Zbl

[3] J.M. Bismut, Growth and optimal intertemporal allocation of risks. J. Econ. Theory 10 (1975) 239–257. | DOI | MR | Zbl

[4] Ph. Briand and R. Carmona, BSDEs with polynomial growth generators. J. Appl. Math. Stoch. Anal. 13 (2000) 207–238. | DOI | MR | Zbl

[5] Ph. Briand, B. Delyon, Y. Hu, E. Pardoux and L. Stoica, Lp solutions of backward stochastic differential equations. Stochastics Processes Appl. 108 (2003) 109–129. | DOI | MR | Zbl

[6] R. Buckdahn and J. Li, Stochastic differential games and viscosity solutions of Hamilton-Jacobi-Bellman-Isaacs equations. SIAM J. Control Optimiz. 47 (2008) 444–475. | DOI | MR | Zbl

[7] L. Chen and Z. Wu, Dynamic Programming principle for stochastic recursive optimal control problem with delayed systems. ESAIM: COCV 18 (2012) 1005–1026. | Numdam | MR | Zbl

[8] D. Duffie and L. Epstein, Stochastic differential utility. Econometrica 60 (1992) 353–394. | DOI | MR | Zbl

[9] N. El Karoui, S. Peng and M.C. Quenez, Backward stochastic differential equations in finance. Math. Finance 7 (1997) 1–71. | DOI | MR | Zbl

[10] S. Fan and L. Jiang, A generalized comparison theorem for BSDEs and its applications. J. Theor. Prob. 25 (2012) 50–61. | DOI | MR | Zbl

[11] W.H. Fleming and H.M. Soner, Controlled Markov Processes and Viscosity Solutions. Springer Verlag (2006). | MR | Zbl

[12] Y. Hu and S. Peng, Solution of forward-backward stochastic differential equations. Probab. Theory Relat. Fields 103 (1995) 273–283. | DOI | MR | Zbl

[13] H. Kraft, F.T. Seifried and M. Steffensen, Consumption-portfolio optimization with recursive utility in incomplete markets. Finance Stoch. 17 (2013) 161–196. | DOI | MR | Zbl

[14] M. Kobylanski, Backward stochastic differential equations and partial differential equations with quadratic growth. Ann. Prob. 28 (2000) 558–602. | DOI | MR | Zbl

[15] J.P. Lepeltier and J. San Martin Backward stochastic differential equations with continuous coefficient. Statist. Prob. Lett. 32 (1997) 425–430. | DOI | MR | Zbl

[16] J. Li and S. Peng, Stochastic optimization theory of backward stochastic differential equations with jumps and viscosity solutions of Hamilton-Jacobi-Bellman equations. Nonlin. Anal. Theory, Methods Appl. 70 (2009) 1776–1796. | DOI | MR | Zbl

[17] J. Ma, P. Protter and J. Yong, Solving forward-backward stochastic differential equations explicitly-a four step scheme. Probab. Theory Relat. Fields 98 (1994) 339–359. | DOI | MR | Zbl

[18] J. Ma and J. Yong, Forward-Backward Stochastic Differential Equations and Their Applications. Lect. Notes Math. Springer Verlag, New York 1702 (1999). | MR | Zbl

[19] E. Pardoux, BSDE’s, weak convergence and homogenization of semilinear PDE’s, in Nonlinear Analysis, Differential Equations and Control.Kluwer Academic Publishers, Dordrecht (1999) 503–549. | MR | Zbl

[20] E. Pardoux and S. Peng, Adapted solution of a backward stochastic differential equation. Syst. Control Lett. 14 (1990) 55–61. | DOI | MR | Zbl

[21] E. Pardoux and S. Peng, Forward-backward stochastic differential equations and quasilinear parabolic PDEs. Probab. Theory Relat. Fields 114 (1999) 123–150. | DOI | MR | Zbl

[22] S. Peng, A general stochastic maximum principle for optimal control problems. SIAM J. Control Optimiz. 28 (1990) 966–979. | DOI | MR | Zbl

[23] S. Peng, Probabilistic interpretation for systems of quasilinear parabolic partial differential equations. Stochastic Stochastic Reports 37 (1991) 61–74. | DOI | MR | Zbl

[24] S. Peng, A generalized dynamic programming principle and Hamilton-Jacobi-Bellman equation. Stochastic Stochastic Reports 38 (1992) 119–134. | DOI | MR | Zbl

[25] S. Peng, Backward stochastic differential equations-stochastic optimiztion theory and viscosity solutions of HJB equations, in Topics on Stochastic Analysis (in Chinese). Science in China Press, Beijing (1997) 85–138.

[26] S. Peng and Z. Wu, Fully coupled forward-backward stochastic differential equations and applications to optimal control. SIAM J. Control Optimiz. 37 (1999) 825–843. | DOI | MR | Zbl

[27] Z. Wu and Z. Yu, Dynamic programming principle for one kind of stochastic recursive optimal control problem and hamilton-Jacobi-Bellman equation. SIAM J. Control Optimiz. 47 (2008) 2616–2641. | DOI | MR | Zbl

[28] J. Yong, Optimality variational principle for controlled forward-backward stochastic differential equations with mixed initial-terminal conditions. SIAM J. Control Optimiz. 48 (2010) 4119–4156. | DOI | MR | Zbl

[29] J. Yong and X. Zhou, Stochastic Controls. Hamiltonian Systems and HJB Equations. Appl. Math. Springer Verlag, New York 43 (1999). | MR | Zbl

[30] Q. Zhang and H.Z. Zhao, Probabilistic representation of weak solutions of partial differential equations with polynomial growth coefficients. J. Theor. Probab. 25 (2012) 396–423. | DOI | MR | Zbl

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