January  2016, 1: 2 doi: 10.1186/s41546-016-0002-3

Mean-field stochastic linear quadratic optimal control problems: closed-loop solvability

1 Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China;

2 Department of Mathematics, University of Central Florida, Orlando, FL 32816, USA

Received  February 24, 2016 Revised  June 10, 2016 Published  August 2016

Fund Project: supported by Hong Kong RGC under grants 519913, 15209614 and 15224215. Jingrui Sun was partially supported by the National Natural Science Foundation of China (11401556) and the Fundamental Research Funds for the Central Universities (WK 2040000012). Jiongmin Yong was partially supported by NSF DMS-1406776.

An optimal control problem is studied for a linear mean-field stochastic differential equation with a quadratic cost functional. The coefficients and the weighting matrices in the cost functional are all assumed to be deterministic. Closedloop strategies are introduced, which require to be independent of initial states; and such a nature makes it very useful and convenient in applications. In this paper, the existence of an optimal closed-loop strategy for the system (also called the closedloop solvability of the problem) is characterized by the existence of a regular solution to the coupled two (generalized) Riccati equations, together with some constraints on the adapted solution to a linear backward stochastic differential equation and a linear terminal value problem of an ordinary differential equation.
Citation: Xun Li, Jingrui Sun, Jiongmin Yong. Mean-field stochastic linear quadratic optimal control problems: closed-loop solvability. Probability, Uncertainty and Quantitative Risk, 2016, 1 (0) : 2-. doi: 10.1186/s41546-016-0002-3
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Tang, S:General linear quadratic optimal stochastic control problems with random coefficients:linear stochastic Hamilton systems and backward stochastic Riccati equations. SIAM J. Control Optim 42, 53-75 (2003) Google Scholar

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Wonham, WM:On a matrix Riccati equation of stochastic control. SIAM J. Control Optim 6, 681-697(1968) Google Scholar

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Yong, J:Linear-quadratic optimal control problems for mean-field stochastic differential equations. SIAM J. Control Optim 51, 2809-2838 (2013) Google Scholar

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Yong, J, Zhou, XY:Stochastic controls:Hamiltonian systems and HJB equations. Springer-Verlag, New York (1999) Google Scholar

show all references

References:
[1]

Ait Rami, M, Moore, JB, Zhou, XY:Indefinite stochastic linear quadratic control and generalized differential Riccati equation. SIAM J. Control Optim 40, 1296-1311 (2001) Google Scholar

[2]

Andersson, D, Djehiche, B:A maximum principle for SDEs of mean-field type. Appl. Math. Optim 63, 341-356 (2011) Google Scholar

[3]

Athans, M:The matrix minimum principle. Inform. Control 11, 592-606 (1968) Google Scholar

[4]

Buckdahn, R, Djehiche, B, Li, J:A general stochastic maximum principle for SDEs of mean-field type.Appl. Math. Optim 64, 197-216 (2011) Google Scholar

[5]

Buckdahn, R, Djehiche, B, Li, J, Peng, S:Mean-field backward stochastic differential equations:a limit approach. Ann. Probab 37, 1524-1565 (2009) Google Scholar

[6]

Buckdahn, R, Li, J, Peng, S:Mean-field backward stochastic differential equations and related partial differential equations. Stoch. Proc. Appl 119, 3133-3154 (2009) Google Scholar

[7]

Chen, S, Li, X, Zhou, XY:Stochastic linear quadratic regulators with indefinite control weight costs.SIAM J. Control Optim 36, 1685-1702 (1998) Google Scholar

[8]

Chen, S, Yong, J:Stochastic linear quadratic optimal control problems with random coefficients. Chin.Ann. Math 21B, 323-338 (2000) Google Scholar

[9]

Cui, XY, Li, X, Li, D:Unified framework of mean-field formulations for optimal multi-period meanvariance portfolio selection. IEEE Trans. Auto. Control 59, 1833-1844 (2014) Google Scholar

[10]

Elliott, R, Li, X, Ni, YH:Discrete time mean-field stochastic linear-quadratic optimal control problems.Automatica 49, 3222-3233 (2013) Google Scholar

[11]

Huang, J, Li, X, Wang, TX:Mean-field linear-quadratic-Gaussian (LQG) games for stochastic integral systems. IEEE Trans. Auto. Control (2015). doi:10.1109/TAC.2015.2506620 Google Scholar

[12]

Huang, J, Li, X, Yong, J:A linear-quadratic optimal control problem for mean-field stochastic differential equations in infinite horizon. Math. Control Related Fields 5, 97-139 (2015) Google Scholar

[13]

Kac, M:Foundations of kinetic theory. Proc. Third Berkeley Symp. Math. Stat. Probab 3, 171-197 (1956) Google Scholar

[14]

McKean, HP:A class of Markov processes associated with nonlinear parabolic equations. Proc. Natl.Acad. Sci. USA 56, 1907-1911 (1966) Google Scholar

[15]

Meyer-Brandis, T, Øksendal, B, Zhou, XY:A mean-field stochastic maximum principle via Malliavin calculus. Stochastics 84(5-6), 643-666 (2012). doi:10.1080/17442508.2011.651619 Google Scholar

[16]

Penrose, R:A generalized inverse of matrices. Proc. Cambridge Philos Soc 52, 17-19 (1955) Google Scholar

[17]

Sun, J:Mean-field stochastic linear quadratic optimal control problems:open-loop solvabilities. ESAIM:COCV, 016023 (2016). doi:10.1051/cocv/2 Google Scholar

[18]

Sun, J, Yong, J:Linear quadratic stochastic differential games:open-loop and closed-loop saddle points.SIAM J. Control Optim 52, 4082-4121 (2014) Google Scholar

[19]

Sun, J, Yong, J, Zhang, S:Linear quadratic stochastic two-person zero-sum differential games in an infinite horizon. ESAIM COCV 22, 743-769 (2016). doi:10.1051/cocv/2015024 Google Scholar

[20]

Tang, S:General linear quadratic optimal stochastic control problems with random coefficients:linear stochastic Hamilton systems and backward stochastic Riccati equations. SIAM J. Control Optim 42, 53-75 (2003) Google Scholar

[21]

Wonham, WM:On a matrix Riccati equation of stochastic control. SIAM J. Control Optim 6, 681-697(1968) Google Scholar

[22]

Yong, J:Linear-quadratic optimal control problems for mean-field stochastic differential equations. SIAM J. Control Optim 51, 2809-2838 (2013) Google Scholar

[23]

Yong, J, Zhou, XY:Stochastic controls:Hamiltonian systems and HJB equations. Springer-Verlag, New York (1999) Google Scholar

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