PATH-DEPENDENT CONTROLLED MEAN-FIELD COUPLED FORWARD-BACKWARD SDES: THE ASSOCIATED STOCHASTIC MAXIMUM PRINCIPLE\

被引:0
作者
Buckdahn, Rainer [1 ,2 ]
Li, Juan [2 ,3 ]
Li, Junsong [3 ,4 ]
Xing, Chuanzhi [2 ,5 ]
机构
[1] Univ Brest, UMR 6205, Lab Math Bretagne Atlantique, CNRS, 6 Ave Gorgeu, F-29200 Brest, France
[2] Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Qingdao 266237, Peoples R China
[3] Shandong Univ, Sch Math & Stat, Weihai 264209, Peoples R China
[4] Shandong Technol & Business Univ, Sch Math & Informat Sci, Yantai 264003, Peoples R China
[5] Shandong Univ, Frontiers Sci Ctr Nonlinear Expectat, Minist Educ, Qingdao 266237, Peoples R China
关键词
mean-field; forward-backward stochastic differential equation; Malliavin calculus; stochastic maximum principle; DIFFERENTIAL-EQUATIONS;
D O I
10.1137/23M1590652
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the present paper we discuss a new type of mean-field coupled forward-backward stochastic differential equations (MFFBSDEs). The novelty consists of the fact that the coefficients of both the forward as well as the backward SDEs depend not only on the controlled solution processes (Xt,Yt,Zt) at the current time t, but also on the law of the paths of (X,Y,u) of the solution process and the control process. The existence of the solution for such a MFFBSDE which is fully coupled through the law of the paths of (X, Y ) in the coefficients of both the forward and the backward equations is proved under rather general assumptions. Concerning the law, we just suppose the continuity under the 2-Wasserstein distance of the coefficients with respect to the law of (X, Y ). The uniqueness is shown under Lipschitz assumptions and the nonanticipativity of the law of X in the forward equation. The main part of this work is devoted to the study of Pontryagin's maximal principle for such a MFFBSDE. The dependence of the coefficients on the law of the paths of the solution processes and their control makes that a completely new and interesting criterion for the optimality of a stochastic control for the MFFBSDE is obtained. In particular, also the Hamiltonian is novel and quite different from that in the existing literature. Last but not least, under the assumption of convexity of the Hamiltonian we show that our optimality condition is not only necessary but also sufficient.
引用
收藏
页码:2124 / 2153
页数:30
相关论文
共 20 条
[1]  
Antonelli F., 1993, Ann. Appl. Probab, V3, P777, DOI DOI 10.1214/AOAP/1177005363
[2]   A Stochastic Maximum Principle for General Mean-Field Systems [J].
Buckdahn, Rainer ;
Li, Juan ;
Ma, Jin .
APPLIED MATHEMATICS AND OPTIMIZATION, 2016, 74 (03) :507-534
[3]   Mean-field backward stochastic differential equations and related partial differential equations [J].
Buckdahn, Rainer ;
Li, Juan ;
Peng, Shige .
STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 2009, 119 (10) :3133-3154
[4]   MEAN-FIELD BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS: A LIMIT APPROACH [J].
Buckdahn, Rainer ;
Djehiche, Boualem ;
Li, Juan ;
Peng, Shige .
ANNALS OF PROBABILITY, 2009, 37 (04) :1524-1565
[5]  
Cardaliaguet P., 2013, NOTES MEAN FIELD GAM
[6]  
Carmona R, 2018, PROB THEOR STOCH MOD, V83, P3, DOI 10.1007/978-3-319-58920-6_1
[7]   A forward-backward stochastic algorithm for quasi-linear PDEs [J].
Delarue, F ;
Menozzi, S .
ANNALS OF APPLIED PROBABILITY, 2006, 16 (01) :140-184
[8]   On the Kantorovich-Rubinstein theorem [J].
Edwards, D. A. .
EXPOSITIONES MATHEMATICAE, 2011, 29 (04) :387-398
[9]  
Hu MS, 2017, PROBAB UNCERTAIN QUA, V2, DOI 10.1186/s41546-017-0014-7
[10]   SOLUTION OF FORWARD-BACKWARD STOCHASTIC DIFFERENTIAL-EQUATIONS [J].
HU, Y ;
PENG, S .
PROBABILITY THEORY AND RELATED FIELDS, 1995, 103 (02) :273-283