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All issues Volume 32 (2026) ESAIM: COCV, 32 (2026) 15 References
Open Access
Issue
ESAIM: COCV
Volume 32, 2026
Article Number 15
Number of page(s) 27
DOI https://doi.org/10.1051/cocv/2026001
Published online 25 February 2026
  1. J.M. Bismut, An introductory approach to duality in optimal stochastic control. SIAM Rev. 20 (1978) 62–78. [CrossRef] [MathSciNet] [Google Scholar]
  2. S. Peng, A general stochastic maximum principle for optimal control problems. SIAM J. Control Optim. 28 (1990) 966–979. [Google Scholar]
  3. Y. Hu and S. Peng, Maximum principle for semilinear stochastic evolution control systems. Stoch. Stoch. Rep. 33 (1990) 159–180. [Google Scholar]
  4. R. Tao and Z. Wu, Maximum principle for optimal control problems of forward—backward regime-switching system and applications. Syst. Control Lett. 61 (2012) 911–917. [CrossRef] [Google Scholar]
  5. L. Zhang and Y. Shi, Maximum principle for forward-backward doubly stochastic control systems and applications. ESAIM Control Optim. Calc. Var. 17 (2011) 1174–1197. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  6. L. Zhang, Q. Zhou and J. Yang, Necessary condition for optimal control of doubly stochastic systems. Math. Control Relat. Fields 10 (2020) 379–403. [Google Scholar]
  7. A. Bensoussan, Stochastic maximum principle for distributed parameter systems. J. Franklin Inst. 315 (1983) 387–406. [Google Scholar]
  8. S. Tang and X. Li, Maximum principle for optimal control of distributed parameter stochastic systems with random jumps. Lect. Notes Pure Appl. Math. 152 (1993) 867–890. [Google Scholar]
  9. C. Tudor, Optimal control for semilinear stochastic evolution equations. Appl. Math. Optim. 20 (1989) 319–331. [Google Scholar]
  10. X. Zhou, On the necessary conditions of optimal controls for stochastic partial differential equations. SIAM J. Control Optim. 31 (1993) 1462–1478. [CrossRef] [MathSciNet] [Google Scholar]
  11. Q. Lü and X. Zhang, General Pontryagin-type Stochastic Maximum Principle and Backward Stochastic Evolution Equations in Infinite Dimensions. Springer (2014). [Google Scholar]
  12. Q. Lü and X. Zhang, Mathematical Control Theory for SPDEs. Springer Nature, Switzerland AG (2021). [Google Scholar]
  13. Q. Lü and X. Zhang, Operator-valued backward stochastic lyapunov equations in infinite dimensions, and its application. Math. Control Relat. Fields 8 (2018) 337–381. [CrossRef] [MathSciNet] [Google Scholar]
  14. K. Du and Q. Meng, A maximum principle for optimal control of stochastic evolution equations. SIAM J. Control Optim. 51 (2013) 4343–4362. [Google Scholar]
  15. M. Fuhrman, Y. Hu and G. Tessitore, Stochastic maximum principle for optimal control of SPDEs. Appl. Math. Optim. 68 (2013) 181–217. [Google Scholar]
  16. G. Guatteri and G. Tessitore, On the backward stochastic Riccati equation in infinite dimensions. SIAM J. Control Optim. 44 (2005) 159–194. [Google Scholar]
  17. G. Guatteri and G. Tessitore, Well posedness of operator valued backward stochastic Riccati equations in infinite dimensional spaces. SIAM J. Control Optim. 52 (2014) 3776–3806. [Google Scholar]
  18. W. Stannat and L. Wessels, Peng's maximum principle for stochastic partial differential equations. SIAM J. Control Optim. 59 (2021) 3552–3573. [CrossRef] [MathSciNet] [Google Scholar]
  19. A. Bensoussan, Maximum principle and dynamic programming approaches of the optimal control of partially observed diffusions. Stochastics 9 (1983) 169–222. [CrossRef] [MathSciNet] [Google Scholar]
  20. U.G. Haussmann, The maximum principle for optimal control of diffusions with partial information. SIAM J. Control Optim. 25 (1987) 341–361. [CrossRef] [MathSciNet] [Google Scholar]
  21. X. Li and S. Tang, General necessary conditions for partially observed optimal stochastic controls. J. Appl. Probab. 32 (1995) 1118–1137. [CrossRef] [MathSciNet] [Google Scholar]
  22. G. Wang and Z. Wu, The maximum principles for stochastic recursive optimal control problems under partial information. IEEE Trans. Automat. Control 54 (2009) 1230–1242. [CrossRef] [MathSciNet] [Google Scholar]
  23. Z. Wu, A maximum principle for partially observed optimal control of forward-backward stochastic control systems. Sci. China Inf. Sci. 53 (2010) 2205–2214. [CrossRef] [MathSciNet] [Google Scholar]
  24. G. Wang, C. Zhang and W. Zhang, Stochastic maximum principle for mean-field type optimal control under partial information. IEEE Trans. Automat. Control 59 (2013) 522–528. [Google Scholar]
  25. T. Chen and Z. Wu, A general maximum principle for partially observed mean-field stochastic system with random jumps in progressive structure. Math. Control Relat. Fields 13 (2023) 664–694. [CrossRef] [MathSciNet] [Google Scholar]
  26. M. Li and Z. Wu, The second-order maximum principle for partially observed optimal controls. Math. Control Relat. Fields 14 (2024) 133–165. [Google Scholar]
  27. A. Bensoussan and M. Viot, Optimal control of stochastic linear distributed parameter systems. SIAM J. Control 13 (1975) 904–926. [Google Scholar]
  28. Q. Lu and X. Zhang, Well-posedness of backward stochastic differential equations with general filtration. J. Differ. Equ. 254 (2013) 3200–3227. [Google Scholar]
  29. G. Da Prato and J. Zabczyk, Stochastic Equations in Infinite Dimensions. Cambridge university Press, United Kingdom (2014). [Google Scholar]
  30. K. Yosida, Functional Analysis. Springer Science & Business Media (2012). [Google Scholar]
  31. J. Huang, X. Li and G. Wang, Maximum principles for a class of partial information risk-sensitive optimal controls. IEEE Trans. Automat. Control 55 (2010) 1438–1443. [Google Scholar]
  32. J. Huang, G. Wang and J. Xiong, A maximum principle for partial information backward stochastic control problems with applications. SIAM J. Control Optim. 48 (2009) 2106–2117. [CrossRef] [MathSciNet] [Google Scholar]
  33. Q. Meng, A maximum principle for optimal control problem of fully coupled forward-backward stochastic systems with partial information. Sci. China Ser. A 52 (2009) 1579–1588. [Google Scholar]

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