Necessary conditions of Pontraygin’s type for general controlled stochastic Volterra integral equations
ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 16.

This article is addressed to giving a solution to a unsolved problem, i.e., to establish the necessary optimality conditions of Pontraygin’s type for controlled stochastic Volterra integral equations (SVIEs) when the control region is non-convex and the control variable enters into the diffusion. This problem has been open since [J. Yong, Stochastic Process Appl. 116 (2006) 779–795] obtained the analogue result for the case of convex control region. The key is to introduce a pair of suitable second-order adjoint processes (SOAPs). It is found that the usual way of using only one SOAP in the maximum condition for the classical setting of controlled stochastic differential equations does not work here.

DOI : 10.1051/cocv/2020001
Classification : 93E20, 60H20, 49K45
Mots-clés : Stochastic Volterra integral equations, maximum principles, second-order adjoint processes, non-convex control region
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     title = {Necessary conditions of {Pontraygin{\textquoteright}s} type for general controlled stochastic {Volterra} integral equations},
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Wang, Tianxiao. Necessary conditions of Pontraygin’s type for general controlled stochastic Volterra integral equations. ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 16. doi : 10.1051/cocv/2020001. http://www.numdam.org/articles/10.1051/cocv/2020001/

[1] N. Agram and B. Øksendal, Malliavin calculus and optimal control of stochastic Volterra equations. J. Optim. Theory Appl., 167 (2015) 1070–1094 | DOI | MR | Zbl

[2] K. Arrow, Optimal capital policy, the cost of capital and myopic decision rules. Ann. Inst. Stat. Math., 16 (1964) 21–30. | DOI | Zbl

[3] A. Bensoussan, Lectures on stochastic control, Lecture notes in mathematics, Vol. 972 of Nonlinear filtering and stochastic control, proceedings, Cortona (1981). | MR | Zbl

[4] J. Bismut, An introductory approach to duality in optimal stochastic control. SIAM Rev. 20 (1978) 62–78. | DOI | MR | Zbl

[5] V. Boltyanski, R. Gamkrelidze and L. Pontryagin, The theory of optimal processes. I: The maximum principle. Izv. Akad. Nauk SSSr Ser. Mat. 24 (1960) 3–42 (in Russian); English transl. in Am. Math. Soc. Transl. 18 (1961) 341–382. | MR | Zbl

[6] K. Du and Q. Meng, A maximum principle for optimal control of stochastic evolution equations. SIAM J. Control Optim. 51 (2013) 4343–4362. | DOI | MR | Zbl

[7] A. Friedman, Optimal control for hereditary processes. Arch. Rat. Mech. Anal. 15 (1964) 396–416. | DOI | MR | Zbl

[8] M. Fuhrman, Y. Hu and G. Tessitore, Stochastic maximum principle for optimal control of SPDEs. Appl. Math. Optim. 68 (2013) 181–217. | DOI | MR | Zbl

[9] P. Lin and J. Yong, Controlled singular Volterra integral equations and Pontryagin maximum principle. Preprint | arXiv | MR | Zbl

[10] Q. Lü and X. Zhang, General Pontryagin-type stochastic maximum principle and backward stochastic evolution equation in infinite dimensions, Springer Briefs in Mathematics (2014). | DOI | MR

[11] H. Kushner, Necessary conditions for continuous parameter stochastic optimization problems. SIAM J. Control, 10 (1972) 550–565. | DOI | MR | Zbl

[12] P. Protter, Volterra equations driven by semimartingales. Ann. Probab., 13 (1985) 519–530. | MR | Zbl

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

[14] Y. Shi, T. Wang and J. Yong, Optimal control problems of forward-backward stochastic Volterra integral equations. Math. Control Relat. Fields, 5 (2015) 613–649. | DOI | MR | Zbl

[15] V. Vinokurov, Optimal control of processes described by integral equations, I, II, III. Izv. Vysš. Učebn. Zaved. Matematika 7 (1969) 21–33; 8 (1969) 16–23; 9 (1969) 16–25; (in Russian) English transl. in SIAM J. Control 7 (1967) 324–336, 337–345, 346–355. | MR | Zbl

[16] T. Wang, Linear quadratic control problems of stochastic Voltera integral equations. ESAIM: Control Optim. Cal. Var. 24 (2018) 1849–1879. | Numdam | MR | Zbl

[17] T. Wang and J. Yong, Comparison theorems for backward stochastic Volterra integral equations. Stochastic Process Appl., 125 (2015) 1756–1798. | DOI | MR | Zbl

[18] T. Wang and H. Zhang, Optimal control problems of forward-backward stochastic Volterra integral equations with closed control regions. SIAM J. Control Optim. 55 (2017) 2574–2602. | DOI | MR | Zbl

[19] J. Yong, Backward stochastic Volterra integral equations and some related problems. Stochastic Process Appl. 116 (2006) 779–795. | DOI | MR | Zbl

[20] J. Yong, Well-posedness and regularity of backward stochastic Volterra integral equation. Probab. Theory Relat. Fields, 142 (2008) 21–77. | DOI | MR | Zbl

[21] J. Yong and X. Zhou, Stochastic Controls: Hamiltonian Systems and HJB Equations. Springer-Verlag, New York, Berlin (2000). | MR | Zbl

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This work is supported in part by NSF of China (Grant 11401404, 11971332, 11931011) and the Fundamental Research Funds for the central Universities (YJ201605).