# Stochastic global maximum principle for optimization with recursive utilities

- Mingshang Hu
^{1}Email author

**2**:1

https://doi.org/10.1186/s41546-017-0014-7

© The Author(s) 2017

**Received: **19 September 2016

**Accepted: **5 January 2017

**Published: **1 March 2017

## Abstract

In this paper, we study the recursive stochastic optimal control problems. The control domain does not need to be convex, and the generator of the backward stochastic differential equation can contain *z*. We obtain the variational equations for backward stochastic differential equations, and then obtain the maximum principle which solves completely Peng’s open problem.

### Keywords

Backward stochastic differential equations Recursive stochastic optimal control Maximum principle Variational equation### AMS subject classifications

93E20 60H10 49K45## Introduction

*W*be a

*d*-dimensional Brownian motion. The filtration \(\{\mathcal {F}_{t}:t\geq 0\}\) is generated by

*W*, i.e.,

*P*-null sets. Let

*U*be a set in \(\mathbb {R}^{k}\) and

*T*>0 be a given terminal time. Set

*U*is called the control domain and \(\mathcal {U}[0,T]\) is called the set of all admissible controls. In fact, we only need \(E\left [\int _{0} ^{T}|u(s)|^{\beta _{0}}ds\right ]<\infty \) for some

*β*

_{0}>0. For simplicity, we do not explicitly give this

*β*

_{0}in this paper. We consider the following state equation:

*J*(

*u*(·)) over \(\mathcal {U}[0,T]\). If there exists a \(\bar {u}\in \mathcal {U}[0,T]\) such that

*U*is not convex, we use the spike variation method. More precisely, let

*ε*>0 and

*E*

_{ ε }⊂[0,

*T*] be a Borel set with Borel measure |

*E*

_{ ε }|=

*ε*, define

*u*

^{ ε }is called a spike variation of the optimal control \(\bar {u}\). For deriving the maximum principle, we only need to use

*E*

_{ ε }=[

*s*,

*s*+

*ε*] for

*s*∈[0,

*T*−

*ε*] and

*ε*>0. The difficulty in the classical stochastic optimal control problem is the variational equation for

*x*(·), which is completely different from that in the deterministic optimal control problem. Peng (1990) was the first to consider the second-order term in the Taylor expansion of the variation and to obtain the maximum principle for the classical stochastic optimal control problem.

where \(\phi :\mathbb {R}^{n}\rightarrow \mathbb {R}\), \(f:[0,T]\times \mathbb {R} ^{n}\times \mathbb {R}\times \mathbb {R}^{d}\times \mathbb {R}^{k}\rightarrow \mathbb {R}\). Pardoux and Peng (1990) were the first to obtain that the BSDE (3) has a unique solution (*y*(·),*z*(·)) if *f* is measurable of linear growth and satisfies a Lipschitz condition in (*y*,*z*). Duffie and Epstein (1992) introduced the notion of recursive utilities in continuous time, which is a type of BSDE where *f* is independent of *z*. In (El Karoui et al. 1997; 2001), the authors extended the recursive utility to the case where *f* contains *z*. The term *z* can be interpreted as an ambiguity aversion term in the market (see (Chen and Epstein 2002)).

*f*is independent of (

*y*,

*z*), it is easy to check that \(y(0)=E[\phi (x(T))+\int _{0}^{T}f(t,x(t),u(t))dt]\). So it is natural to extend the classical stochastic optimal control problem to the recursive case. we consider the control system which formed by Eqs. (1) and (3), and we define the cost functional

The recursive stochastic optimal control problem is to minimize *J*(*u*(·)) in (4) over \(\mathcal {U}[0,T]\). When the control domain *U* is convex, the local maximum principle for this problem was studied in (Dokuchaev and Zhou 1999, Ji and Zhou 2006, Peng 1993, Shi and Wu 2006, Wu 1998, Xu 1995) see also the references therein. In this paper, the control domain *U* is not necessarily convex, and we shall obtain our global maximum principle using the spike variation method.

A direct method for treating this problem would be to consider the second-order terms in the Taylor expansion of the variation for the BSDE (3) as in (Peng 1990). When *f* depends nonlinearly on *z*, there are two major difficulties (see (Yong 2010)) one meets: (i) What is the second-order variational equation for the BSDE (3)? it is not similar to the one in (Peng 1990). (ii) How to obtain the second-order adjoint equation which seems to be unexpectedly complicate due to the quadratic form with respect to the variation of *z*?

Facing on these difficulties, Peng (1998) proposed the following open problem on page 269:

“The corresponding ‘global maximum principle’ for the case where *f* depends nonlinearly on *z* is open.”

Recently, a new method for treating this problem is to see *z*(·) as a control process and the terminal condition *y*(*T*)=*ϕ*(*x*(*T*)) as a constraint, and then use the Ekeland variational principle to obtain the maximum principle. This idea was used in (Kohlmann and Zhou 2000; Lim and Zhou 2001) for studying the backward linear-quadratic optimal control problem, and then in (Wu 2013; Yong 2010) for studying the recursive stochastic optimal control problem. But the maximum principle obtained by these method contains unknown parameters.

In this paper, we overcome these both major difficulties one meets in the above direct method by introducing two new adjoint equations in Peng’s open problem. The second-order variational equation for the BSDE (3) and the maximum principle are obtained. The main difference of our variational equations with those in (Peng 1990) consists in the term \(\langle p(t),\delta \sigma (t)\rangle I_{E_{\varepsilon }}(t)\) (see equation (15) in Variational equation for BSDEs and maximum principle for the definition of *p*(*t*)) in the variation of *z*, which is *O*(*ε*) for any order expansion of *f*. So it is not helpful to use the second-order Taylor expansion for treating this term. Moreover, we also obtain the structure of the variation for (*y*,*z*) and the variation for *x*. Based on this, we can get the second-order adjoint equation. Due to the term \(\langle p(t),\delta \sigma (t)\rangle I_{E_{\varepsilon }}(t)\) in the variation of *z*, our global maximum principle is novel and different from that in (Wu 2013; Yong 2010), which solves completely Peng’s open problem. Furthermore, our maximum principle is stronger than the one in (Wu 2013; Yong 2010) (see Example 1).

The paper is organized as follows. In Preliminaries, we give some basic results and the idea for the variation of BSDEs. The variational equations for BSDEs and the maximum principle are deduced in Variational equation for BSDEs and maximum principle. In Problem with state constraint, we obtain the maximum principle for the control system with state constraint.

## Preliminaries

*d*=1. We need the following assumption:

- (A1)
The functions

*b*=*b*(*t*,*x*,*u*),*σ*=*σ*(*t*,*x*,*u*) are twice continuously differentiable with respect to*x*;*b*,*b*_{ x },*b*_{ xx },*σ*,*σ*_{ x },*σ*_{ xx }are continous in (*x*,*u*);*b*_{ x },*b*_{ xx },*σ*_{ x },*σ*_{ xx }are bounded;*b*,*σ*are bounded by*C*(1+|*x*|+|*u*|).

*x*

^{ ε }(·),

*u*

^{ ε }(·)). Set

*b*

_{ x }(

*t*), \(b_{xx}^{i}(t)\),

*δ*

*b*

_{ x }(

*t*), \(\delta b_{xx}^{i}(t)\),

*σ*(

*t*),

*σ*

_{ x }(

*t*), \(\sigma _{xx}^{i}(t)\),

*δ*

*σ*(

*t*),

*δ*

*σ*

_{ x }(

*t*) and \(\delta \sigma _{xx}^{i}(t)\),

*i*≤

*n*, where \(b_{x}=(b_{x_{j}}^{i})_{i,j}\), \(\sigma _{x}=(\sigma _{x_{j} }^{i})_{i,j}\). Let

*x*

_{ i }(·),

*i*=1, 2, be the solution of the following stochastic differential equation (SDE for short):

respectively, where \(b_{xx}(t)x_{1}(t)x_{1}(t)=(\text {tr}[b_{xx}^{1} (t)x_{1}(t)x_{1}(t)^{T}],\ldots,\text {tr}[b_{xx}^{n}(t)x_{1}(t)x_{1} (t)^{T}])^{T}\) and similarly for *σ*
_{
xx
}(*t*)*x*
_{1}(*t*)*x*
_{1}(*t*).

###
**Lemma 1**

*β*≥1,

*ϕ*

_{ xx }is bounded, we have the following expansion:

*x*

_{2}(

*t*)=

*O*(

*ε*), and

In the following we recall standard estimates of BSDEs (see (Briand et al. 2003) and the references therein).

###
**Lemma 2**

*Y*

_{ i },

*Z*

_{ i }),

*i*=1,2, be the solution of the following BSDE

*E*[|

*ξ*

_{ i }|

^{ β }]<

*∞*, \(f_{i}=f_{i}(s,\omega,y,z):[0,T]\times \Omega \times \mathbb {R}\times \mathbb {R}^{d}\rightarrow \mathbb {R}\) is progressively measurable for each fixed (

*y*,

*z*), Lipschitz in (

*y*,

*z*), and \(E[(\int _{0}^{T}|f_{i}(s,0,0)|ds)^{\beta }]<\infty \) for some

*β*>1. Then there exists a constant

*C*

_{ β }>0 depending on

*β*,

*T*and the Lipschitz constant such that

*ξ*

_{1}=0 and

*f*

_{1}=0, we have

## Variational equation for BSDEs and maximum principle

### Peng’s open problem

Suppose *n*=1 and *d*=1 for simplicity of presentation. The results for the multi-dimensional case will be given in the next subsection.

We consider the control system composed of SDE (1) and BSDE (3). The cost function *J*(*u*(·)) is defined in (4). The control problem is to minimize *J*(*u*(·)) over \(\mathcal {U}[0,T]\).

- (A2)
The functions

*f*=*f*(*t*,*x*,*y*,*z*),*ϕ*=*ϕ*(*x*) are twice continuously differentiable with respect to (*x*,*y*,*z*);*f*,*Df*,*D*^{2}*f*are continuous in (*x*,*y*,*z*,*u*);*Df*,*D*^{2}*f*,*ϕ*_{ xx }are bounded;*f*is bounded by*C*(1+|*x*|+|*y*|+|*z*|+|*u*|).

Here *Df* is the gradient of *f* with respect to (*x*,*y*,*z*), *D*
^{2}
*f* is the Hessian matrix of *f* with respect to (*x*,*y*,*z*).

Let \(\bar {u}(\cdot)\) be the optimal control and let \((\bar {x}(\cdot),\bar {y}(\cdot),\bar {z}(\cdot))\) be the corresponding solution of the equations (1) and (3). Similarly, we define (*x*
^{
ε
}(·),*y*
^{
ε
}(·),*z*
^{
ε
}(·),*u*
^{
ε
}(·)).

where *F*(*t*) and *G*(*t*) are adapted processes with suitable properties, the will be chosen later.

###
**Remark 1**

From the above computation, we can see that *F*(*s*) and *G*(*s*) do not appear in the *d*
*W*(*s*)-term.

*β*≥2, we have

*o*(

*ε*) is in the above sense, similarly for

*O*(

*ε*). If we replace

*ϕ*(

*x*

^{ ε }(

*T*)) by \(\phi (\bar {x}(T))+p(T)(x_{1}(T)+x_{2}(T))+\frac {1}{2}P(T)(x_{1}(T))^{2} +o(\varepsilon)\) and note (17), then BSDE (3) corresponding to (

*y*

^{ ε }(·),

*z*

^{ ε }(·),

*u*

^{ ε }(·)) can be rewritten as

###
**Remark 2**

By Lemma 1, for any *β*≥2, we have \(E\left [|\phi (x^{\varepsilon }(T))-\phi (\bar {x}(T))|^{\beta }\right ]=O(\varepsilon ^{\beta /2})\). So the purpose of this transformation is to simplify the complex terminal condition *ϕ*(*x*
^{
ε
}(*T*)).

*F*(

*t*) and

*G*(

*t*) are chosen to satisfy the following conditions:

- (1)
*F*(*t*) and*G*(*t*) are determined by \((\bar {x}(\cdot),\bar {y} (\cdot),\bar {z}(\cdot),\bar {u}(\cdot))\) ; - (2)
\(f(s,\bar {x}(s)+x_{1}(s)+x_{2}(s),\bar {y}(s)+A_{8}(s),\bar {z}(s)+A_{9}(s),\bar {u}(s))-f(s,\bar {x}(s),\bar {y}(s),\bar {z}(s),\bar {u}(s))+A_{2}(s)(x_{1}(s)+x_{2}(s))+\frac {1}{2}A_{3}(s)(x_{1}(s))^{2} =O(\varepsilon)\) and the

*O*(*ε*) part does not contain*x*_{1}(*s*) and*x*_{2}(*s*).

*F*(

*t*) and

*G*(

*t*) must have been chosen as follows:

where \(f_{x}(t)=f_{x}(t,\bar {x}(t),\bar {y}(t),\bar {z}(t),\bar {u}(t))\) and similarly for *f*
_{
y
}(*t*), *f*
_{
z
}(*t*) and *D*
^{2}
*f*(*t*).

###
**Remark 3**

If *f* is independent of (*y*,*z*), then the adjoint Eqs. (15) and (16) are the same as those in (Peng 1990).

*p*(·),

*q*(·)) and (

*P*(·),

*Q*(·)), respectively, and for any

*β*≥2,

Consider the following BSDE:

In the following theorem, we will prove that \(\hat {y}^{\varepsilon }(t)-\hat {y}(t)=o(\varepsilon)\).

###
**Theorem 1**

*β*≥2,

###
*Proof*

*D*

^{2}

*f*is bounded, we get that for each

*β*≥2,

###
**Remark 4**

*y*

_{1},

*z*

_{1}) satisfies the following BSDE:

*L*(

*t*). Note the Eqs. (37) and (39), then the adjoint equations for (

*z*

_{1}(

*t*))

^{2}and other terms are essential for

*x*

_{1}(

*t*),

*x*

_{2}(

*t*) and

*x*

_{1}(

*t*)(

*x*

_{1}(

*t*))

^{ T }, which is solved in (Peng 1990). In order to further explain the difference between the expansions for SDEs and BSDEs, we consider the following equations:

The main difference with the variation equation for SDEs is equation (42) which is due to the term \(p(t)\delta \sigma (t)I_{E_{\varepsilon }}(t)\) in the variation of *z*. If *f* is independent of *z*, the variational equations for (*y*,*z*) are the same as in (Peng 1990), which is pointed out in (Peng 1998).

*γ*(

*s*)>0, we then define the following function:

where (*p*,*q*,*P*) is defined by the Eqs. (15) and (16). Thus we obtain the following maximum principle.

###
**Theorem 2**

where \(\mathcal {H}(\cdot)\) is defined in (44).

###
**Remark 5**

If *f* is independent of (*y*,*z*), the above theorem is called Peng’s maximum principle, which was first obtained by Peng in (1990).

If the control domain *U* is convex, we can get the following corollary which was obtained by Peng in (1993).

###
**Corollary 1**

*U*is convex and

*b*,

*σ*,

*f*are continuously differentiable with respect to

*u*, then

Now we give an example to compare our result with the result in (Wu 2013; Yong 2010).

###
**Example 1**

*n*=

*d*=

*k*=1.

*U*is a given subset in \(\mathbb {R}\). Consider the following control system:

*U*={0,1},

*f*(0)=0,

*f*

^{′}(0)<0,

*f*(1)>0,

*f*(−1)<0, it is easy to verify that \((\bar {x},\bar {y},\bar {z},\bar {u})=(0,0,0,0)\) satisfies (46), thus \(\bar {u}=0\) is an optimal control. But \(f_{z}(\bar {z}(t))(1-\bar {u}(t))<0\), which implies that \(\bar {u}=0\) is not an optimal control for the case

*U*=[0,1]. The maximum principle in (Yong 2010) is \(f_{z}(\bar {z}(t))(u-\bar {u}(t))\geq 0\), ∀

*u*∈

*U*,

*a*.

*e*.,

*a*.

*s*., which only covers the case when

*U*is convex. The maximum principle in (Wu 2013) contains two unknown parameters.

###
**Remark 6**

The optimal control problem is to minimize *J*(*u*(·),*y*
_{0},*v*(·))=*y*
_{0} over \(\mathcal {\tilde {U}}[0,T]\). Obviously, this problem is equivalent to Peng’s problem, i.e., the control system composed of SDE (1) and BSDE (3), the control problem is to minimize *J*(*u*(·)) over \(\mathcal {U}[0,T]\). Thus our maximum principle also solves completely this control problem.

### Multi-dimensional case

*m*-dimensional, \(\phi :\mathbb {R}^{n}\rightarrow \mathbb {R}^{m}\), \(f:[0,T]\times \mathbb {R}^{n} \times \mathbb {R}^{m}\times \mathbb {R}^{m\times d}\times \mathbb {R} ^{k}\rightarrow \mathbb {R}^{m}\). The cost functional is defined by

*n*≥1,

*m*≥1,

*d*≥1, and introduce the following adjoint equations: for

*i*=1,…,

*m*,

*F*

_{ i }(

*t*) and

*G*

_{ i }(

*t*) are defined as follows:

*D*

^{2}

*f*

^{ i }is the Hessian matrix of

*f*

^{ i }with respect to (

*x*,

*y*,

*z*

^{1},…,

*z*

^{ d }). Let \(\hat {y}(t)=(\hat {y}^{1}(t),\ldots,\hat {y} ^{m}(t))^{T}\), \(\hat {z}(t)=(\hat {z}^{ij}(t))\) be the solution of the following BSDE:

Applying Itô’s formula to \(\langle \gamma (t),\hat {y}(t)\rangle \), we obtain the following maximum principle.

###
**Theorem 3**

where *p*, *q*
^{
j
}, *P* and *γ* are given in Eqs. (50), (51), (53) and (56).

## Problem with state constraint

For simplicity of presentation, suppose *d*=*m*=1, the multi-dimensional case can be treated with the same method.

*J*(

*u*(·)) is defined in (4). In addition, we consider the following state constraint:

where \(\varphi :\mathbb {R}^{n}\times \mathbb {R}\rightarrow \mathbb {R}\). We need the following assumption: (A3) The function *φ*=*φ*(*x*,*y*) is twice continuously differentiable with respect to (*x*,*y*); *D*
^{2}
*φ* is bounded.

*J*(

*u*(·)) over \(\mathcal {U}_{ad}[0,T]\).

*x*(·),

*y*(·),

*z*(·),

*u*(·)) for any \(u(\cdot)\in \mathcal {U}[0,T]\). For any

*ρ*>0, define the following cost functional on \(\mathcal {U}[0,T]\):

*J*

_{ ρ }(·) is continuous, otherwise we can use the technique in (Tang and Li 1994; Wu 2013) and obtain the same result. Thus, by Ekeland’s variational principle, there exists a \(u_{\rho }(\cdot)\in \mathcal {U}[0,T]\) such that

*ε*>0, let

*E*

_{ ε }⊂[0,

*T*] be a Borel subset with Borel measure |

*E*

_{ ε }|=

*ε*, and define

*x*

_{ ρ }(·),

*y*

_{ ρ }(·),

*z*

_{ ρ }(·)) be the solution corresponding to

*u*

_{ ρ }(·). Similarly, \((x_{\rho }^{\varepsilon }(\cdot),y_{\rho }^{\varepsilon }(\cdot),z_{\rho }^{\varepsilon }(\cdot),u_{\rho }^{\varepsilon }(\cdot))\) is associated with \(u_{\rho }^{\varepsilon }(\cdot)\). Thus, with (60), we get

*p*

^{ ρ }(·),

*q*

^{ ρ }(·)) and (

*P*

^{ ρ }(·),

*Q*

^{ ρ }(·)) be, respectively, the solution of Eq. (15) and (16) with \((\bar {x}(\cdot),\bar {y}(\cdot),\bar {z}(\cdot),\bar {u}(\cdot))\) replaced by (

*x*

_{ ρ }(·),

*y*

_{ ρ }(·),

*z*

_{ ρ }(·),

*u*

_{ ρ }(·)), and all the coefficients endowed with the superscript

*ρ*. Then, the same computation as for Theorem 1 leads to:

*ε*>0 and

*E*

_{ ε }⊂[0,

*T*] with |

*E*

_{ ε }|=

*ε*, we obtain

*λ*

_{ ρ }and

*μ*

_{ ρ }, we have |

*λ*

_{ ρ }|

^{2}+|

*μ*

_{ ρ }|

^{2}=1. Thus there exists a subsequence of (

*λ*

_{ ρ },

*μ*

_{ ρ }) which converges to (

*λ*,

*μ*) with |

*λ*|

^{2}+|

*μ*|

^{2}=1, as

*ρ*→0. As \(d(u_{\rho }(\cdot),\bar {u}(\cdot))\leq \sqrt {\rho }\), we can choose a sub-subsequence satisfying

*p*

_{0}(·),

*q*

_{0}(·)) and (

*P*

_{0}(·),

*Q*

_{0}(·)) is, respectively, the solution of Eqs. (15) and (16),

Thus we obtain the following theorem.

###
**Theorem 4**

*λ*and

*μ*with |

*λ*|

^{2}+|

*μ*|

^{2}=1 such that

*p*(·),

*q*(·)), (

*P*(·),

*Q*(·)), (

*p*

_{0}(·),

*q*

_{0}(·)), (

*P*

_{0}(·),

*Q*

_{0}(·)) and

*γ*(·) are defined in (68), (15), (16), (69), (70) and (71).

## Declarations

### Acknowledgments

I would like to thank Professor S. Peng and S. Ji for many helpful discussions and valuable comments. I also would like to thank the referees for careful reading and valuable comments.

Research supported by NSF (No. 11671231, 11201262 and 10921101), Shandong Province (No.BS2013SF020 and ZR2014AP005), Young Scholars Program of Shandong University and the 111 Project (No. B12023).

### Authors’ contributions

All authors read and approved the final manuscript.

### Competing interests

The author declares that he has no competing interests.

**Open Access** This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

## References

- Briand, PH, Delyon, B, Hu, Y, Pardoux, E, Stoica, L:
*L*^{ p }solutions of backward stochastic differential equations. Stochastic Process. Appl. 108, 109–129 (2003).MathSciNetView ArticleMATHGoogle Scholar - Chen, Z, Epstein, L: Ambiguity, risk, and asset returns in continuous time. Econometrica. 70, 1403–1443 (2002).MathSciNetView ArticleMATHGoogle Scholar
- Dokuchaev, M, Zhou, XY: Stochastic controls with terminal contingent conditions. J. Math. Anal. Appl. 238, 143–165 (1999).MathSciNetView ArticleMATHGoogle Scholar
- Duffie, D, Epstein, L: Stochastic differential utility. Econometrica. 60, 353–394 (1992).MathSciNetView ArticleMATHGoogle Scholar
- El Karoui, N, Peng, S, Quenez, MC: Backward stochastic differential equations in finance. Math. Finance. 7, 1–71 (1997).MathSciNetView ArticleMATHGoogle Scholar
- El Karoui, N, Peng, S, Quenez, MC: A dynamic maximum priciple for the optimization of recursive utilities under constraints. Ann. Appl. Probab. 11, 664–693 (2001).MathSciNetView ArticleMATHGoogle Scholar
- Ji, S, Zhou, XY: A maximum principle for stochastic optimal control with terminal state constrains, and its applications. Comm. Inf. Syst. 6, 321–337 (2006).MATHGoogle Scholar
- Kohlmann, M, Zhou, XY: Relationship between backward stochastic differential equations and stochastic controls: a linear-quadratic approach. SIAM J. Control Optim. 38, 1392–1407 (2000).MathSciNetView ArticleMATHGoogle Scholar
- Lim, A, Zhou, XY: Linear-quadratic control of backward stochastic differential equations. SIAM J. Control Optim. 40, 450–474 (2001).MathSciNetView ArticleMATHGoogle Scholar
- Pardoux, E, Peng, S: Adapted Solutions of Backward Stochastic Equations. Systerm Control Lett. 14, 55–61 (1990).MathSciNetView ArticleMATHGoogle Scholar
- Peng, S: A general stochastic maximum principle for optimal control problems. SIAM J. Control Optim. 28, 966–979 (1990).MathSciNetView ArticleMATHGoogle Scholar
- Peng, S: Backward stochastic differential equations and applications to optimal control. Appl. Math. Optim. 27, 125–144 (1993).MathSciNetView ArticleMATHGoogle Scholar
- Peng, S: Open problems on backward stochastic differential equations. In: Chen, S, Li, X, Yong, J, Zhou, XY (eds.)Control of distributed parameter and stocastic systems, pp. 265–273, Boston: Kluwer Acad. Pub. (1998).Google Scholar
- Shi, J, Wu, Z: The maximum principle for fully coupled forward-backward stochastic control system. Acta Automat. Sinica. 32, 161–169 (2006).MathSciNetGoogle Scholar
- Tang, S, Li, X: Necessary conditions for optimal control of stochastic systems with random jumps. SIAM J. Control Optim. 32, 1447–1475 (1994).MathSciNetView ArticleMATHGoogle Scholar
- Wu, Z: Maximum principle for optimal control problem of fully coupled forward-backward stochastic systems. Syst. Sci. Math. Sci. 11, 249–259 (1998).MathSciNetMATHGoogle Scholar
- Wu, Z: A general maximum principle for optimal control of forward-backward stochastic systems. Automatica. 49, 1473–1480 (2013).MathSciNetView ArticleMATHGoogle Scholar
- Xu, W: Stochastic maximum principle for optimal control problem of forward and backward system. J. Austral. Math. Soc. Ser. B. 37, 172–185 (1995).MathSciNetView ArticleMATHGoogle Scholar
- Yong, J: Optimality variational principle for controlled forward-backward stochastic differential equations with mixed initial-terminal conditions. SIAM J. Control Optim. 48, 4119–4156 (2010).MathSciNetView ArticleMATHGoogle Scholar
- Yong, J, Zhou, XY: Stochastic controls: Hamiltonian systems and HJB equations. Springer-Verlag, New York (1999).View ArticleMATHGoogle Scholar