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        Open Loop Saddle Point on Linear Quadratic Stochastic Di ff erential Games

        2014-03-03 00:49:10

        (1.School of Basic Science,Changchun University of Technology,130012)

        (2.College of Mathematics,Jilin University,Changchun,130012)

        Open Loop Saddle Point on Linear Quadratic Stochastic Di ff erential Games

        WANG JUN1,2

        (1.School of Basic Science,Changchun University of Technology,130012)

        (2.College of Mathematics,Jilin University,Changchun,130012)

        Communicated by Li Yong

        In this paper,we deal with one kind of two-player zero-sum linear quadratic stochastic di ff erential game problem.We give the existence of an open loop saddle point if and only if the lower and upper values exist.

        stochastic di ff erential game,saddle point,open loop strategy

        1 Introduction

        In this paper,we consider the two-player zero-sum linear quadratic stochastic di ff erential games on a fi nite horizon.The fundamental theory of di ff erential games was given in 1965 by [1].Pontryagin’s Maximum Principle(see[2])and Bellman’s Dynamic Programming(see [3])are applied to games.Bensoussan[4],Bensoussan and Friedman[5]studied stochastic di ff erential games.It is well known that the existence of open loop saddle points guarantees the existence of the value of the di ff erential games;the existence and equivalence of the lower and upper values guarantee the existence of the value of the di ff erential games.These statements can be found,for instance,in[6–8].

        Zhang[9]considered the two-person linear quadratic di ff erential games and showed that the value of the game exists if and only if both the upper and lower values exist.The same outcomes were proved by Delfour[10]by using another way.Specially,Mou and Yong[11]discussed two-person zero-sum linear quadratic stochastic di ff erential games in Hilbert spaces. The stochastic form of this problem is studied in this paper and we can achieve the same outcomes:No need of equivalence of the lower and upper values,we can prove the existence of the saddle point if and only if the lower and upper values exist.Due to stochastic op-timal control(see[4],[12])is concerned,in the present paper we use the Peng’s stochastic maximum principle(see[12])to gain the adjoint equation of this stochastic state system.

        This paper is organized as follows:Section 2 provides the basic framework.Some results of payo fffunction are discussed in Section 3.The main outcomes are characterized in Section 4,where we prove the existence of the saddle point by the existence of lower and upper values in this di ff erential game.

        2 Statement of the Problem

        Let?be a bounded smooth domain in Rn,(?,F,P)be a probability space with fi ltration Ft,and W(·)be an Rn-valued standard Wiener process.We assume that

        Let x be a solution of the following stochastic di ff erential equation:

        where x0is the initial state at time t=0.We call that u(t)∈L2(0,T;Rm),m≥1,is the strategy of the fi rst player if,u(·)is an Ft-adapted process with values in U(a nonempty subset of Rm(control domain))such that

        and v(t)∈L2(0,T;Rk),k≥1,is the strategy of the second player.

        For any choice of controls u,v,we have the following payo fffunction:

        We assume that F is an n×n matrix,and A(t),B1(t),B2(t),C1(t),C2(t),D(t)and Q(t) are matrix functions of appropriate order that are measurable and bounded a.e.in[0,T]. Moreover,F and Q(t)are symmetrical.We write A,B1,B2,C1,C2,D and Q instead of A(t),B1(t),B2(t),C1(t),C2(t),D(t)and Q(t)throughout this paper and use the above assumptions.T>0 is a given fi nal time.|x|and x·y are the usual norm and inner product, respectively.

        The more general quadratic structure involving cross terms and di ff erent quadratic weights N1u·u and N2v·v on u and v can be simpli fi ed to our model(see[10]).

        De fi nition 2.1The game is said to achieve its open loop lower value if

        is fi nite and is said to achieve its open loop upper value if

        is fi nite.

        Obviously,we always have

        De fi nition 2.2If bothv?(x0)andv+(x0)exist andv?(x0)=v+(x0),then we say that the open loop value of the game exists and is denoted byv(x0).

        De fi nition 2.3A pair of controls(ˉu,ˉv)∈L2(0,T;Rm)×L2(0,T;Rk)is called an open loop saddle point of the stochastic di ff erential game(2.1)with payo ff(2.2),if for all(t,x)∈(0,T)×?,u∈L2(0,T;Rm)andv∈L2(0,T;Rk),

        By De fi nition 2.3,(2.3)is equivalent to

        De fi nition 2.4Forx0∈Rn,we de fi ne

        3 Some Results of Payo ffFunction

        Since the payo fffunction(2.2)is quadratic,it is in fi nitely di ff erentiable.We can prove

        where x is the solution of(2.1)andˉy is the solution of

        De fi nition 3.1Given a real functionfde fi ned on a Banach spaceB,the fi rst directional semiderivative atxin the directionv(when it exists)is de fi ned as

        The second order bidirectional derivative atxin the directions(v,w)(when it exists)is de fi ned as

        According to adjoint equation of(2.1)and(2.2),we can rewrite expression(3.1)in another form.Therefore,we quote some remarks on the stochastic di ff erential control.

        According to the de fi nition of directional derivative,we have

        We de fi ne the Hamiltonian by

        where

        Moreover,(p(·),K(·))∈L2(0,T;Rn)×(L2(0,T;Rn))dand K=(K1,K2,···,Kd),

        The adjoint equation of(2.1)and(2.2)is {

        where Ψ(t)is de fi ned by

        and

        with

        where Φ(t)is de fi ned by

        and the following property holds

        For the above assumptions and discussions about Hamiltonian and the adjoint equation, see[12]and[4].

        Proposition 3.1According to adjoint equation(3.2),we can rewrite expression(3.1)in the following form:

        Proof.By It?o formula,

        Thus

        Similarly,the second order bidirectional derivative of payo fffunction is of the following form:

        where

        In particular,for all x0,u,v,and

        Namely,the second order bidirectional derivative of payo fffunction is independent of x0and(u,v).So we have the following lemma.

        Lemma 3.1The following statements are equivalent:

        (1)The mapu→C0(u,0):L2(0,T;Rm)→Ris convex;

        (2)For allu∈L2(0,T;Rm),C0(u,0)≥0;

        (4)For allvandx0,the mapu→Cx0(u,v):L2(0,T;Rm)→Ris convex.

        Corollary 3.1The following statements are equivalent:

        (1)The mapv→C0(0,v):L2(0,T;Rk)→Ris concave;

        (2)For allv∈L2(0,T;Rk),C0(0,v)≤0;

        (4)For alluandx0,the mapv→Cx0(u,v):L2(0,T;Rk)→Ris concave.

        Corollary 3.2The following statements are equivalent:

        (1)The map(u,v)→C0(u,v):L2(0,T;Rm)×L2(0,T;Rk)→Ris(u,v)-convexconcave.That is,for anyv∈L2(0,T;Rk),

        is convex,and for anyu∈L2(0,T;Rm),

        is concave;

        (2)The pair(0,0)is a saddle point ofC0(u,v):

        (3)For allx0,the map(u,v)→Cx0(u,v):L2(0,T;Rm)×L2(0,T;Rk)→Ris(u,v)-convex-concave.That is,for anyv∈L2(0,T;Rm),

        is convex,and for anyu∈L2(0,T;Rm),

        is concave.

        Theorem 3.1IfV(x0)?=?andU(x0)?=?,then the saddle point of payo ffC0(u,v)exists and it is(0,0).

        Thus

        From the de fi nition of directional derivative one has

        By(3.3),it follows that

        By Corollary 3.1 we have

        So

        Similarly,since U(x0)?=?,by Lemma 3.1,we have

        Hence

        which shows that the saddle point of the payo ffC0(u,v)exists and it is(0,0).The proof is completed.

        Now we show the payo ffCx0of the game when

        in(2.1).

        Theorem 3.2There exists a solution(x,p,K)of the adjoint system

        If

        then

        Proof.Byformula one has

        and

        Then

        4 Main Results

        In this section,we prove the existence of the saddle point of the system(2.1)-(2.3)if and only if the lower and upper values exist.

        De fi nition 4.1We de fi ne

        and

        The main result in this paper is the following theorem.

        Theorem 4.1Consider the stochastic di ff erential game(2.1)and(2.3).The following statements are equivalent:

        (1)There exists an open loop saddle point ofCx0(u,v);

        (2)The value of the game exists;

        (3)Both the lower value and the upper value of the game exist.

        The proof of Theorem 4.1 is discussed later.To prove it,some other theorems and discussions are needed.Firstly,we consider a part of Theorem 4.1:the open loop lower value of the game.

        Theorem 4.2The following statements are equivalent:

        (1)There existu?∈L2(0,T;Rm)andv?∈L2(0,T;Rk)such that

        (2)The open loop lower valuev?(x0)of the game exists;

        (3)There exists a solution(x,p,K)of the adjoint system(3.4)such thatV(x0),the solution pairs(u?,v?)is(3.5),and the open loop lower value are given by(3.6).

        Proof.To prove this theorem,we need four steps.

        (a)We show that if lower value exists,then for any v∈V(x0),one has

        where(p,K)∈A(v,x0).

        By the standard stochastic extremal principle(see[4]),(2.1)and(3.2),u?is an optimizer if

        Similarly to Theorem 3.2,we can get(4.3).

        (b)We show that if lower value exists,then the following statements hold:

        (i)

        where

        and

        (ii)For all v∈V(0),

        and we denote V(0)=B⊥in the sense of expectation;

        (iii)

        The di ff erential game(2.1)can be written as

        where

        and

        We denote by R?the adjoint operator of the operator R.Let

        Then(4.1)can be written as

        and(4.5)can be written as

        Given v∈V(x0),for all(p,K)∈A(v,x0)and∈A(0,0),we have

        So

        For all v∈V(0),we have

        Let

        We say that V(0)=B⊥in the sense of expectation.It is easy to prove that

        (c)We show that if the lower value exists,then there exist v∈V(x0)and(p?,K?)∈A(v,x0)such that

        where(x?,p?,K?)is the solution of(4.1).

        Since the lower value of the game exists,there exists a v0∈V(x0)such that for any w∈V(0),

        By(4.4),there exists(p?,K?)∈A(v0,x0)such thatand(x?,p?,K?) is the solution of(4.1).Therefore,(x?,p?,K?)is the solution of(3.4).By Theorem 3.2, the open loop lower value is given by(3.6).

        can achieve maximization at v?.

        By(4.8)and v?v?∈V(0)we have

        According to the de fi nition of directional derivative,we have

        Thus

        Now we go back to the proof of Theorem 4.2.

        It is obvious that(1)?(2).

        According to the above(a)–(c),we have(2)?(3).

        (3)?(1).By(d),

        So

        The proof is completed.

        Corresponding to the Theorem 4.2,we have

        Theorem 4.3The following statements are equivalent:

        (1)There existu?∈L2(0,T;Rm)andv?∈L2(0,T;Rk)such that

        (2)The open loop upper valuev+(x0)of the game exists;

        (3)There exists a solution(x,p,K)of the adjoint system(3.4)such thatthe solution pairs(u?,v?)is(3.5),and the open loop lower value is given by(3.6).

        Now we give the proof of Theorem 4.1.

        Proof of Theorem 4.1(1)?(2)?(3)are obvious.

        (3)?(1).By Theorems 4.2 and 4.3,there exists a solution(x,p,K)of the system (3.10).Therefore,the game has a saddle point.

        [1]Isaacs R.Di ff erential Games.New York:John Wiley and Sons,1965.

        [2]Pontryagin L S,Boltyanskii V G,Gamkrekidze R V,Mishchenko E F.The Mathematical Theory of Optimal Processes.New York:Interscience Publishers,1962.

        [3]Bellman R.Dynamic Programming.Princeton:Princeton Univ.Press,1957.

        [4]Bensoussan A.Nonlinear Filtering and Stochastic Control.Lecture Notes in Math.vol.972. Berlin-Heidelberg-New York:Springer,1982.

        [5]Bensoussan A,Friedman A.Nonzero-sum stochastic di ff erential games with stopping times and free boundary problems.Trans.Amer.Math.Soc.,1977,231(2),275–327.

        [6]Ho Y C,Bryson A E,Baron Jr S.Di ff erential games and optimal pursuit-evasion strategies, IEEE Trans.Automat.Control,1965,AC-10:385–389.

        [7]Case J H.Toward a theory of many player di ff erential games.SIAM J.Control Optim.,1969,7:179–197.

        [8]Starr A W,Ho Y C.Nonzero-sum di ff erential games.J.Optim.Theory Appl.,1969,3:184–206.

        [9]Zhang P.Some results on two-person zero-sum linear quadratic di ff erential games.SIAM J. Control Optim.,2005,43(6):2157–2165.

        [10]Delfour M C.Linear quadratic di ff erential games:saddle point and riccati di ff erential equation. SIAM J.Control Optim.,2007,46(2):750–774.

        [11]Mou L B,Yong J M.Two-person zero-sum linear quadratic stochastic di ff erential games by a Hilbert space method,J.Indian Manag.Optim.,2006,2:93–115.

        [12]Peng S.A general stochastic maximum principle for optimal control problems.SIAM J.Control Optim.,1990,28(4):966–979.

        tion:91A23

        A

        1674-5647(2014)01-0011-12

        Received date:Jan.4,2011.

        Foundation item:The Young Research Foundation(201201130)of Jilin Provincial Science&Technology Department,and Research Foundation(2011LG17)of Changchun University of Technology.

        E-mail address:0435lover@163.com(Wang J).

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