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Lower semicontinuity of approximate solution mappings for parametric generalized vector equilibrium problems

Abstract

In this paper, we obtain sufficient conditions for the lower semicontinuity of an approximate solution mapping for a parametric generalized vector equilibrium problem involving set-valued mappings. By using a scalarization method, we obtain the lower semicontinuity of an approximate solution mapping for such a problem without the assumptions of monotonicity and compactness.

1 Introduction

The vector equilibrium problem is a unified model of several problems, for example, the vector optimization problem, the vector variational inequality problem, the vector complementarity problem and the vector saddle point problem. In the literature, existence results for various types of vector equilibrium problems have been investigated intensively, e.g., see [14] and the references therein. The stability analysis of the solution mappings for VEP is an important topic in vector equilibrium theory. Recently, the semicontinuity, especially the lower semicontinuity, of solution mappings to parametric vector equilibrium problems has been studied in the literature, see [516]. In the mentioned results, the lower semicontinuity of solution mappings to parametric generalized strong vector equilibrium problems is established under the assumptions of monotonicity and compactness. Very recently, Han and Gong [17] studied the lower semicontinuity of solution mappings to parametric generalized strong vector equilibrium problems without the assumptions of monotonicity and compactness.

On the other hand, exact solutions of the problems may not exist in many practical problems because the data of the problems are not sufficiently ‘regular’. Moreover, these mathematical models are solved usually by numerical methods which produce approximations to the exact solutions. So it is impossible to obtain an exact solution of many practical problems. Naturally, investigating approximate solutions of parametric equilibrium problems is of interest in both practical applications and computations. Anh and Khanh [18] considered two kinds of approximate solution mappings to parametric generalized vector quasiequilibrium problems and established the sufficient conditions for their Hausdorff semicontinuity (or Berge semicontinuity). Among many approaches for dealing with the lower semicontinuity and continuity of solution mappings for parametric vector variational inequalities and parametric vector equilibrium problems, the scalarization method is of considerable interest. By using a scalarization method, Li and Li [19] discussed the Berge lower semicontinuity and Berge continuity of an approximate solution mapping for a parametric vector equilibrium problem.

Motivated by the work reported in [1719], in this paper we aim to establish efficient conditions for the lower semicontinuity of an approximate solution mapping for a parametric generalized vector equilibrium problem involving set-valued mappings. By using a scalarization method, we obtain the lower semicontinuity of an approximate solution mapping for such a problem without the assumptions of monotonicity and compactness.

2 Preliminaries

Throughout this paper, let X and Y be real Hausdorff topological vector spaces, and let Z be a real topological space. We also assume that C is a pointed closed convex cone in Y with its interior intC. Let Y be the topological dual space of Y. Let C :={ξ Y :ξ,y0,yC} be the dual cone of C, where ξ,y denotes the value of ξ at y. Since intC, the dual cone C of C has a weak compact base. Let eintC. Then B e :={ξ C :ξ,e=1} is a weak compact base of C .

Suppose that K is a nonempty subset of X and F:K×K 2 Y {} is a set-valued mapping. We consider the following generalized vector equilibrium problem (GVEP) of finding x 0 K such that

F( x 0 ,y)YintC,yK.
(2.1)

When the set K and the mapping F are perturbed by a parameter μ which varies over a set M of Z, we consider the following parametric generalized vector equilibrium problem (PGVEP) of finding x 0 K(μ) such that

F( x 0 ,y,μ)YintC,yK(μ),
(2.2)

where K:M 2 X {} is a set-valued mapping, F:B×B×MX×X×Z 2 Y {} is a set-valued mapping with K(M)= μ M K(μ)B. For each ε>0 and μM, the approximate solution set of (PGVEP) is defined by

S ˜ (ε,μ):= { x K ( μ ) : F ( x , y , μ ) + ε e Y int C , y K ( μ ) } ,

where eintC. For each ξ B e and (ε,μ) R + ×M, by S ˜ ξ (ε,μ) we denote the ξ-approximate solution set of (PGVEP), i.e.,

S ˜ ξ (ε,μ):= { x K ( μ ) : inf z F ( x , y , μ ) ξ ( z ) + ε 0 , y K ( μ ) } .

Definition 2.1 Let D be a nonempty convex subset of X. A set-valued mapping G:X 2 Y is said to be:

  1. (i)

    C-convex on D if, for any x 1 , x 2 D and for any t[0,1], we have

    tG( x 1 )+(1t)G( x 2 )G ( t x 1 + ( 1 t ) x 2 ) +C.
  2. (ii)

    C-concave on D if, for any x 1 , x 2 D and for any t[0,1], we have

    G ( t x 1 + ( 1 t ) x 2 ) tG( x 1 )+(1t)G( x 2 )+C.

Definition 2.2 [17]

Let M and M 1 be topological vector spaces. Let D be a nonempty subset of M. A set-valued mapping G:M 2 M 1 is said to be uniformly continuous on D if, for any neighborhood V of 0 M 1 , there exists a neighborhood U 0 of 0M such that G( x 1 )G( x 2 )+V for any x 1 , x 2 D with x 1 x 2 U 0 .

Definition 2.3 [20]

Let M and M 1 be topological vector spaces. A set-valued mapping G:M 2 M 1 is said to be:

  1. (i)

    Hausdorff upper semicontinuous (H-u.s.c.) at u 0 M if, for any neighborhood V of 0 M 1 , there exists a neighborhood U( u 0 ) of u 0 such that

    G(u)G( u 0 )+Vfor every uU( u 0 ).
  2. (ii)

    Lower semicontinuous (l.s.c.) at u 0 M if, for any xG( u 0 ) and any neighborhood V of x, there exists a neighborhood U( u 0 ) of u 0 such that

    G(u)Vfor every uU( u 0 ).

The following lemma plays an important role in the proof of the lower semicontinuity of the solution mapping S ˜ (,).

Lemma 2.4 [[21], Theorem 2]

The union Γ= i I Γ i of a family of l.s.c. set-valued mappings Γ i from a topological space X into a topological space Y is also an l.s.c. set-valued mapping from X into Y, where I is an index set.

3 Lower semicontinuity of the approximate solution mapping for (PGVEP)

In this section, we establish the lower semicontinuity of the approximate solution mapping for (PGVEP) at the considered point ( ε 0 , μ 0 ) R + ×M with ε 0 >0.

Firstly, using the same argument as in the proof given in [[22], Lemma 3.1], we can prove the following useful result.

Lemma 3.1 For each ε>0, μM, if for each xK(μ), F(x,K(μ),μ)+C is a convex set, then

S ˜ (ε,μ)= ξ C { 0 } S ˜ ξ (ε,μ)= ξ B e S ˜ ξ (ε,μ).

Proof For any x ξ C { 0 } S ˜ ξ (ε,μ), there exists ξ C {0} such that x S ˜ ξ (ε,μ). Thus, we can obtain that xK(μ) and inf z F ( x , y , μ ) ξ (z)+ε0, yK(μ). Then, for each yK(μ) and zF(x,y,μ), ξ (z)+ε0, which arrives at zintC. It then follows that, for each zF(x,y,μ),

F(x,y,μ)+εeYintC,yK(μ),

which gives that x S ˜ (ε,μ). Hence, ξ C { 0 } S ˜ ξ (ε,μ) S ˜ (ε,μ). Conversely, let x S ˜ (ε,μ) be arbitrary. Then xK(μ) and F(x,y,μ)+εeYintC, yK(μ). Thus, we have

F ( x , K ( μ ) , μ ) (intC)=,

and hence

( F ( x , K ( μ ) , μ ) + C ) (intC)=.

Because F(x,K(μ),μ)+C is a convex set, by the well-known Edidelheit separation theorem (see [23], Theorem 3.16), there exist a continuous linear functional ξ Y {0} and a real number γ such that

ξ( c ˆ )<γξ(z+c)

for all zF(x,K(μ),μ), cC and c ˆ intC. Since C is a cone, we have ξ( c ˆ )0 for all c ˆ intC. Thus, ξ( c ˆ )0 for all c ˆ C, that is, ξ C . Moreover, it follows from cC, c ˆ intC and the continuity of ξ that ξ(z)+ε0 for all zF(x,K(μ),μ). Thus, for all yK(μ), we have inf z F ( x , y , μ ) ξ(z)+ε0, i.e., x S ˜ ξ (ε,μ) ξ C { 0 } S ˜ ξ (ε,μ). □

Theorem 3.2 We assume that for any given ξ B e , there exists δ>0 such that the ξ-approximate solution set S ˜ ξ (,) exists in [ ε 0 ,δ)×N( μ 0 ), where N( μ 0 ) is a neighborhood of  μ 0 . Assume further that the following conditions are satisfied:

  1. (i)

    K( μ 0 ) is nonempty convex;

  2. (ii)

    K is H-u.s.c. at μ 0 and l.s.c. at μ 0 ;

  3. (iii)

    for any yK( μ 0 ), F(,y, μ 0 ) is C-concave on K( μ 0 );

  4. (iv)

    F(,,) is uniformly continuous on K(M)×K(M)×N( μ 0 ).

Then the ξ-approximate solution mapping S ˜ ξ :[ ε 0 ,δ)×N( μ 0 ) 2 X is l.s.c. at ( ε 0 , μ 0 ).

Proof Suppose to the contrary that S ˜ ξ (,) is not l.s.c. at ( ε 0 , μ 0 ), then there exist x 0 S ˜ ξ ( ε 0 , μ 0 ) and a neighborhood W 0 of 0 X X. For any neighborhoods J( ε 0 ) and U( μ 0 ) of ε 0 and μ 0 , respectively, there exist ε J( ε 0 )[ ε 0 ,δ) and μ U( μ 0 ) such that ( x 0 + W 0 ) S ˜ ξ ( ε , μ )=. In particular, there exist sequences { ε n } ε 0 and { μ n } μ 0 such that

( x 0 + W 0 ) S ˜ ξ ( ε n , μ n )=,nN.
(3.1)

For the above W 0 , there exists a neighborhood W 1 of 0 X X such that

W 1 + W 1 W 0 .
(3.2)

We define a ξ-set-valued mapping H ξ :[0,δ) 2 X by

H ξ (ε)= { x K ( μ 0 ) : inf z F ( x , y , μ 0 ) ξ ( z ) + ε + ε 0 0 , y K ( μ 0 ) } ,ε[0,δ).

Notice that H ξ (0)= S ˜ ξ ( ε 0 , μ 0 ). Next, we claim that H ξ is l.s.c. at 0. Suppose to the contrary that H ξ is not l.s.c. at 0, then there exist x ¯ H ξ (0) and a neighborhood O 0 of 0 X X. For any neighborhood U of 0, there exists εU such that ( x ¯ + O 0 ) H ξ (ε)=. In particular, there exists a nonnegative sequence { ε n }0 such that

( x ¯ + O 0 ) H ξ ( ε n ) =,nN.
(3.3)

Since H ξ (0), we choose x H ξ (0). Since ε n 0, there exists ε n 0 such that

ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x = x ¯ + ε n 0 ε 0 + ε n 0 ( x x ¯ ) x ¯ + O 0 .
(3.4)

We claim that ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x H ξ ( ε n 0 ). In fact, since x ¯ H ξ (0) and x H ξ (0), for any yK( μ 0 ), we have inf t F ( x ¯ , y , μ 0 ) ξ(t)+ ε 0 0 and inf k F ( x , y , μ 0 ) ξ(k)+ ε 0 0. Then, for any uF( x ¯ ,y, μ 0 ),

ε 0 ε 0 + ε n 0 ξ(u)+ ε 0 ε 0 + ε n 0 ε 0 0,
(3.5)

and for any vF( x ,y, μ 0 ),

ε n 0 ε 0 + ε n 0 ξ(v)+ ε n 0 ε 0 + ε n 0 ε 0 0.
(3.6)

By the C-concavity of F(,y, μ 0 ), we have that

F ( ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x , y , μ 0 ) ε 0 ε 0 + ε n 0 F( x ¯ ,y, μ 0 )+ ε n 0 ε 0 + ε n 0 F ( x , y , μ 0 ) +C.

It follows that, for any wF( ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x ,y, μ 0 ), there exist z ¯ F( x ¯ ,y, μ 0 ), z F( x ,y, μ 0 ) and c C such that w= ε 0 ε 0 + ε n 0 z ¯ + ε n 0 ε 0 + ε n 0 z + c . It follows from the linearity of ξ that ξ(w) ε 0 ε 0 + ε n 0 ξ( z ¯ ) ε n 0 ε 0 + ε n 0 ξ( z )=ξ( c )0, which gives that ξ(w) ε 0 ε 0 + ε n 0 ξ( z ¯ )+ ε n 0 ε 0 + ε n 0 ξ( z ). For all wF( ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x ,y, μ 0 ), by (3.5) and (3.6), we have

ξ(w) ε 0 ε 0 + ε n 0 ε 0 ε n 0 ε 0 + ε n 0 ε 0 = ε 0 ε 0 + ε n 0 ( ε n 0 + ε 0 ) ( ε n 0 + ε 0 ) .

This implies that inf z F ( ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x , y , μ 0 ) ξ(z)+ ε n 0 + ε 0 0, that is, ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x H ξ ( ε n 0 ). By (3.4), we get that ε 0 ε 0 + ε n 0 x ¯ + ε n 0 ε 0 + ε n 0 x ( x ¯ + O 0 ) H ξ ( ε n 0 ), which contradicts (3.3). Therefore, H ξ is l.s.c. at 0. Since H ξ is l.s.c. at 0, for above x 0 S ˜ ξ ( ε 0 , μ 0 )= H ξ (0) and for above W 1 , there exists a balanced neighborhood V 0 of 0 such that ( x 0 + W 1 ) H ξ (ε), ε V 0 . In particular, from { ε n } ε 0 , there exits N 0 N such that ( x 0 + W 1 ) H ξ ( ε N 0 ε 0 ). Let x ( x 0 + W 1 ) H ξ ( ε N 0 ε 0 ).

For any ε ¯ >0, since eintC, there exists δ 0 >0 such that

δ 0 B Y + ε ¯ eC.
(3.7)

Since F(,,) is uniformly continuous on K(M)×K(M)×N( μ 0 ), for above δ 0 B Y , there exists a neighborhood V 1 of 0B, a neighborhood U 1 of 0B and a neighborhood N 1 of 0M, for any ( x 1 , y 1 , μ 1 ),( x 2 , y 2 , μ 2 )K(M)×K(M)×N( μ 0 ) with x 1 x 2 V 1 , y 1 y 2 U 1 and μ 1 μ 2 N 1 , we have

F( x 1 , y 1 , μ 1 ) δ 0 B Y +F( x 2 , y 2 , μ 2 ).
(3.8)

Since K is H-u.s.c. at μ 0 , for above U 1 , there exists a neighborhood U 1 ( μ 0 ) of μ 0 such that

K(μ)K( μ 0 )+ U 1 ,μ U 1 ( μ 0 ).
(3.9)

We see that x K( μ 0 ). Since K is l.s.c. at μ 0 , for V 1 W 1 , there exists a neighborhood U 2 ( μ 0 ) of μ 0 such that

( x + V 1 W 1 ) K(μ),μ U 2 ( μ 0 ).
(3.10)

It follows from μ n μ 0 that there exists a positive integer N 0 N 0 such that μ N 0 U 1 ( μ 0 ) U 2 ( μ 0 )U( μ 0 )( μ 0 + N 1 ). Noting that (3.9) and (3.10), we obtain

K( μ N 0 )K( μ 0 )+ U 1
(3.11)

and

( x + V 1 W 1 ) K( μ N 0 ).
(3.12)

By (3.12), we choose

x ( x + V 1 W 1 ) K( μ N 0 ).
(3.13)

Next, we prove that x S ˜ ξ ( ε N 0 , μ N 0 ). For any y K( μ N 0 ), by (3.11), there exists y 0 K( μ 0 ) such that y y 0 U 1 . It follows from (3.13) that x x V 1 . Noting that μ N 0 U( μ 0 )( μ 0 + N 1 ) and (3.8), we have

F ( x , y , μ N 0 ) δ 0 B Y +F ( x , y 0 , μ 0 ) .

By (3.7), we have

F ( x , y , μ N 0 ) C ε ¯ e+F ( x , y 0 , μ 0 ) .
(3.14)

Hence, for any yK( μ N 0 ) and z F( x , y , μ N 0 ), there exist c C and z F( x ,y, μ 0 ) such that

z = c ε ¯ e+ z .

It follows from the linearity of ξ that ξ( z )+ ε ¯ ξ( z ) for all ε ¯ >0. This leads to ξ( z )ξ( z ). Thus

ξ ( z ) + ε N 0 ξ ( z ) + ε N 0 =ξ ( z ) +( ε N 0 ε 0 )+ ε 0 0.

Hence x S ˜ ξ ( ε N 0 , μ N 0 ). Also, since x ( x 0 + W 1 ) and by (3.2) and (3.13), we have

x x + V 1 W 1 x 0 + W 1 + W 1 x 0 + W 0 .

This means that ( x 0 + W 0 ) S ˜ ξ ( ε N 0 , μ N 0 ), which contradicts (3.1). This completes the proof. □

Theorem 3.3 We assume that for any given ξ B e , there exists δ>0 such that the approximate solution set S ˜ ξ (,) exists in [ ε 0 ,δ)×N( μ 0 ). Suppose that conditions (i)-(iv) as in Theorem  3.2 are satisfied. Assume further that for each xK( μ 0 ), F(x,K( μ 0 ), μ 0 )+C is a convex set. Then the approximate solution mapping S ˜ :[ ε 0 ,δ)×N( μ 0 ) 2 X is l.s.c. at ( ε 0 , μ 0 ).

Proof Since F(x,K( μ 0 ), μ 0 )+C is a convex set for each xK( μ 0 ), by virtue of Lemma 3.1, it holds that S ˜ ( ε 0 , μ 0 )= ξ B e S ˜ ξ ( ε 0 , μ 0 ). It follows from Theorem 3.2 that for each ξ B e , S ˜ ξ (,) is l.s.c. at ( ε 0 , μ 0 ). Thus, in view of Lemma 2.4, we obtain that S ˜ (,) is l.s.c. at ( ε 0 , μ 0 ). □

The following example illustrates all of the assumptions in Theorem 3.3.

Example 3.4 Let Y= R 2 , C= R + 2 :={( x 1 , x 2 ) R 2 : x 1 0, x 2 0} and Z=X=R. Let B(0, 1 2 ) be the closed ball of radius 1/2 in R 2 . Let B=[2,2], M=[1,1] and the set-valued mapping F:B×B×M 2 Y be defined by

F(x,y,μ)= ( w ( x , y , μ ) , v ( x , y , μ ) ) +B(0,1/2),

where w(x,y,μ):= y 2 ( 2 μ 1)+x(yx+1)3y+2 and v(x,y,μ):= y 2 ( 2 μ 1) x 2 +2xy+3. Define a set-valued mapping K:M 2 X for all μM, by K(μ):=[2+μ,2+μ][2,2]. We choose e=(1,1)intC, ε 0 =2.5, μ 0 =0 and ξ=(1,0). We can see that B ( 1 , 1 ) ={( x 1 , x 2 ): x 1 + x 2 =1, x 1 , x 2 0} and 1 S ˜ ( 1 , 0 ) ( ε 0 ,0). Further, for any μ(1,1), there exists ε[2.5,4.5) such that 1 S ˜ ( 1 , 0 ) (ε,μ). Hence, S ˜ ( 1 , 0 ) (,) exists in [2.5,4.5)×[1,1]. It is easy to observe that for any yK(0), F(,y,0) is C-concave on K(0). Clearly, condition (ii) is true. It is obvious that K(M)=[2,2]. Let N( μ 0 )=[1,1], we can see that F(,,) is uniformly continuous on K(M)×K(M)×N( μ 0 ). Finally, we can check that for each x[2,2], F(x,[2,2],0)+C is a convex set. Applying Theorem 3.3, we obtain that S ˜ is l.s.c. at (2.5,0).

The following example illustrates that the concavity of F cannot be dropped.

Example 3.5 Let Y= R 2 , C= R + 2 and Z=X=R. Let B=[2,2], M=[1,1] and the set-valued mapping F:B×B×M 2 Y be defined by

F(x,y,μ)= [ μ x ( x y ) 0.5 , 2 ] × { x ( x y ) 0.5 } .

Define a set-valued mapping K:M 2 X for all μM, by K(μ):=[0,1]. We choose e=(1,1)intC, ε 0 =0.5, μ 0 =0. Then, all the assumptions of Theorem 3.3 are satisfied except (iii). Indeed, taking y=1, x 1 =0, x 2 =1 and t=0.5, we have

( 2.5 , 0.25 ) = ( 0.5 , 0.75 ) 0.5 ( 2 , 0.5 ) 0.5 ( 2 , 0.5 ) [ 0.5 , 2 ] × { 0.75 } 0.5 ( [ 0.5 , 2 ] × { 0.5 } ) 0.5 ( [ 0.5 , 2 ] × { 0.5 } ) F ( 0.5 ( 0 ) + 0.5 ( 1 ) , 1 , 0 ) 0.5 F ( 0 , 1 , 0 ) 0.5 F ( 1 , 1 , 0 ) = F ( 0.5 , 1 , 0 ) 0.5 F ( 0 , 1 , 0 ) 0.5 F ( 1 , 1 , 0 ) ,

but (2.5,0.25)C. The direct computation shows that

S ˜ ( ε 0 ,μ)={ { 0 , 1 } if  μ ( 0 , 1 ] , [ 0 , 1 ] if  μ = 0 , { 0 } if  μ [ 1 , 0 ) .
(3.15)

Clearly, we see that S ˜ (,) is even not l.s.c. at ( ε 0 , μ 0 ) since F(,y, μ 0 ) is not C-concave on K( μ 0 ).

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Acknowledgements

The authors were partially supported by the Thailand Research Fund, Grant No. PHD/0078/2554 and Grant No. RSA5780003. The authors would like to thank the referees for their remarks and suggestions, which helped to prove the paper.

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Wangkeeree, R., Boonman, P. & Preechasilp, P. Lower semicontinuity of approximate solution mappings for parametric generalized vector equilibrium problems. J Inequal Appl 2014, 421 (2014). https://doi.org/10.1186/1029-242X-2014-421

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Keywords

  • lower semicontinuity
  • approximate solution mapping
  • parametric generalized vector equilibrium problems
  • scalarization method