1 Introduction

Many problems in nonlinear analysis can be solved by showing the nonemptyness of the intersection of certain family of subsets of an underlying set. Each point of the intersection can be a fixed point, a coincidence point, an equilibrium point, a saddle point and an optimal point or others of the corresponding equilibrium problem under consideration.

The first remarkable result on the nonempty intersection was the celebrated Knaster-Kuratowski-Mazurkiewicz theorem (simply, KKM principle) in 1929 [1], which concerns with certain types of multimaps called the KKM maps later. The KKM theory first called by Park in [2] and [3] was the study of KKM maps and their applications. At the beginning, the theory was mainly devoted to study on convex subsets of topological vector spaces. Later, it has been extended to convex spaces by Lassonde [4], to C-spaces (or H-spaces) by Horvath [58] and to others.

In 1993, Park and Kim [9] introduced the concept of generalized convex spaces, and the KKM theory is extended to generalized convex (G-convex) in a sequence of papers by many authors (for details, see [2, 3, 9] and [10]) and the references cited therein. Note that, in the KKM theory, there have appeared a number of coincidence theorems with many significant applications.

In 1996, since Chang and Yen [11] introduced the class KKM(X, Y) of multimaps, it was developed by many authors. Recently, Lin et al. [12] studied the class KKM(X, Y) in topological vector space and proved some KKM type coincidence and fixed point theorems. In all KKM type theorems, the convexity plays important role and so many efforts have done to establish KKM type theorems without convexity structure. For example, see KKM type theorems in minimal generalized convex spaces [1315], G-convex spaces [9], FC-spaces [16] and topological ordered spaces [17].

Recently, Zafarani [18] and Park [19] have introduced a new concept of abstract convex space and certain broad classes KC and KO of multimaps (having the KKM property). With this new concept, the KKM type maps were used to obtain matching theorems, coincidence theorems, fixed point theorems and others.

In this paper, by using the concept of abstract convexity and minimal spaces, the classes m- KKM(X, Y) and ms-KKM(X, Y, Z) as a generalization of KKM(X, Y) and s-KKM(X, Y, Z) are introduced. Some generalized KKM and s-KKM type theorems for minimal transfer closed valued multimaps are established. As applications, some new coincidence theorems and a new version of Ky Fan's minimax theorem are obtained.

2 Preliminaries

A multimap F : XY is a function from a set X into the power set of Y. For any AX, set F(A) = ⋃xAF(x). Define the graph of F as G F = {(x, y) ∈ X × Y : yF(x)}. Furthermore, for any xX, define Fc (x) = {yY : yF(x)} and, for any yY, F-(y) = {xX : yF(x)} and F*(y) = {xX : yF(x)}. Note that F*(y) = X\F-(y) for any yY. For any set D, put ⟨D⟩ as the family of all nonempty finite subsets of D.

Proposition 2.1. [20]Suppose F, G : XY are two multimaps. Then we have the following.

  1. (1)

    for each xX, F(x) ⊆ G(x) if and only if G*(y) ⊆ F*(y) for each yY,

  2. (2)

    yF(x) if and only if xF*(y),

  3. (3)

    for each xX, (F*)*(x) = F(x),

  4. (4)

    for each xX,F ( x ) if and only if y Y F * ( y ) =,

  5. (5)

    for each yY, (Fc )*(y) = F -(y),

  6. (6)

    for each yY, (F -) c (y) = F*(y).

A family MP ( X ) is said to be a minimal structure on , X if , X M . In this case, ( X , M ) is called a minimal space. For example, let (X, τ) be a topological space, then τ, SO(X), PO(X), αO(X) and βO(X) are minimal structures on X[21]. In a minimal space ( X , M ) , AP ( X ) is called an m-open set if AM and also BP ( X ) is called an m-closed set if B c M. Set m  - Int ( A ) = { U : U A , U M } and m  - Cl ( A ) = { B : A B , B c M } . Notice that, for any set AX, m-Cl(A) (resp., m-Int(A)) is not necessarily m-closed (resp., m-open).

It is not hard to see that there are many minimal spaces which are not topological space. Furthermore, in the following example, it is shown that there are some linear minimal spaces which are not topological vector space. Moreover in [13], there is a minimal G-convex space which is not G-convex space (see [9] and [13]).

Definition 2.2[22]. Let ( X , M ) and ( Y , N ) be two minimal spaces. A function f: ( X , M ) ( Y , N ) is called minimal continuous (briefly m-continuous) if f - 1 ( U ) M for any UN.

Example 2.3[13]. Consider the real field ℝ. Clearly M= { ( a , b ) : a , b { ± } } is a minimal structure on ℝ. We claim that M is a linear minimal structure on ℝ. For this, we must prove that, two operations + and · are m-continuous. Suppose (x0, y0) ∈ + -1(a, b) and so x0 + y0 ∈ (a, b). Put ε = min{x0 + y0 - a, b - (x0 + y0)} and so x 0 ( x 0 - ε 2 , x 0 + ε 2 ) and y 0 ( y 0 - ε 2 , y 0 + ε 2 ) . Hence,

x 0 + y 0 x 0 - ε 2 , x 0 + ε 2 + y 0 - ε 2 , y 0 + ε 2 ( a , b ) ;

which implies that + -1(a, b) is m-open in the minimal product space ℝ × ℝ; that is + is m-continuous. Also, suppose (α0, x0) ∈ ·-1(a, b). Since α0x0 ∈ (a, b) and lims,t→0(α0 - s)(x0 - t) = α0x0, so one can find some 0 < δ for which |α0 - s| < δ and |x0 - t| < δ imply that a < (α0 - s)(x0 - t) < b. Therefore, (α0, x0) ∈ (α0 - δ, α0 + δ) · (x0 - δ, x0 + δ) ⊆ (a, b); i.e., ·-1(a, b) is m-open in the minimal product space ℝ × ℝ, which implies that the operation · is m-continuous.

For the main results in this paper, we recall some basic definitions and results. More details can be found in [13, 15, 2124] and [22] and references therein.

Proposition 2.4. [21]For any two sets A and B,

  1. (1)

    m-Int(A) ⊆ A and m-Int(A) = A if A is an m-open set.

  2. (2)

    Am-Cl(A) and A = m-Cl(A) if A is an m-closed set.

  3. (3)

    m-Int(A) ⊆ m-Int(B) and m-Cl(A) ⊆ m-Cl(B) if AB.

  4. (4)

    m-Int(m-Int(A)) = m-Int(A) and m-Cl(m-Cl(B)) = m-Cl(B),

  5. (5)

    (m-Cl(A)) c = m-Int(Ac ) and (m-Int(A)) c = m-Cl(Ac ).

Definition 2.5[14]. Let X be a nonempty set and Y be a minimal space. A multimap F : XY is said to be

  1. (1)

    minimal transfer open valued if, for each xX and yF(x), there exists x 0X such that ym-Int(F(x 0)).

  2. (2)

    minimal transfer closed valued if, for any xX and yF(x), there exists x 0X such that ym-Cl(F(x 0)).

Theorem 2.6. Suppose that X is a nonempty set and Y is a minimal space. Then the following are equivalent.

  1. (1)

    The multimap F : XY is minimal transfer closed valued,

  2. (2)

    xX F(x) = ⋂xX m-Cl(F(x)),

  3. (3)

    xX Fc (x) = ⋃xX m-Int(Fc (x)),

  4. (4)

    xX Fc (x) = ⋃xX(m-Cl(F(x))) c ,

  5. (5)

    Fc is minimal transfer open valued.

Proof. See theorems 2.1, 2.2 and 2.3 in [14].

Definition 2.7[22]. For a minimal space ( X , M ) ,

  1. (1)

    a family A= { A j : j J } of m-open sets in X is called an m-open cover of K if K ⊆ ⋃jJ A j . Any subfamily of A which is also an m-open cover of K is called a subcover of A for K,

  2. (2)

    a subset K of X is m-compact whenever given any m-open cover of K has a finite subcover.

Definition 2.8. Suppose that X is a nonempty set and Y is a minimal space. A multimap T : XY is called m-compact if m-Cl(T(X)) is an m-compact subset of Y.

Lemma 2.9. [15]Suppose that ( X , M ) is an m-compact minimal space and {A i : iI} is a family of subsets of X. If {m- Cl(A i ): iI} has the finite intersection property, then i I m - Cl ( A i ) . .

3 Coincidence theorems in abstract convex spaces

Now, we give some definitions for the results in this section as follows.

Definition 3.1[19]. An abstract convex space (X, D, Γ) consists of two nonempty sets X, D and a multimap Γ: ⟨D⟩ ⊸ X. In case to emphasize XD, (X, D, Γ) will be denoted by (XD, Γ); and if X = D, then (XX; Γ) by (X, Γ). If DX and EX, then E is called abstract convex if Γ(A) ⊆ E for each A ∈ ⟨DE⟩. Obviously, for any BD, we can define CoΓ(B) = ⋃{Γ A | A ∈ ⟨B⟩}.

Motivated by the results of Chang et al. [23], we introduce a new definition about the family of multimaps with the ms-KKMC (ms-KKMO) property in minimal abstract convex space as follows.

Definition 3.2. Let Y be a nonempty set, Z be a minimal space, s : YD be a function and (X, D, Γ) be an abstract convex space. Let T : XZ and F : YZ be two multimaps. we say that F is generalized s-KKM with respect to T if

T ( Γ ( s ( A ) ) ) F ( A ) for any A Y .

The multimap T has ms- KKMC (resp. ms- KKMO) property if the following conditions.

  1. (1)

    for any yD, F(y) = m-Cl(A y ) (resp. F(y) = m-Int(A y )) for some A y Z,

  2. (2)

    F is generalized s-KKM with respect to T,

imply that the family {F(y): yD} has the finite intersection property.

Set

  1. (1)

    ms-KKMC(X, Y, Z) = {T : XZ : T has ms-KKMC property},

  2. (2)

    ms-KKMO(X, Y, Z) = {T : XZ : T has ms-KKMO property}.

Remark 3.3. Note that

  1. (1)

    If D = Y and the function s : YD is the identity map Id D , the class ms-KKMC(X, Y, Z) reduces to the class m-KKMC(X, Z) introduced and investigated in [1315].

  2. (2)

    The classes m-KKMC(X, Y) and ms-KKMC(X, Y, Z) generalize the classes KKM(X, Y) and s-KKM(X, Y, Z) in G-convex spaces and their subclasses (see [24]), in topological space.

Proposition 3.4. Suppose that X is a minimal space and A, BX such that B is m-compact and m- Cl(A) ⊆ B. Then m- Cl(A) is m-compact.

Proof. Suppose that m-Cl(A) ⊆ ⋃iIG i for any m-open cover {G i : iI}. Since Bm-Cl(A) ∪ (m-Cl(A)) c , we have Bm-Cl(A) ∪ m-Int(Ac ). From the definition of "m-Int", it follows that

B i I G i m  -  Int A c i I G i j J U j : U j M , U j A c .

Thus the compactness of B implies that

B i = 1 n G i j = 1 m { U j : U j M , U j A c } i = 1 n G i m  -  Int ( A c ) .

Therefore, we have B i = 1 n G i ( m  -  Cl ( A ) ) c , which implies that m- Cl ( A ) =Bm  -  Cl ( A) i = 1 n G i . Hence m-Cl(A) is m-compact. This completes the proof.

Theorem 3.5. Let (X, D, Γ) be an abstract convex space and Z be a minimal space. Suppose that s : YD is a mapping, Tms- KKMC(X, Y, Z) is an m-compact mapping such that F : YZ is generalized s- KKM with respect to T. Then we have

m  -  Cl ( T ( C o Γ ( s ( Y ) ) ) ) y Y m  -  Cl ( F ( y ) ) .

Proof. Consider the multimap G : Ym-Cl(T(CoΓ(s(Y)))) defined by

G ( y ) = m  -  Cl ( m  -  Cl ( T ( C o Γ ( s ( Y ) ) ) ) F ( y ) )

for any yY. It is easy to check that G is well defined. Since F is generalized s-KKM with respect to T, for any A ∈ ⟨Y⟩, T(Γ(s(A))) ⊆ F(A). Also, T(Γ(s(A))) ⊆ m-Cl(T(CoΓ(s(Y)))) for each A ∈ ⟨Y⟩. Hence T(Γ(s(A))) ⊆ m-Cl(m-Cl(T(CoΓ(s(Y)))) ∩ F(A)) = G(A) and so G is generalized s-KKM with respect to T. Since Tms-KKMC(X, Y, Z), it follows that {G(y) = m-Cl(m-Cl(T(CoΓ(s(Y)))) ∩ F(Y)): yY} has the finite intersection property in m-Cl(T(CoΓ(s(Y)))), which is an m-compact subset of Z by Proposition 3.4. Thus it follows from Lemma 2.9 that y Y m  - Cl ( m  - Cl ( T ( C o Γ ( s ( Y ) ) ) F ( y ) ) , which, from Proposition 2.4, we have y Y m  - Cl ( T ( C o Γ ( s ( Y ) ) ) m  - Cl( F ( y ) ) . Therefore, we have m  - Cl ( T ( C o Γ ( s ( Y ) ) ) y Y m  - Cl( F ( y ) ) . This completes the proof.

Theorem 3.6. Let (X, D, Γ) be an abstract convex space and Z be a minimal space. Suppose that s : YD is a mapping and Tms- KKMC(X, Y, Z) is m-compact. If a multimap F : YZ satisfies the following conditions.

  1. (1)

    F is minimal transfer closed valued,

  2. (2)

    F is generalized s-KKM with respect to T.

Then m  - Cl ( T ( C o Γ ( s ( Y ) ) ) y Y F ( y ) .

Proof. It follows from Theorem 3.5 and the part (2) of Theorem 2.6.

Corollary 3.7. Let (X, D, Γ) is an abstract convex space and Y is a minimal space. Let Tm- KKMC(X, Y) be m-compact. If a multimap F : DY satisfies the following conditions.

  1. (1)

    F is minimal transfer closed valued,

  2. (2)

    F is generalized KKM with respect to T.

Thenm  - Cl ( T ( C o Γ ( D ) ) ) x D F ( x ) .

Proof. All conditions of Theorem 3.6 are satisfied for D instead of Y and for Y instead of Z, where s is the identity map on D. Therefore, one can deduce that m  - Cl ( T ( C o Γ ( D ) ) ) x D F ( x ) .

Remark 3.8. Note that

  1. (1)

    Theorem 3.5 generalizes Theorem 4.3 in [23] in convex space, theorems 13 and 14 in [25] in L-convex spaces, theorems 3.2 and 3.3 in [26], Theorem 3.2 in [27] in FC-spaces, Theorem 1 in [28] in G-convex spaces and Corollary 1.1 in [29] in abstract convex minimal spaces.

  2. (2)

    Theorem 3.6 generalizes Theorem 3.1 in [26] and [27], without the assumption "compactly" in FC-spaces and Corollary 3.1 [29] in abstract convex minimal spaces.

  3. (3)

    Corollary 3.7 generalizes Theorem 2.2 in [12] from convex spaces to abstract convex spaces and also from the class KKM(X, Y) in convex spaces to the class m-KKM(X, Y) in abstract convex spaces.

  4. (4)

    The compactness condition of multimap T in Theorem 3.5 and Theorem 3.6 can be replaced by the coercivity condition (see [24]) in minimal spaces. However, we use this condition in the following theorems.

Theorem 3.9. Let (X, D, Γ) be an abstract convex space and Y be a minimal space. Let Tm- KKMC(X, Y) be m-compact. If F, G, H : DY are three multimaps satisfying the following conditions.

  1. (1)

    F is minimal transfer closed valued,

  2. (2)

    F*(y) ⊆ G*(y) and H*(y) ⊆ T*(y) for any yY,

  3. (3)

    CoΓ(G*(y)) ⊆ H*(y) for any yY.

Thenm  - Cl ( T ( C o Γ ( D ) ) ) x D F ( x ) .

Proof. We claim that G is generalized KKM with respect to T. To see this, suppose that there exists A ∈ ⟨D⟩ such that T(Γ(A)) ⊈ ⋃xAG(x). Then there exist x ̄ Γ ( A ) and ȳT ( x ̄ ) such that ȳG ( x ) for all xA. Hence x G * ( ȳ ) for all xA and so A G * ( ȳ ) . From (3), we have x ̄ Γ ( A ) H * ( ȳ ) and then the condition (2) implies that x ̄ T * ( ȳ ) . Thus it follows that ȳT ( x ̄ ) , which is a contradiction. Therefore, G is generalized KKM with respect to T. Since G*(y) ⊆ F*(y) for all yY, from Proposition 2.1, F is generalized KKM with respect to T. Now, all conditions of Corollary 3.7 hold and hence m  - Cl ( T ( C o Γ ( D ) ) ) x D F ( x ) . This completes the proof.

Remark 3.10. According to the Remark 3.8, Theorem 3.9 generalizes the main theorem in [30] in FC-spaces, also it generalizes Theorem 2 in [31].

Theorem 3.11. Let (X, D, Γ) be an abstract convex space and Y be a minimal space. Let Tm- KKMC(X, Y) be m-compact. If F : DY and H, P : YD are three multimaps satisfying the following conditions.

  1. (1)

    F is minimal transfer closed valued and DF -(y) for all yY,

  2. (2)

    H*(x) ⊆ F(x) for any xD,

  3. (3)

    CoΓ(H(y)) ⊆ P(y) for all yY.

Then there exists ( x ̄ , ȳ ) D×Ysuch thatȳT ( x ̄ ) and x ̄ P ( ȳ ) .

Proof. By the condition (2), it is easy to see that F*(y) ⊆ (H*)*(y) for all yY. Now, suppose that the conclusion does not hold, that is T ( x ) P - ( x ) = for all xD. Choose xP(y). Then yP-(x) and so yT(x), which gives xT*(y). Thus P(y) ⊂ T*(y) for all yY. The condition (3) implies that CoΓ((H*)*(y)) ⊆ (P*)*(y) for all yY. Hence all conditions of Theorem 3.9 are satisfied for P* and H*, which implies that m- Cl ( T ( Γ ( s ( Y ) ) ) ) x D F ( x ) and so there exists y0m-Cl(T(CoΓ(D))) such that y0F(x) for all xD. This means that D = F-(y0), which contradicts the condition (1). Therefore, there exists ( x ̄ , ȳ ) D×Y such that ȳT ( x ̄ ) and x ̄ P ( ȳ ) . This completes the proof.

Theorem 3.12. Let (X, D, Γ) be an abstract convex space and Y be a minimal space. Let Tm- KKMC(X, Y) be m-compact. If H, P, Q : YD are three multimaps satisfying the following conditions.

  1. (1)

    Q - is minimal transfer open valued and Q ( y ) for all yY,

  2. (2)

    H*(x) ⊆ Q*(x) for all xD,

  3. (3)

    CoΓ(H(y)) ⊆ P(y) for all yY.

Then there exists ( x ̄ , ȳ ) D×Ysuch thatȳT ( x ̄ ) and x ̄ P ( ȳ ) .

Proof. The condition (1) and Theorem 2.6 imply that Q* is minimal transfer closed valued. By the definition of Q*, (Q*)- = D\Q(y) and so D ≠ (Q*)-(y) for all yY. By applying Theorem 3.11 with Q* instead of F, there exists ( x ̄ , ȳ ) D×Y such that ȳT ( x ̄ ) and x ̄ P ( ȳ ) . This completes the proof.

Remark 3.13. Theorem 3.12 generalizes Theorem 2.5 in [12] from convex spaces to abstract convex spaces and from the class KKM(X, Y) in convex spaces to the class m-KKM(X, Y) in abstract convex spaces.

Lemma 3.14. Let (XD, Γ) be an abstract convex space and Y be a minimal space. Let Tm- KKMC(X, Y). If M is an abstract convex subset of X, then T/ M m- KKMC(M, Y).

Proof. Set D' = DM and let Γ': ⟨D'⟩ ⊸ M be the multimap defined by Γ'(A) = Γ(A) for any A ∈ ⟨D'⟩. Consider a multimap F : MY defined by F(x) = m-Cl(A x ) for all xM. Then F is generalized KKM with respect to T| M . Now, define the multimap G : DY by

G ( x ) = F ( x ) , i f x D , Y , i f x D \ D .

It is not hard to check that G is generalized KKM with respect to T. Since Tm-KKMC(X, Y), the family {G(x): xD} has the finite intersection property and so {F(x): xD'} has the finite intersection property. Then T| M m-KKMC(M, Y). This completes the proof.

In the results on the KKM and coincidence type theorems mentioned above, it is assumed that Tms-KKMC(X, Y, Z) or Tm-KKMC(X, Y) is an m-compact multimap, but, when it is not m-compact, we can use the coercivity conditions instead of the m-compactness condition of T as follows.

Proposition 3.15[14]. Let X and Y be two minimal spaces. Then the following statements for a multimap F : XY are equivalent.

  1. (1)

    F- : YX is minimal transfer open valued and F ( x ) for all xX.

  2. (2)

    X = ⋃{m-Int (F -(y)): yY}.

Definition 3.16[32]. Let ( X , M ) be a minimal space and Y be a nonempty subset of X. The family M | Y = { U Y : U M } is called induced minimal structure by M on Y. ( Y , M | Y ) is called minimal subspace of ( X , M ) . Also, for any subset A of X we define m  -  Int Y ( A ) = { V : V M | Y and VA}.

Lemma 3.17. Let ( X , M ) be a minimal space and A be a nonempty subset of X. Then we have

m  -  Int ( A ) B m  -  Int B ( A B ) .

Proof. From Definition 3.16, we have

m  - Int A B = U : U M  and  U A B = { U B : U M  and  U A } { G : G M | B  and  G A B } = m  -  Int B ( A B ) .

Theorem 3.18. Let (XD, Γ) be an abstract convex space, Y be a minimal space and Tm- KKMC(X, Y). If H, P, Q: YD are three multimaps satisfying the following conditions.

  1. (1)

    Q - is minimal transfer open valued and Q ( y ) for all yY,

  2. (2)

    H*(x) ⊆ Q* (x) for all xD,

  3. (3)

    CoΓ (H(y)) ⊆ P(y) for all yY,

  4. (4)

    for each m-compact subset CD, m-Cl(T(C)) is m-compact in Y,

  5. (5)

    there is an m-compact subset KY such that, for any A ∈ ⟨D⟩, there is an m-compact abstract convex subset L A D containing A such that T ( L A ) \K x L A m  -  Int ( Q - ( x ) ) .

Then there exists ( x ̄ , ȳ ) D×Y such that ȳT ( x ̄ ) and x ̄ P ( ȳ ) .

Proof. The condition (1) and Proposition 3.15 imply that Y = ⋃xDm-Int(Q- (x)) and so we have

K = x D m  - Int ( Q - ( x ) ) K x D m  - Int ( Q - ( x ) ) = x D j J x U x , j ,

where U x, j is m-open and U x,j Q-(x). Since K is an m-compact subset of Y, K i = 1 n U x i , j i i = 1 n m  -  Int ( Q - ( x i ) ) - for j i J x i .

On the other hand, by the condition (5), there exists an m-compact abstract convex subset L A of D containing A such that T ( L A ) \K x L A m  -  Int ( Q - ( x ) ) . Thus we have

T ( L A ) x L A m  - Int ( Q - ( x ) ) K = x L A m  - Int ( Q - ( x ) ) i = 1 n m - Int ( Q - ( x i ) ) ,

where x i AL A . Then T ( L A ) x L A m  -  Int ( Q - ( x ) ) . By Lemma 3.17, it follows that

T ( L A ) = x L A m  - Int ( Q - ( x ) ) T ( L A ) x L A m  - In t T ( L A ) ( Q - ( x ) T ( L A ) ) T ( L A ) .

From the condition (4), it follows that m-Cl(T(L A )) is m-compact subset of Y. Moreover, since L A is an abstract convex subset of X, applying Lemma 3.14, T | L A m  - KKMC ( L A , Y ) .

Now, consider three multimaps P ̄ , Q ̄ , H ̄ :T ( L A ) L A defined by P ̄ ( y ) =P ( y ) L A , Q ̄ ( y ) =Q ( y ) L A and H ̄ ( y ) =H ( y ) L A for all yT(L A ). Then T | L A , P ̄ , Q ̄ and H ̄ satisfy in all conditions of Theorem 3.12 (notice Proposition 3.15) and hence there exist ȳT ( L A ) Y and x ̄ L A D such that ȳT | L A ( x ̄ ) =T ( x ̄ ) and x ̄ P ̄ ( ȳ ) P ( ȳ ) . This completes the proof.

Corollary 3.19. Let (XD, Γ) be an abstract convex space, Y be a minimal space and Tm- KKMC(X, Y). If P, Q : YD are two multimaps satisfying the following conditions.

  1. (1)

    Q- is minimal transfer open valued and Q ( y ) for all yY,

  2. (2)

    CoΓ(Q(y)) ⊆ P(y) for all yY,

  3. (3)

    for each m-compact subset CD, m-Cl(T(C)) is m-compact in Y,

  4. (4)

    there exists an m-compact subset KY such that, for any A ∈ ⟨D⟩, there exists an m-compact abstract convex subset L A D containing A satisfying T ( L A ) \K x L A m  -  Int ( Q - ( x ) ) .

Then there exists ( x ̄ , ȳ ) D×Ysuch thatȳT ( x ̄ ) and x ̄ P ( ȳ ) .

Proof. Letting H* = Q* in Theorem 3.18, the conclusion follows.

Remark 3.20. Theorem 3.18 and Corollary 3.19 generalize Theorem 2.6 in [12] from the class of convex spaces to the class of abstract convex spaces under the weaker assumptions.

4 Applications to minimax inequalities

Fan's minimax inequality [33] has played very important roles in the study of modern nonlinear analysis and especially in mathematical economics. Moreover, some general minimax theorems and extensions of these inequalities have been obtained for the functions with KKM property under the various weaker conditions in many spaces with the convex structure (see [10] and [3439]).

In this section, we prove some results to obtain Ky Fan's type minimax theorem as follows.

Theorem 4.1. Let (XD, Γ) be an abstract convex space, Y be a minimal space and T : X ⊸ Y is a multimap and Tm- KKMC(X, Y). If G, H : D ⊸ Y are two multimaps satisfying the following conditions.

  1. (1)

    G*(y) ⊆ T*(y) for all yY,

  2. (2)

    CoΓ (H*(y)) ⊆ G* (y) for all yY,

  3. (3)

    H is minimal transfer closed valued,

  4. (4)

    for any m-compact subset CD, m-Cl(T(C)) is m-compact in Y,

  5. (5)

    there exists an m-compact subset KY such that, for any A ∈ ⟨D⟩, there exists an m-compact abstract convex subset L A D containing A satisfying T ( L A ) x L A m  -  Cl ( H ( x ) ) K .

Then x D H ( x ) .

Proof. Suppose that x D H ( x ) =. For all yY, there exists xD such that yH(x) and so xH*(y). Thus H* is nonempty valued. Since H is minimal transfer closed valued, from Lemma 2.6 and Proposition 2.1, it follows that Hc = (H*)- is minimal transfer open valued and m-Cl(H(x)) = (m-Int((H*)-(x))) c for all xD. Hence, from the condition (5), there exists an m-compact subset KY such that, for any A ∈ ⟨D⟩, there exists an m-compact abstract convex subset L A D containing A such that T ( L A ) \K x L A m  - Int ( H * ) - ( x ) ) . Applying Corollary 3.19 for (G*, H*) instead of (P, Q), there exists ( x ̄ , ȳ ) D×Y such that ȳT ( x ̄ ) and x ̄ G * ( ȳ ) . Thus x ̄ T * ( ȳ ) and x ̄ G * ( ȳ ) , which contradicts the condition (1). Therefore, x D H ( x ) .

Corollary 4.2. Let (XD, Γ) be an abstract convex space, Y be a minimal space and Tm- KKMC(X, Y). If H : DY is a multimap satisfying the following conditions.

  1. (1)

    CoΓ (H*(y)) ⊆ T* (y) for all yY,

  2. (2)

    H is minimal transfer closed valued,

  3. (3)

    for any m-compact subset CD, m-Cl(T(C)) is m-compact in Y,

  4. (4)

    there exists an m-compact subset KY such that, for any A ∈ ⟨D⟩, there exists an m-compact abstract convex subset L A D containing A satisfying T ( L A ) x L A m  -  Cl ( H ( x ) )K.

Then x D H ( x ) .

Proof. Letting G = T| D in Theorem 4.1, we can get the conclusion.

Lemma 4.3. Let (X, Γ) is an abstract convex space and Y be a nonempty set. If T, S : XY are two multimaps, then the following statements are equivalent.

  1. (1)

    CoΓ (T*(y)) ⊆ S* (y) for each yY.

  2. (2)

    For any A ∈ ⟨X⟩, S(Γ(A)) ⊆ T(A).

Proof. (1) ⇒ (2): Let A ∈ ⟨X⟩ and yS(Γ(A)). Then there exists x ∈ Γ(A) such that yS(x). Hence, from the definition of S - ,xΓ ( A ) S - ( y ) . So, it follows from Proposition 2.1 that Γ(A) ⊈ S*(y). From (1), it follows that AT*(y) and so A T - ( y ) . Choose zAT-(y). Then yT(z) ⊆ T(A). This means that S(Γ(A)) ⊆ T(A) and so (1) ⇒ (2) is proved.

  1. (2)

    ⇒ (1): Choose yY such that A T * ( y ) Γ ( A ) = C o Γ ( T * ( y ) ) S * ( y ) . Then there exist A ∈ ⟨T*(y)⟩ and x ∈ Γ(A) such that xS*(y), which implies that yS(x) ⊆ S(Γ(A)). On the other hand, A ∈ ⟨T*(y)⟩ implies that AD \ T -(y) and so A T - ( y ) =. Thus it follows that y ∉ T (A) and hence S(Γ(A)) ⊈ T(A), which this contradicts (2).

Remark 4.4. Note that

  1. (1)

    If X = D, then, by Lemma 4.3, in the condition (2) of Theorem 4.1, we can put G(Γ(A)) ⊆ H(A) for all A ∈ ⟨D⟩ and so T(Γ(A)) ⊆ H(A) for all A ∈ ⟨D⟩ instead of the condition (1) in Corollary 4.2.

  2. (2)

    Theorem 4.1 and Corollary 4.2 are generalizations of Theorem 3.3 and Corollary 3.2 in [40] respectively.

Definition 4.5. Let (XD, Γ) be an abstract convex space, Y be a nonempty set and f be a real-valued bifunction defined on X × Y. Then f is said to be

  1. (1)

    quasi-abstract convex in the first variable if, for all yY and γ ∈ ℝ, the set (xX : f(x, y) < γ} is abstract convex.

  2. (2)

    quasi-abstract concave in the first variable if, for all yY and γ ∈ ℝ, the set (xX : f(x, y) > γ} is abstract convex.

Definition 4.6. Let X, Y be minimal spaces and f be a real-valued bifunction defined on X × Y. Then f is said to be

  1. (1)

    strongly path minimal transfer lower semi-continuous in the first variable (shortly, spmt l.s.c.) if, for any (x, y) ∈ X × Y and ε > 0, there exists an m-open set N(x) containing x in X and there exists y' ∈ Y such that f(x, y) < f(x', y') + ε for any x' ∈ N(x).

  2. (2)

    strongly path minimal transfer upper semi-continuous in the first variable (shortly, spmt u.s.c.) if the function - f is l.s.c. in the first variable.

Theorem 4.7. Let (XD, Γ) be an abstract convex space, Y be a minimal space and Tm- KKMC(X, Y). If f, g : D × Y → ℝ are two functions and γ= inf ( x , y ) G T f ( x , y ) satisfying the following conditions.

  1. (1)

    for any m-compact subset CD, m-Cl(T(C)) is m-compact in Y,

  2. (2)

    f(x, y) ≤ g(x, y) for all (x, y) ∈ D × Y,

  3. (3)

    f is quasi-abstract convex in the first variable,

  4. (4)

    g is spmt u.s.c in the second variable,

  5. (5)

    there exists an m-compact subset KY such that, for any A ∈ ⟨D⟩, there exists an m-compact abstract convex subset L A D containing A such that, for all ȳT ( L A ) \K, there exists xL A such that ȳm  - Int { y : g ( x , y ) < γ } . Then

    inf ( x , y ) G T f ( x , y ) sup y Y inf x D g ( x , y ) .

Proof. Consider the multimap H : DY defined by

H ( x ) = { y Y : g ( x , y ) γ }

for all xD. We claim that C o Γ ( H * ( ȳ ) ) T * ( ȳ ) for all yY. Suppose that this is not the case, so there exists ȳY such that C o Γ ( H * ( ȳ ) ) T * ( ȳ ) or C o Γ ( D \ H - ( ȳ ) ) X\ T - ( ȳ ) . Thus there exist A D \ H - ( ȳ ) and x ̄ Γ ( A ) such that x ̄ X\ T - ( ȳ ) . Then ȳT ( x ̄ ) , which implies that ( x ̄ , ȳ ) G T .

On the other hand, from AD\ H - ( ȳ ) , we have A H - ( ȳ ) =. It follows that ȳH ( A ) and hence g ( x , ȳ ) <γ, for all xA. By the condition (2), we have f ( x , ȳ ) <γ for all xA. The condition (3) implies that f ( x , ȳ ) <γ for all x ∈ Γ(A). Therefore, f ( x ̄ , ȳ ) <γ, which contradicts γ= inf ( x , y ) G T f ( x , y ) . Thus it follows that CoΓ (H*(y)) ⊆ T*(y) for all yY.

To prove that H is minimal transfer closed valued, suppose that (x, y) ∈ D × Y and yH(x) and hence g(x, y) < γ. By the condition (4), setting ε = γ - g(x, y), there exist x' ∈ D and an m-open set N(y) containing y such that

- g ( x , y ) < - g ( x , y ) + ε = - g ( x , y ) + γ - g ( x , y )

for all y' ∈ N(y). Thus g(x', y') < γ for all y' ∈ N(y) and so N ( y ) H ( x ) =. It follows that N(y) ⊆ (H(x')) c and so we have

yN ( y ) m  - Int ( ( H ( x ) ) c ) = ( m  - Cl ( H ( x ) ) ) c .

Therefore, ym-Cl(H(x')). From the condition (5), it follows that T ( L A ) \K x L A m  -  Int ( H c ( x ) ), which implies that T ( L A ) \K ( x L A m  - C l ( H c ( x ) ) or T ( L A ) x L A m  -  C ( H ( x ) )K. Thus the condition (5) is equivalent to the condition (4) in Corollary 4.2 for multimap H.

Now, applying Corollary 4.2, we have x D H ( x ) . There exists y' ∈ Y such that g(x, y') ≥ γ for all xD and so sup y Y inf x D g ( x , y ) γ. This implies that

inf ( x , y ) G T f ( x , y ) sup y Y inf x D g ( x , y ) .

Remark 4.8. Note that

  1. (1)

    The conditions (2) and (3) in Theorem 4.7 are satisfied if we assume that f is g-quasiconvex in the first variable, that is, for any yY and A ∈ ⟨D⟩, we have f ( x , y ) max x A g ( x , y ) for all x ∈ Γ(A).

  2. (2)

    Theorem 4.7 is a generalization of Theorem 1 in [41], Theorem 8 in [39] and Theorem 6.4 in [42].