Research article

Viscosity-type method for solving pseudomonotone equilibrium problems in a real Hilbert space with applications

  • The aim of this article is to introduce a new algorithm by integrating a viscosity-type method with the subgradient extragradient algorithm to solve the equilibrium problems involving pseudomonotone and Lipschitz-type continuous bifunction in a real Hilbert space. A strong convergence theorem is proved by the use of certain mild conditions on the bifunction as well as some restrictions on the iterative control parameters. Applications of the main results are also presented to address variational inequalities and fixed-point problems. The computational behaviour of the proposed algorithm on various test problems is described in comparison to other existing algorithms.

    Citation: Habib ur Rehman, Poom Kumam, Kanokwan Sitthithakerngkiet. Viscosity-type method for solving pseudomonotone equilibrium problems in a real Hilbert space with applications[J]. AIMS Mathematics, 2021, 6(2): 1538-1560. doi: 10.3934/math.2021093

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  • The aim of this article is to introduce a new algorithm by integrating a viscosity-type method with the subgradient extragradient algorithm to solve the equilibrium problems involving pseudomonotone and Lipschitz-type continuous bifunction in a real Hilbert space. A strong convergence theorem is proved by the use of certain mild conditions on the bifunction as well as some restrictions on the iterative control parameters. Applications of the main results are also presented to address variational inequalities and fixed-point problems. The computational behaviour of the proposed algorithm on various test problems is described in comparison to other existing algorithms.


    The classical Hermite-Hadamard (H-H) inequality, serving as a litmus test for convexity, formally establishes that if the function F:[1,2]R is a convex function satisfying the essential containment relationship between its midpoint value and integral mean. Specifically, the following inequalities are satisfied:

    F(1+22)12121F(σ)dσ12(F(1)+F(2)).

    Convexity inequalities have numerous applications in the study of different models from real-world applications [1,2]. Because the H-H inequality is of great significance in convex analysis, it is widely applied in various fields such as integral inequalities, information theory, and optimization theory. In recent years, it has been extended and generalized through various forms of convexity, such as log-convexity [3,4], harmonic convexity [5,6], h-convexity [7,8,9], convexity in q-calculus [10,11,12] and especially s-convexity [13]. Since 1994, s-convexity has been a significant development and widespread application and various generalizations and results regarding H-H inequalities related to s-convex mappings have been established in [14,15,16].

    From another perspective, interval analysis provides an effective numerical tool for solving uncertain and nonlinear problems. Since the publication of the first monograph in 1966 [17], it has evolved into a distinct branch of mathematics, with applications spanning data mining, machine learning, and various other fields. A key focus has been interval-valued (IV) function inequalities. Recently, based on interval calculus and generalized convexity, some authors like Ali et al. [18,19,20], Budak et al. [21,22], Costa et al. [23,24,25], Du et al. [26,27], Khan et al.[28,29], Sarikaya et al. [30,31,32], and Zhao et al. [33,34,35] established the interval versions of the Chebyshev's inequality, Jensen's inequality, and H-H inequality. Furthermore, with the successive introduction of the bilateral Riemann-Liouville (R-L) fractional integral operators (left-hand and right-hand variants) for IV functions, the results related to inequalities for IV functions are more extensive and profound [36,37]. Especially, in 2023, Budak et al. [38] introduced a novel generalized integral to demonstrate the generalized H-H-type inclusion of IV convex functions.

    Motivated by the above-mentioned literature, we have established some novel interval forms of H-H inequalities by using generalized fractional integral operators and combining them with IV s-convex functions. The classical convexity theory is extended to the generalized s-convex framework, some interesting theorems are proved, and the exact inequality representation of the product of two s-convex functions is given. Our findings not only extend the main conclusions in [36,38,39], but also provide new insights for the study of IV inequalities.

    The organization of this work is outlined below: Section 2 introduces essential background concepts, while Section 3 establishes a series of H-H-type inequalities for IV s-convex mappings using generalized fractional integral operators. Section 4 gives the conclusion.

    Let A denote a compact real interval, mathematically expressed as

    [A]=[A_,¯A]={aR|A_a¯A},

    where A_,¯AR satisfy A_¯A. If A_=¯A, the interval becomes degenerate. The intervals discussed in this paper are all non-degenerate intervals. An interval [A] is termed positive when A_>0, and negative if ¯A<0. The sets RI, R+I, and RI, respectively, denote all negative, positive, and arbitrary real-valued intervals. Additionally, we adopt the partial ordering "" defined by

    [A_,¯A][B_,¯B]A_B_and¯B¯A.

    For ηR and ARI, then

    ηA=η[A_,¯A]={[ηA_,η¯A],ifη0,[η¯A,ηA_],ifη<0.

    For arbitrary A,BRI, the following four arithmetic operations are given:

    A+B=[A_,¯A]+[B_,¯B]=[A_+B_,¯A+¯B],AB=[A_,¯A][B_,¯B]=[A_B_,¯A¯B],AB=[min{A_B_,A_¯B,¯AB_,¯A¯B},max{A_B_,A_¯B,¯AB_,¯A¯B}],B/A=[min{B_/A_,B_/¯A,¯B/A_,¯B/¯A},max{B_/A_,B_/¯A,¯B/A_,¯B/¯A}],(0[A_,¯A]).

    For additional information on interval arithmetic, refer to [40].

    Definition 2.1. [13] A function F:[1,2]R is classified as s-convex if it satisfies the convexity condition:

    F(ι1+(1ι)2)ιsF(1)+(1ι)sF(2), (2.1)

    for all 1,2[1,2] and ι[0,1] with s(0,1].

    In [41], Breckner introduced the IV s-convex functions.

    Definition 2.2. [41] F:[1,2]R+I is defined as an IV s-convex function if

    F(ι1+(1ι)2)ιsF(1)+(1ι)sF(2), (2.2)

    for 1,2[1,2] and ι[0,1].

    Consider F:[1,2]R+I, we define 21F(ι)dι by

    21F(ι)dι=[21F_(ι)dι,21¯F(ι)dι]

    and say that the function F is interval Lebesgue integrable on [1,2] (or that FIL[1,2]).

    Definition 2.3. [42] Let FL[1,2]. The bilateral R-L fractional integral operators, namely the left-sided Jα1+F and the right-sided Jα2F, are defined as

    Jα1+F(σ)=1Γ(α)σ1(σι)α1F(ι)dι,

    and

    Jα2F(σ)=1Γ(α)2σ(ισ)α1F(ι)dι,

    respectively, where α>0, 10, J01+F(σ)=J02F(σ)=F(σ) and Γ(α) is the Gamma function.

    The H-H inequality in the form of R-L fractional integrals was proved by Sarikaya et al. in [32] as follows:

    Theorem 2.4. Let FL[1,2] be a mapping from [1,2] to R+. If F satisfies the convexity condition, then

    F(1+22)Γ(α+1)2(21)α(Jα1+F(2)+Jα2F(1))12(F(1)+F(2)),

    with α>0.

    Definition 2.5. [36,37] Let F:[1,2]R+I. The IV R-L fractional integral operators, including both left-sided and right-sided variants, are defined as follows:

    Iα1+F(σ)=1Γ(α)σ1(σι)α1F(ι)dι,σ>1,

    and

    Iα2F(σ)=1Γ(α)2σ(ισ)α1F(ι)dι,σ<2,

    respectively. Here, Iα1+F(σ)=[Jα1+F_(σ),Jα1+¯F(σ)], and α>0.

    In [36], Budak et al. gave the fractional H-H inequality for IV convex functions as follows:

    Theorem 2.6. Let F:[1,2]R+I, and α>0. If F is an IV convex function, then

    F(1+22)Γ(α+1)2(21)α(Iα1+F(2)+Iα2F(1))F(1)+F(2)2. (2.3)

    Definition 2.7. [31] Let φ:[1,2]R+ be a monotonically increasing mapping, and suppose F,φL[1,2]. The generalized R-L fractional integral operators Jα,k1+,φF and Jα,k2,φF are defined by

    Jα,k1+,φ(F)(σ)=1Γ(α)σ1(σι)α1(φ(σ)φ(ι))kF(ι)dι,σ>1,

    and

    Jα,k2,φ(F)(σ)=1Γ(α)2σ(ισ)α1(φ(ι)φ(σ))kF(ι)dι,σ<2,

    respectively, where kN{0}, α>0, and 10.

    In 2023, Budak et al. introduced the generalized fractional integral for IV function as follows:

    Definition 2.8. [38] Let φ:[1,2]R+ be a monotonically increasing function, and F:[1,2]R+I. The generalized R-L fractional integrals Iα,k1+,φF and Iα,k2,φF of interval-valued functions are defined by

    Iα,k1+,φ(F)(σ)=1Γ(α)σ1(σι)α1(φ(σ)φ(ι))kF(ι)dι,σ>1,

    and

    Iα,k2,φ(F)(σ)=1Γ(α)2σ(ισ)α1(φ(ι)φ(σ))kF(ι)dι,σ<2,

    respectively, where α>0, 10 and kN{0}.

    The families of all functions that are Riemann integrable and interval Riemann integrable over the closed interval [1,2] are respectively represented by the notations R[1,2] and IR[1,2].

    Theorem 2.9. [38] Suppose FIR[1,2], φ:[1,2]R is a monotonically increasing function on (1,2) with φL[1,2]. Letting Φ(ι)=F(ι)+F(1+2ι), then ΦIR[1,2], and we have

    F(12(1+2))(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))12(Iα,k1+,φ(Φ)(2)+Iα,k2,φ(Φ)(1))12(F(1)+F(2))(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1)),

    where α>0 and kN{0}.

    Let Φ(ϖ)=F(ϖ)+F(1+2ϖ) and Λ(ϖ)=G(ϖ)+G(1+2ϖ) for ϖ[1,2]. It is straightforward to demonstrate that if F,GIR[1,2], then Φ(ϖ),Λ(ϖ)IR[1,2].

    Theorem 3.1. Let F:[1,2]R+I, FIR[1,2], and φ:[1,2]R+ be a monotonically increasing function. If F is an IV s-convex function, then

    F(1+22)(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))12s(Iα,k1+,φ(Φ)(2)+Iα,k2,φ(Φ)(1))12s(F(1)+F(2))(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1)), (3.1)

    where α>0 and kN{0}.

    Proof. By the assumption, we have

    F(1+22)F(1)+F(2)2s. (3.2)

    Letting 1=ι1+(1ι)2, 2=(1ι)1+ι2 for ι[0,1], we obtain

    F(1+22)12s(F(ι1+(1ι)2)+F((1ι)1+ι2)). (3.3)

    Then, multiplying the two sides of (3.3) by ια1(φ(2)φ(ι1+(1ι)2))k and integrating on [0, 1], we obtain

    F(1+22)10ια1(φ(2)φ(ι1+(1ι)2))kdι12s(10ια1(φ(2)φ(ι1+(1ι)2))kF(ι1+(1ι)2)dι+10ια1(φ(2)φ(ι1+(1ι)2))kF((1ι)1+ι2)dι).

    Letting y=ι1+(1ι2), we have

    F(1+22)21(2y)α1(φ(2)φ(y))kdy12s(21(2y)α1(φ(2)φ(y))kF(y)dy+21(2y)α1(φ(2)φ(y))kF(1+2y)dy).

    That is,

    F(1+22)Iα,k1+,φ(1)(2)12sIα,k1+,φ(Φ)(2). (3.4)

    By multiplying the two sides of (3.3) by ια1(φ((1ι)1+ι2)φ(1))k and integrating on [0, 1], we obtain

    F(1+22)10τα1(φ((1ι)1+ι2)φ(1))kdι12s(10ια1(φ((1ι)1+ι2)φ(1))kF(ι1+(1ι)2)dι+10ια1(φ((1ι)1+ι2)φ(1))kF((1ι)1+ι2)dι).

    Letting y=(1ι)1+ι2, we have

    F(1+22)Iα,k2,φ(1)(1)12sIα,k2,φ(Φ)(2). (3.5)

    Combining with conclusions (3.4) and (3.5), we obtain

    F(1+22)(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))12s(Iα,k1+,φ(Φ)(2)+Iα,k2,φ(Φ)(1)).

    According to (2.2), we have

    F(ι1+(1ι)2)ιsF(1)+(1ι)sF(2),F((1ι)1+ι2)ιsF(2)+(1ι)sF(1).

    That is,

    F(ι1+(1ι)2)+F((1ι)1+ι2)(F(1)+F(2)). (3.6)

    Multiplying the two sides of (3.6) by ια1(φ(2)φ(ι1+(1ι)2))k and integrating on [0, 1], we obtain

    10ια1(φ(2)φ(ι1+(1ι)2))kF(ι1+(1ι)2)dι+10ια1(φ(2)φ(ι1+(1ι)2))kF(ι2+(1ι)1)dι(F(1)+F(2))10ια1(φ(2)φ(ι1+(1ι)2))kdι.

    Letting y=ι1+(1ι)2, we have

    21(2y21)α1(φ(2)φ(y))kF(y)dy+21(2y21)α1(φ(2)φ(y))kF(1+2y)dy(F(1)+F(2))21(2y21)α1(φ(2)φ(y))kdy.

    That is,

    Iα,k1+,φ(Φ)(2)(F(1)+F(2))Iα,k1+,φ(1)(2). (3.7)

    Similarly, multiplying the two sides of (3.6) by ια1(φ((1ι)1+ι2)φ(1))k and integrating on [0, 1], let y=ι2+(1ι)1, we have

    21(y121)α1(φ(y)φ(1))kF(1+2y)dy+21(y121)α1(φ(y)φ(1))kF(y)dy(F(1)+F(2))21(y121)α1(φ(y)φ(1))kdy.

    Then, we obtain

    Iα,k2,φ(Φ)(1)(F(1)+F(2))Iα,k2,φ(1)(1). (3.8)

    By (3.7) and (3.8), we have

    Iα,k1+,φ(Φ)(2)+Iα,k2,φ(Φ)(1)(F(1)+F(2))(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1)).

    Hence, Theorem 3.1 is verified.

    Corollary 3.2. If φ(ι)=ιω for ωR and s=1, then

    F(1+22)Γ(α+k+1)2(21)α+k(Iα+k1+F(2)+Iα+k2F(1))12(F(1)+F(2)).

    Remark 3.3. If s=1, Theorem 3.1 is reduced to Theorem 4.1 obtained by Budak et al. in[38].

    Remark 3.4. If k=0, then

    F(1+22)Γ(α+1)2s(21)α(Iα1+F(2)+Iα2F(1))12s(F(1)+F(2)).

    Remark 3.5. If s=1 and k=0, then Theorem 3.1 simplifies to Theorem 3.2 as presented by Budak et al. in [36].

    Example 3.6. Consider F:[0,2]R+I, where F(x)=[2x24,8x2+20x3], φ(ι)=ιω for ωR, α=2,s=1, and k=1. Then we have

    Δ1=F(1+22)(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))=F(1)1Γ(2)20((2x)2+x2)dx=163[2,9]=[323,48],
    Δ2=12s(Iα,k1+,φ(Φ)(2)+Iα,k2,φ(Φ)(1))=121Γ(2)20((2x)2+x2)(F(x)+F(2x))dx=12[645,163+1285]=[325,46415],

    and

    Δ3=12s(F(1)+F(2))(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))=12(F(0)+F(2))1Γ(2)20((2x)2+x2)dx=83([4,3]+[4,5])=[0,163].

    Then, we obtain

    Δ1Δ2Δ3.

    The graphical representation (Figure 1) confirms the results. $

    Figure 1.  Illustration of Example 3.6: α=2,k=1, and s[0.4,1]. The red pattern represents Δ1, the blue pattern represents Δ2, and the green pattern represents Δ3.

    Theorem 3.7. Assuming that the conditions of Theorem 3.1 are met, then

    F(1+22)(Iα,k(12(1+2))+,φ(1)(2)+Iα,k(12(1+2)),φ(1)(1))12s(Iα,k(12(1+2))+,φ(Φ)(2)+Iα,k(12(1+2)),φ(Φ)(1))12s(F(1)+F(2))(Iα,k(12(1+2))+,φ(1)(2)+Iα,k(12(1+2)),φ(1)(1)), (3.9)

    where α>0 and kN{0}.

    Proof. First, consider 1=12ι1+12(2ι)2 and 2=12ι2+12(2ι)1 for ι[0,1] in the inclusion (3.2). Then,

    F(1+22)12s(F(12ι1+12(2ι)2)+F(12ι2+12(2ι)1))12s(F(1)+F(2)). (3.10)

    Then, multiplying the two sides of (3.10) by ια1(φ(2)φ(12ι1+12(2ι)2))k and integrating on [0, 1], we obtain

    F(1+22)10ια1(φ(2)φ(12ι1+12(2ι)2))kdι12s10ια1(φ(2)φ(12ι1+12(2ι)2))k(F(12ι1+12(2ι)2)+F(12ι2+12(2ι)1))dι12s10ια1(φ(2)φ(12ι1+12(2ι)2))k(F(1)+F(2))dι.

    Letting y=12ι1+12(2ι)2, we have

    F(1+22)(221)α21+22(2y)α1(φ(2)φ(y))kdy12s(221)α21+22(2y)α1(φ(2)φ(y))k(F(y)+F(1+2y))dy12s(F(1)+F(2))(221)α21+22(2y)α1(φ(2)φ(y))kdy,

    that is,

    F(1+22)Iα,k(12(1+2))+,φ(1)(2)12sIα,k(12(1+2))+,φ(Φ)(2)12s(F(1)+F(2))Iα,k(12(1+2))+,φ(1)(2). (3.11)

    Similarly, multiplying the two sides of (3.10) by ια1(φ(12(2ι)1+12ι2)φ(1))k and integrating on [0, 1], we have

    F(1+22)10ια1(φ(12(2ι)1+12ι2)φ(1))kdι12s10ια1(φ(12(2ι)1+12ι2)φ(1))k×(F(12ι1+12(2ι)2)+F(12ι2+12(2ι)1))dι12s(F(1)+F(2))10ια1(φ(12(2ι)1+12ι2)φ(1))kdι.

    Letting y=12(2ι)1+12ι2, we have

    F(1+22)(221)α1+221(y1)α1(φ(y)φ(1))kdy12s(221)α1+221(y1)α1(φ(y)φ(1))k(F(y)+F(1+2y))dy12s(F(1)+F(2))(221)α1+221(y1)α1(φ(y)φ(1))kdy,

    that is,

    F(1+22)Iα,k(12(1+2)),φ(1)(1))12sIα,k(12(1+2)),φ(Φ)(1)12s(F(1)+F(2))Iα+s,k(12(1+2)),φ(1)(1). (3.12)

    By (3.11) and (3.12), we obtain the result.

    Corollary 3.8. If φ(ι)=ιω for ωR and s=1, then

    F(1+22)2α+k1Γ(α+k+1)(21)α+k(Iα+k12(1+2)+F(2)+Iα+k12(1+2)F(1))F(1)+F(2)2.

    Remark 3.9. If s=1, then Theorem 3.7 simplifies to Theorem 4.4 given by Budak et al. in[38].

    Remark 3.10. If k=0, then

    F(1+22)2αΓ(α+1)2s(21)α(Iα+k12(1+2)+F(2)+Iα+k12(1+2)F(1))12s(F(1)+F(2)).

    Remark 3.11. If s=1 and k=0, then Theorem 3.7 which has been obtained by Zhao et al. in [39].

    Example 3.12. Consider F:[0,1]R+I, where F(x)=[x2,5(12)x], φ(ι)=ιω for ωR, α=2,s=1, and k=1. Then we have

    T1=F(1+22)(Iα,k(12(1+2))+,φ(1)(2)+Iα,k(12(1+2)),φ(1)(1))=F(12)1Γ(2)(112(1x)2dx+120x2dx)=112[14,5(12)12][0.0208,0.3750],
    T2=12s(Iα,k(12(1+2))+,φ(Φ)(2)+Iα,k(12(1+2)),φ(Φ)(1))=121Γ(2)(112(1x)2(F(x)+F(1x))dx+120x2(F(x)+F(1x))dx)[0.0229,0.3574],

    and

    T3=12s(F(1)+F(2))(Iα,k(12(1+2))+,φ(1)(2)+Iα,k(12(1+2)),φ(1)(1))=121Γ(2)(F(0)+F(1))(112(1x)2dx+120x2dx)=124([0,4]+[1,92])[0.0417,0.3542].

    Then, we obtain

    T1T2T3.

    Theorem 3.13. Given that the assumptions of Theorem 3.1 hold, we can obtain

    F(1+22)(Iα,k1+,φ(1)(12(1+2))+Iα,k2,φ(1)(12(1+2)))12s(Iα,k1+,φ(Φ)(12(1+2))+Iα,k2,φ(Φ)(12(1+2)))12s(F(1)+F(2))(Iα,k1+,φ(1)(12(1+2))+Iα,k2,φ(1)(12(1+2))), (3.13)

    for α>0 and kN{0}.

    Proof. Considering 1=12(1+ι)1+12(1ι)2 and 1=12(1ι)1+12(1+ι)2 for ι[0,1] in the inclusion (3.2). Then we obtain

    F(1+22)12s(F(12(1+ι)1+12(1ι)2)+F(12(1ι)1+12(1+ι)2))12s(F(1)+F(2)). (3.14)

    Then, multiplying two sides of (3.14) by

    ια1(φ(12(1+2))φ(12(1+ι)1+12(1ι)2))k,

    and integrating on [0, 1], we obtain

    F(1+22)10ια1(φ(1+22)φ(12(1+ι)1+12(1ι)2)k)dι12s10τα1(φ(1+22)φ(12(1+ι)1+12(1ι)2))k(F(12(1+ι)1+12(1ι)2)+F(12(1ι)1+12(1+ι)2))dι12s(F(1)+F(2))10ια1(φ(1+22)φ(12(1+ι)1+12(1ι)2))kdι.

    Letting z=12(1+ι)1+12(1ι)2, we have

    F(1+22)1+221(1+22z)α1(φ(12(1+2))φ(z))kdz12s1+221(1+22z)α1(φ(1+22)φ(z))k(F(z)+F(1+2z))dz12s(F(1)+F(2))12(1+2)1(1+22z)α1(φ(1+22)φ(z))kdz,

    that is,

    F(1+22)Iα,k1+,φ(1)(1+22)12sIα,k1+,φ(Φ)(1+22)12s(F(1)+F(2))Iα,k1+,φ(1)(1+22). (3.15)

    Multiplying two sides of (3.14) by

    ια1(φ(12(1ι)1+12(1+ι)2)φ(1+22))k,

    and integrating on [0, 1], we obtain

    F(1+22)10τα1(φ(1+22)φ(12(1+ι)1+12(1ι)2))kdι12s10ια1(φ(1+22)φ(12(1+ι)1+12(1ι)2))k×(F(12(1+ι)1+12(1ι)2)+F(12(1ι)1+12(1+ι)2))dι12s(F(1)+F(2))10ια1(φ(1+22)φ(12(1+ι)1+12(1ι)2))kdι.

    Letting z=12(1ι)1+12(1+ι)2, we obtain

    F(1+22)Iα,k2,φ(1)(1+22)12sIα,k2,φ(Φ)(1+22)12s(F(1)+F(2))Iα,k2,φ(1)(1+22). (3.16)

    Adding the inclusion (3.15) to (3.16), we complete the proof.

    Corollary 3.14. If φ(ι)=ιω for ωR and s=1, then

    F(1+22)2α+k1Γ(α+k+1)(21)α+k(Iα+k1+F(1+22)+Iα+k2F(1+22))12(F(1)+F(2)).

    Remark 3.15. If s=1, Theorem 3.13 simplifies to Theorem 4.7 presented by Budak et al. in[38].

    Remark 3.16. If s=1 and k=0, then

    F(1+22)2α1Γ(α+1)(21)α(Iα1+F(1+22)+Iα2F(1+22))12(F(1)+F(2)),

    which has been obtained by Zhao et al. in [39].

    Example 3.17. Consider F:[0,2]R+I, where F(x)=[x2,x2+8], φ(ι)=ιω for ωR, α=2,s=1, and k=1. Then we have

    D1=F(1+22)(Iα,k1+,φ(1)(12(1+2))+Iα,k2,φ(1)(12(1+2)))=F(1)1Γ(2)(10(1x)2dx+21(x1)2dx)=23[1,7]=[23,143],
    D2=12s(Iα,k1+,φ(Φ)(12(1+2))+Iα,k2,φ(Φ)(12(1+2)))=121Γ(2)20(1x)2(F(x)+F(2x))dx=[1615,6415],

    and

    D3=12s(F(1)+F(2))(Iα,k1+,φ(1)(12(1+2))+Iα,k2,φ(1)(12(1+2)))=121Γ(2)(F(0)+F(2))20(1x)2dx=13([0,8]+[4,4])=[43,4].

    Then, we obtain

    D1D2D3.

    Theorem 3.18. Let F,G:[1,2]R+I, s1,s2(0,1], and φ:[1,2]R be a monotonically increasing function. If F and G are both IV s-convex functions, then

    F(1+22)G(1+22)(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))12s1+s2(Iα,k1+,φ(ΦΛ)(2)+Iα,k2,φ(ΦΛ)(1))12s1+s2(P(1,2)+Q(1,2))(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1)), (3.17)

    where

    P(1,2)=F(1)G(1)+F(2)G(2),

    and

    Q(1,2)=F(1)G(2)+F(2)G(1).

    Proof. By hypothesis, then

    F(1+22)F(1)+F(2)2s1,G(1+22)G(1)+G(2)2s2.

    Considering 1=ι1+(1ι2) and 2=(1ι)1+ι2, we obtain

    F(1+22)G(1+22)12s1+s2(F(ι1+(1ι2))+F((1ι)1+ι2))(G(ι1+(1ι2))+G((1ι)1+ι2))12s1+s2(P(1,2)+Q(1,2)). (3.18)

    By multiplying two sides of (3.18) by ια1(φ(2)φ(ι1+(1ι)2))k and integrating on [0, 1], we obtain

    F(1+22)G(1+22)10ια1(φ(2)φ(ι1+(1ι)2))kdι12s1+s210ια1(φ(2)φ(ι1+(1ι)2))k(F(ι1+(1ι2))+F((1ι)1+ι2))(G(ι1+(1ι2))+G((1ι)1+ι2))dτ12s1+s2(P(1,2)+Q(1,2))10ια1(φ(2)φ(ι1+(1ι)2))kdι.

    Letting y=ι1+(1ι)2, then

    F(1+22)G(1+22)(121)α21(2y)α1(φ(2)φ(y))kdy12s1+s2(121)α(21(2y)α1(φ(2)φ(y))k(F(y)+F(1+2y))(G(y)+G(1+2y))dy12s1+s2(121)α(P(1,2)+Q(1,2))21(2y)α1(φ(2)φ(y))kdy,

    that is,

    F(1+22)Iα,k1+,φ(1)(2)12s1+s2Iα,k1+,φ(ΦΛ)(2)12s1+s2(P(1,2)+Q(1,2))Iα,k1+,φ(1)(2). (3.19)

    By multiplying two sides of (3.18) by ια1(φ((1ι)1+ι2)φ(1))k and integrating on [0, 1], we have

    F(1+22)G(1+22)10ια1(φ((1ι)1+ι2)φ(1))kdι12s1+s210ια1(φ((1ι)1+ι2)φ(1))k(F(ι1+(1ι2))+F((1ι)1+ι2))(G(ι1+(1ι2))+G((1ι)1+ι2))dτ12s1+s210ια1(φ((1ι)1+ι2)φ(1))k(P(1,2)+Q(1,2))dι.

    Letting y=(1ι)1+ι2, that is,

    F(1+22)G(1+22)Iα,k2,φ(1)(1)12s1+s2Iα,k2,φ(ΦΛ)(1)12s1+s2(P(1,2)+Q(1,2))Iα,k2,φ(1)(1). (3.20)

    Adding the inclusion (3.19) and (3.20), we obtained the desired result.

    Example 3.19. Assume F,G:[0,2]R+I, where F(x)=[x2,x2+10] and

    G(x)=[12x2,x2+8],

    respectively. Let φ(ι)=ιω for ωR, α=2,k=1, and s=1. Then we have

    Θ1=F(1+22)G(1+22)(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))=F(1)G(1)1Γ(2)20((2x)2+x2)dx=163([1,9][12,7])=[83,336],

    and

    Θ2=12s1+s2(Iα,k1+,φ(ΦΛ)(2)+Iα,k2,φ(ΦΛ)(1))=141Γ(2)20((2x)2+x2)(F(x)+F(2x))(G(x)+G(2x))dx=[5.4857,303.2381],

    and

    Θ3=12s1+s2(P(1,2)+Q(1,2))(Iα,k1+,φ(1)(2)+Iα,k2,φ(1)(1))=14(F(0)+F(2))(G(0)+G(2))1Γ(2)20((2x)2+x2)dx=43([8,192])=[323,256].

    Then, we obtain

    Θ1Θ2Θ3.

    Analogously, we can derive the subsequent results.

    Theorem 3.20. Assuming that the conditions of Theorem 3.18 hold, we consequently obtain

    F(1+22)G(1+22)(Iα,k(12(1+2))+,φ(1)(2)+Iα,k(12(1+2)),φ(1)(1))12s1+s2(Iα,k(12(1+2))+,φ(ΦΛ)(2)+Iα,k(12(1+2)),φ(ΦΛ)(1))12s1+s2(P(1,2)+Q(1,2))(Iα,k(12(1+2))+,φ(1)(2)+Iα+s,k(12(1+2)),φ(1)(1)). (3.21)

    Theorem 3.21. Assuming that the conditions of Theorem 3.18 hold, we obtain

    F(1+22)G(1+22)(Iα,k1+,φ(1)(1+22)+Iα,k2,φ(1)(1+22))12s1+s2(Iα,k1+,φ(ΦΛ)(1+22)+Iα,k2,φ(ΦΛ)(1+22))12s1+s2(P(1,2)+Q(1,2))(Iα,k1+,φ(1)(1+22)+Iα,k2,φ(1)(1+22)). (3.22)

    This study derives novel H-H-type inequalities through generalized fractional integrals applied to IV s-convex functions. These not only generalize but also refine the previously proposed inequalities established by Budak et al. and provide a foundation for further exploration of generalized convexity and IV estimation. The developed techniques offer new tools for uncertainty quantification in convex optimization problems with imprecise measurements. Future research directions include:

    (1) Develop new interval H-H inequalities based on more general convexity.

    (2) Extension to IV quasi-convex functions using variable-order fractional operators.

    (3) Applications in robust portfolio optimization with IV risk measures.

    G. Deng and D. Zhao: Conceptualization, Methodology, Formal analysis; S. Etemad: Conceptualization, Software, Formal analysis, Investigation, Writing–original draft preparation; G. Deng: Writing–original draft preparation; D. Zhao: Validation, Investigation, Supervision, Project administration, Writing–review and editing; J. Tariboon: Validation, Formal analysis, Project administration, Writing–review and editing. All authors read and approved the final manuscript.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This research was funded by the National Science, Research and Innovation Fund (NSRF) and King Mongkut's University of Technology North Bangkok, under Contract No. KMUTNB-FF-68-B-04, and the Foundation of Hubei Normal University (2024Z022).

    The authors declare no conflict of interest.



    [1] P. N. Anh, L. T. H. An, The subgradient extragradient method extended to equilibrium problems, Optimization, 64 (2012), 225-248.
    [2] A. Antipin, Equilibrium programming: proximal methods, Comput. Math. Math. Phys., 37 (1997), 1285-1296.
    [3] M. Bianchi, S. Schaible, Generalized monotone bifunctions and equilibrium problems, J. Optimiz. Theory App., 9 (1996), 31-43.
    [4] G. Bigi, M. Castellani, M. Pappalardo, M. Passacantando, Existence and solution methods for equilibria, Eur. J. Oper. Res., 227 (2013), 1-11. doi: 10.1016/j.ejor.2012.11.037
    [5] G. Bigi, M. Castellani, M. Pappalardo, M. Passacantando, Nonlinear programming techniques for equilibria, Springer International Publishing, 2019.
    [6] E. Blum, From optimization and variational inequalities to equilibrium problems, Mathematics Students, 63 (1994), 123-145.
    [7] F. Browder, W. Petryshyn, Construction of fixed points of nonlinear mappings in hilbert space, J. Math. Anal. Appl., 20 (1967), 197-228. doi: 10.1016/0022-247X(67)90085-6
    [8] Y. Censor, A. Gibali, S. Reich, The subgradient extragradient method for solving variational inequalities in hilbert space, J. Optimiz. Theory App., 148 (2010), 318-335.
    [9] C. E. Chidume, A. Adamu, L. C. Okereke, A krasnoselskii-type algorithm for approximating solutions of variational inequality problems and convex feasibility problems, J. Nonlinear Var. Anal., 2 (2018), 203-218. doi: 10.23952/jnva.2.2018.2.07
    [10] S. Dafermos, Traffic equilibrium and variational inequalities, Transport. Sci., 14 (1980), 42-54. doi: 10.1287/trsc.14.1.42
    [11] K. Fan, A minimax inequality and applications, Inequalities Ⅲ, New York: Academic Press, 1972.
    [12] M. Farid, The subgradient extragradient method for solving mixed equilibrium problems and fixed point problems in hilbert spaces, J. Appl. Numer. Optim., 1 (2019), 335-345.
    [13] S. D. Flåm, A. S. Antipin, Equilibrium programming using proximal-like algorithms, Math. Program., 78 (1996), 29-41. doi: 10.1007/BF02614504
    [14] F. Giannessi, A. Maugeri, P. M. Pardalos, Equilibrium problems: Nonsmooth optimization and variational inequality models, Springer Science & Business Media, 2006.
    [15] H. H. Bauschke, P. L. Combettes, Convex analysis and monotone operator theory in Hilbert spaces, New York: Springer, 2017.
    [16] D. V. Hieu, Halpern subgradient extragradient method extended to equilibrium problems, RACSAM, 111 (2016), 823-840.
    [17] D. V. Hieu, P. K. Quy, L. V. Vy, Explicit iterative algorithms for solving equilibrium problems, Calcolo, 56 (2019), 11. doi: 10.1007/s10092-019-0308-5
    [18] G. Korpelevich, The extragradient method for finding saddle points and other problems, Matecon, 12 (1976), 747-756.
    [19] S. I. Lyashko, V. V. Semenov, A new two-step proximal algorithm of solving the problem of equilibrium programming, In: Optimization and its applications in control and data sciences, Springer International Publishing, 2016,315-325.
    [20] P. E. Maingé, Strong convergence of projected subgradient methods for nonsmooth and nonstrictly convex minimization, Set-Valued Anal., 16 (2008), 899-912. doi: 10.1007/s11228-008-0102-z
    [21] G. Mastroeni, On auxiliary principle for equilibrium problems, In: Nonconvex optimization and its applications, Springer, 2003,289-298.
    [22] A. Moudafi, Proximal point algorithm extended to equilibrium problems, J. Nat. Geom., 15 (1999), 91-100.
    [23] A. Moudafi, Viscosity approximation methods for fixed-points problems, J. Math. Anal. Appl., 241 (2000), 46-55. doi: 10.1006/jmaa.1999.6615
    [24] L. Muu, W. Oettli, Convergence of an adaptive penalty scheme for finding constrained equilibria, Nonlinear Anal. Theor., 18 (1992), 1159-1166. doi: 10.1016/0362-546X(92)90159-C
    [25] F. U. Ogbuisi, Y. Shehu, A projected subgradient-proximal method for split equality equilibrium problems of pseudomonotone bifunctions in banach spaces, J. Nonlinear Var. Anal., 3 (2019), 205-224.
    [26] T. D. Quoc, P. N. Anh, L. D. Muu, Dual extragradient algorithms extended to equilibrium problems, J. Global Optim., 52 (2011), 139-159.
    [27] T. D. Quoc, M. N. V. H. Le Dung, Extragradient algorithms extended to equilibrium problems, Optimization, 57 (2008), 749-776. doi: 10.1080/02331930601122876
    [28] R. T. Rockafellar, Convex analysis, Princeton University Press, 1970.
    [29] P. Santos, S. Scheimberg, An inexact subgradient algorithm for equilibrium problems, Comput. Appl. Math., 30 (2011), 91-107.
    [30] T. M. M. Sow, An iterative algorithm for solving equilibrium problems, variational inequalities and fixed point problems of multivalued quasi-nonexpansive mappings, Appl. Set-Valued Anal. Optim., 1 (2019), 171-185.
    [31] G. Stampacchia, Formes bilinéaires coercitives sur les ensembles convexes, Comptes Rendus Hebdomadaires Des Seances De L Academie Des Sciences, 258 (1964), 4413.
    [32] S. Takahashi, W. Takahashi, Viscosity approximation methods for equilibrium problems and fixed point problems in hilbert spaces, J. Math. Anal. Appl., 331 (2007), 506-515. doi: 10.1016/j.jmaa.2006.08.036
    [33] L. Q. Thuy, C. F. Wen, J. C. Yao, T. N. Hai, An extragradient-like parallel method for pseudomonotone equilibrium problems and semigroup of nonexpansive mappings, Miskolc Math. Notes, 19 (2018), 1185. doi: 10.18514/MMN.2018.2114
    [34] D. Q. Tran, M. L. Dung, V. H. Nguyen, Extragradient algorithms extended to equilibrium problems, Optimization, 57 (2008), 749-776. doi: 10.1080/02331930601122876
    [35] H. ur Rehman, P. Kumam, A. B. Abubakar, Y. J. Cho, The extragradient algorithm with inertial effects extended to equilibrium problems, Comp. Appl. Math., 39 (2010), 100.
    [36] H. ur Rehman, P. Kumam, Y. J. Cho, P. Yordsorn, Weak convergence of explicit extragradient algorithms for solving equilibirum problems. J. Inequal. Appl., 2019 (2019), 282. doi: 10.1186/s13660-019-2233-1
    [37] H. ur Rehman, P. Kumam, Y. Je Cho, Y.I. Suleiman, W. Kumam, Modified popov's explicit iterative algorithms for solving pseudomonotone equilibrium problems. Optimization Methods and Software, 2020, 1-32.
    [38] H. ur Rehman, P. Kumam, W. Kumam, M. Shutaywi, W. Jirakitpuwapat, The inertial sub-gradient extra-gradient method for a class of pseudo-monotone equilibrium problems, Symmetry, 12 (2020), 463. doi: 10.3390/sym12030463
    [39] N. T. Vinh, L. D. Muu, Inertial extragradient algorithms for solving equilibrium problems, Acta Mathematica Vietnamica, 44 (2019), 639-663. doi: 10.1007/s40306-019-00338-1
    [40] H. K. Xu, Another control condition in an iterative method for nonexpansive mappings, B. Aust. Math. Soc., 65 (2002), 109-113. doi: 10.1017/S0004972700020116
    [41] J. C. Yao, Variational inequalities with generalized monotone operators, Math. Oper. Res., 19 (1994), 691-705. doi: 10.1287/moor.19.3.691
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