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Multi-step extragradient method with regularization for triple hierarchical variational inequalities with variational inclusion and split feasibility constraints

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Abstract

By combining Korpelevich’s extragradient method, the viscosity approximation method, the hybrid steepest-descent method, Mann’s iteration method, and the gradient-projection method with regularization, a hybrid multi-step extragradient algorithm with regularization for finding a solution of triple hierarchical variational inequality problem is introduced and analyzed. It is proven that under appropriate assumptions, the proposed algorithm converges strongly to a unique solution of a triple hierarchical variational inequality problem which is defined over the set of solutions of a hierarchical variational inequality problem defined over the set of common solutions of finitely many generalized mixed equilibrium problems (GMEP), finitely many variational inclusions, fixed point problems, and the split feasibility problem (SFP). We also prove the strong convergence of the proposed algorithm to a common solution of the SFP, finitely many GMEPs, finitely many variational inclusions, and the fixed point problem of a strict pseudocontraction. The results presented in this paper improve and extend the corresponding results announced by several others.

MSC:49J30, 47H09, 47J20, 49M05.

1 Introduction

The following problems have their own importance because of their applications in diverse areas of science, engineering, social sciences, and management:

  • Equilibrium problems including variational inequalities.

  • Variational inclusion problems.

  • Split feasibility problems.

  • Fixed point problems.

One way or the other, these problems are related to each other. They are described as follows.

Equilibrium problem

Let C be a nonempty closed convex subset of a real Hilbert space H and Θ:C×CR be a real-valued bifunction. The equilibrium problem (EP) is to find an element xC such that

Θ(x,y)0,yC.

The set of solutions of EP is denoted by EP(Θ). It includes several problems, namely, variational inequality problems, optimization problems, saddle point problems, fixed point problems, etc., as special cases. For further details on EP, we refer to [16] and the references therein.

Let A:CH be a nonlinear operator. If Θ(x,y)=A(x),yx, then EP reduces to the variational inequality problem of finding xC such that

A ( x ) , y x 0,yC.

For further details on variational inequalities and their generalizations, we refer to [713] and the references therein.

During the last two decades, EP has been extended and generalized in several directions. The generalized mixed equilibrium problem (GMEP), one of the generalizations of EP, is to find xC such that

Θ(x,y)+φ(y)φ(x)+Ax,yx0,yC,
(1.1)

where φ:CR is a real-valued function. The set of solutions of GMEP is denoted by GMEP(Θ,φ,A). For different choices of operators/functions Θ, φ, and A, we get different forms of equilibrium problems. For applications of GMEP, we refer to [14, 15] and the references therein.

Variational inclusion problem

Let B:CH be a single-valued mapping and R:C 2 H be a set-valued mapping with D(R)=C, where D(R) denotes the domain of R. The variational inclusion problem is to find xC such that

0Bx+Rx.
(1.2)

We denote by I(B,R) the solution set of the variational inclusion problem (1.2). In particular, if B=R=0, then I(B,R)=C. If B=0, then problem (1.2) becomes the inclusion problem introduced by Rockafellar [16]. It is well known that problem (1.2) provides a convenient framework for the unified study of optimal solutions in many optimization related areas including mathematical programming, complementarity problems, variational inequalities, optimal control, mathematical economics, equilibria and game theory, etc. Let a set-valued mapping R:D(R)H 2 H be maximal monotone. We define the resolvent operator J R , λ :H D ( R ) ¯ associated with R and λ>0 as follows:

J R , λ = ( I + λ R ) 1 ,xH.

Huang [17] studied problem (1.2) in the case where R is maximal monotone and B is strongly monotone and Lipschitz continuous with D(R)=C=H. Zeng et al. [18] further studied problem (1.2) in a more general setting than in [17]. They gave the geometric convergence rate estimate for approximate solutions. Various types of iterative algorithms for solving variational inclusions have been further studied and developed in the literature; see, for example, [1922] and the references therein.

Split feasibility problem

Let C and Q be nonempty closed convex subsets of real Hilbert spaces H 1 and H 2 , respectively. The split feasibility problem (SFP) is to find a point x such that

xCandAxQ,
(1.3)

where A: H 1 H 2 is a bounded linear operator from H 1 to H 2 . We denote by Γ the solution set of the SFP. It is a model of an inverse problem which arises in phase retrievals and in medical image reconstruction. A number of image reconstruction problems can be formulated as SFP; see, for example [23] and the references therein. Recently, it is found that the SFP can also be applied to study intensity-modulated radiation therapy (IMRT); see, for example, [24, 25] and the references therein. In the recent past, a wide variety of iterative methods have been proposed to solve SFP; see, for example, [2428] the references therein.

Fixed point problem

Let C be a nonempty subset of a H and T:CC be a mapping. The fixed point problem is to find an element xC such that T(x)=x.

It is a well-known problem and has tremendous applications in different branches of science, engineering, social sciences, and management.

The following proposition provides some relations among the above mentioned problems.

Proposition 1.1 Given x H, the following statements are equivalent:

  1. (a)

    x solves the SFP;

  2. (b)

    x solves the fixed point equation

    P C (Iλf) x = x ,

    where λ>0, f= A (I P Q )A, P Q is the projection operator and A is the adjoint of A;

  3. (c)

    x solves the variational inequality problem (VIP) of finding x C such that

    f ( x ) , x x 0,xC.

A variational inequality problem which is defined over the set of fixed points of a mapping is called hierarchical variational inequality problem; that is, when the set C in variational inequality formulation is equal to the set of fixed points of a mapping. A variational inequality problem which is defined over the set of solutions of a hierarchical variational inequality problem is called a triple hierarchical variational inequality problem. For further details on hierarchical variational inequality problems and triple hierarchical variational inequality problems, we refer to [29], a recent survey on these problems.

Very recently, Kong et al. [30] considered the following triple hierarchical variational inequality problem (THVIP).

Problem 1.1 Let C be a nonempty closed convex subset of a real Hilbert space H and F:CH be a κ-Lipschitzian and η-strongly monotone operator, where κ and η are positive constants. Let A:CH be a monotone and L-Lipschitzian mapping, V:CH be a ρ-contraction with coefficient ρ[0,1), S:CC be a nonexpansive mapping, and T:CC be a ξ-strictly pseudocontractive mapping with Fix(T)VI(C,A), where Fix(T) denotes the set of all fixed points of T. Let 0<μ< 2 η κ 2 and 0<γτ, where τ=1 1 μ ( 2 η μ κ 2 ) . Then the objective is to find x Ξ such that

( μ F γ V ) x , x x 0,xΞ,
(1.4)

where Ξ denotes the solution set of the hierarchical variational inequality problem (HVIP) of finding z Fix(T)VI(C,A) such that

( μ F γ S ) z , z z 0,zFix(T)VI(C,A).
(1.5)

Kong et al. [30] presented an algorithm for finding a solution of Problem 1.1. Under some conditions, they proved that the sequence { x n } generated by the proposed algorithm converges strongly to a point x Fix(T)VI(C,A) which is a unique solution of Problem 1.1 provided that {S x n } is bounded and x n + 1 x n + x n z n =o( ϵ n 2 ). They also showed under certain conditions that the sequence { x n } generated by proposed algorithm converges strongly to a unique solution x of the following VIP provided that x n + 1 x n + x n z n =o( ϵ n 2 ) and the sequence {S x n } is bounded:

find  x Ξ such that  F x , x x 0,xΞ.

In this paper, we consider the following triple hierarchical variational inequality problem (THVIP).

Problem 1.2 Let M, N be two positive integers. Assume that

  1. (i)

    F:HH is κ-Lipschitzian and η-strongly monotone with positive constants κ,η>0 such that 0<γτ and 0<μ< 2 η κ 2 where τ=1 1 μ ( 2 η μ κ 2 ) ;

  2. (ii)

    for each k{1,2,,M}, Θ k :C×CR satisfies conditions (A1)-(A4) and φ k :CR{+} is a proper lower semicontinuous and convex function with restriction (B1) or (B2) (conditions (A1)-(A4) and (B1)-(B2) are given in the next section);

  3. (iii)

    for each k{1,2,,M} and i{1,2,,N}, R i :C 2 H is a maximal monotone mapping, and A k :HH and B i :CH are μ k -inverse strongly monotone and η i -inverse strongly monotone, respectively;

  4. (iv)

    T:HH is a ξ-strict pseudocontraction, S:HH is a nonexpansive mapping and V:HH is a ρ-contraction with coefficient ρ[0,1);

  5. (v)

    Ω:=( k = 1 M GMEP( Θ k , φ k , A k ))( i = 1 N I( B i , R i ))Fix(T)Γ.

Then the objective is to find x Ξ such that

( μ F γ V ) x , x x 0,xΞ,
(1.6)

where Ξ denotes the solution set of the hierarchical variational inequality problem (HVIP) of finding z Ω such that

( μ F γ S ) z , z z 0,zΩ.
(1.7)

By combining Korpelevich’s extragradient method, the viscosity approximation method, the hybrid steepest-descent method, Mann’s iteration method, and the gradient-projection method (GPM) with regularization, we introduce and analyze a hybrid multi-step extragradient algorithm with regularization in the setting of Hilbert spaces. It is proven that under appropriate assumptions, the proposed algorithm converges strongly to a unique solution of THVIP (1.6). The algorithm and convergence result of this paper extend and generalize several existing algorithms and results, respectively, in the literature.

2 Preliminaries

Throughout this paper, unless otherwise specified, we assume that H is a real Hilbert space whose inner product and norm are denoted by , and , respectively. We write x n x (respectively, x n x) to indicate that the sequence { x n } converges (respectively, weakly) to x. Moreover, we use ω w ( x n ) to denote the weak ω-limit set of the sequence { x n }, that is,

ω w ( x n ):= { x H : x n i x  for some subsequence  { x n i }  of  { x n } } .

Definition 2.1 A mapping T:HH is said to be

  1. (a)

    nonexpansive if

    TxTyxy,x,yH;
  2. (b)

    firmly nonexpansive if 2TI is nonexpansive, or equivalently, if T is 1-inverse strongly monotone (1-ism),

    xy,TxTy T x T y 2 ,x,yH;

alternatively, T is firmly nonexpansive if and only if T can be expressed as

T= 1 2 (I+S),

where S:HH is nonexpansive; projections are firmly nonexpansive.

It can easily be seen that if T is nonexpansive, then IT is monotone.

Definition 2.2 A mapping T:HH is said to be an averaged mapping if it can be written as the average of the identity I and a nonexpansive mapping, that is,

T(1α)I+αS,

where α(0,1) and S:HH is nonexpansive. More precisely, when the last equality holds, we say that T is α-averaged. Thus firmly nonexpansive mappings (in particular, projections) are 1 2 -averaged mappings.

Proposition 2.1 [31]

Let T:HH be a given mapping.

  1. (a)

    T is nonexpansive if and only if the complement IT is 1 2 -ism.

  2. (b)

    If T is ν-ism, then for γ>0, γT is ν γ -ism.

  3. (c)

    T is averaged if and only if the complement IT is ν-ism for some ν>1/2. Indeed, for α(0,1), T is α-averaged if and only if IT is 1 2 α -ism.

Proposition 2.2 [31, 32]

Let S,T,V:HH be given operators.

  1. (a)

    If T=(1α)S+αV for some α(0,1) and if S is averaged and V is nonexpansive, then T is averaged.

  2. (b)

    T is firmly nonexpansive if and only if the complement IT is firmly nonexpansive.

  3. (c)

    If T=(1α)S+αV for some α(0,1) and if S is firmly nonexpansive and V is nonexpansive, then T is averaged.

  4. (d)

    The composite of finitely many averaged mappings is averaged, that is, if each of the mappings { T i } i = 1 N is averaged, then so is the composite T 1 T N . In particular, if T 1 is α 1 -averaged and T 2 is α 2 -averaged, where α 1 , α 2 (0,1), then the composite T 1 T 2 is α-averaged, where α= α 1 + α 2 α 1 α 2 .

  5. (e)

    If the mappings { T i } i = 1 N are averaged and have a common fixed point, then

    i = 1 N Fix( T i )=Fix( T 1 T N ).

The notation Fix(T) denotes the set of all fixed points of the mapping T, that is, Fix(T)={xH:Tx=x}.

A mapping T:CC is said to be ξ-strictly pseudocontractive if there exists ξ[0,1) such that

T x T y 2 x y 2 +ξ ( I T ) x ( I T ) y 2 ,x,yC.

In this case, we also say that T is a ξ-strict pseudocontraction. We denote by Fix(S) the set of fixed points of S. In particular, if ξ=0, T is a nonexpansive mapping.

It is clear that, in a real Hilbert space H, T:CC is ξ-strictly pseudocontractive if and only if the following inequality holds:

TxTy,xy x y 2 1 ξ 2 ( I T ) x ( I T ) y 2 ,x,yC.

This immediately implies that if T is a ξ-strictly pseudocontractive mapping, then IT is 1 ξ 2 -inverse strongly monotone; for further details, we refer to [33] and the references therein. It is well known that the class of strict pseudocontractions strictly includes the class of nonexpansive mappings and that the class of pseudocontractions strictly includes the class of strict pseudocontractions.

Lemma 2.1 [[33], Proposition 2.1]

Let C be a nonempty closed convex subset of a real Hilbert space H and T:CC be a mapping.

  1. (a)

    If T is a ξ-strictly pseudocontractive mapping, then T satisfies the Lipschitzian condition

    TxTy 1 + ξ 1 ξ xy,x,yC.
  2. (b)

    If T is a ξ-strictly pseudocontractive mapping, then the mapping IT is semiclosed at 0, that is, if { x n } is a sequence in C such that x n x ˜ and (IT) x n 0, then (IT) x ˜ =0.

  3. (c)

    If T is ξ-(quasi-)strict pseudocontraction, then the fixed point set Fix(T) of T is closed and convex so that the projection P Fix ( T ) is well defined.

Lemma 2.2 [34]

Let C be a nonempty closed convex subset of a real Hilbert space H. Let T:CC be a ξ-strictly pseudocontractive mapping. Let γ and δ be two nonnegative real numbers such that (γ+δ)ξγ. Then

γ ( x y ) + δ ( T x T y ) (γ+δ)xy,x,yC.

Lemma 2.3 (Demiclosedness principle)

Let C be a nonempty closed convex subset of a real Hilbert space H. Let S be a nonexpansive self-mapping on C with Fix(S). Then IS is demiclosed. That is, whenever { x n } is a sequence in C weakly converging to some xC and the sequence {(IS) x n } strongly converges to some y, it follows that (IS)x=y, where I is the identity operator of H.

Definition 2.3 A nonlinear operator T with the domain D(T)H and the range R(T)H is said to be

  1. (a)

    monotone if

    TxTy,xy0,x,yD(T);
  2. (b)

    β-strongly monotone if there exists a constant β>0 such that

    TxTy,xyη x y 2 ,x,yD(T);
  3. (c)

    ν-inverse strongly monotone if there exists a constant ν>0 such that

    TxTy,xyν T x T y 2 ,x,yD(T).

It is easy to see that the projection P C is 1-inverse strongly monotone. Inverse strongly monotone (also referred to as co-coercive) operators have been applied widely in solving practical problems in various fields, for instance, in traffic assignment problems; see, for example, [35]. It is obvious that if T is ν-inverse strongly monotone, then T is monotone and 1 ν -Lipschitz continuous. Moreover, we also have, for all u,vD(T) and λ>0,

( I λ T ) u ( I λ T ) v 2 = ( u v ) λ ( T u T v ) 2 = u v 2 2 λ T u T v , u v + λ 2 T u T v 2 u v 2 + λ ( λ 2 ν ) T u T v 2 .
(2.1)

So, if λ2ν, then IλT is a nonexpansive mapping.

The metric (or nearest point) projection from H onto C is the mapping P C :HC which assigns to each point xH the unique point P C xC satisfying the property

x P C x= inf y C xy=:d(x,C).

Some important properties of projections are gathered in the following proposition.

Proposition 2.3 For given xH and zC:

  1. (a)

    z= P C xxz,yz0, yC;

  2. (b)

    z= P C x x z 2 x y 2 y z 2 , yC;

  3. (c)

    P C x P C y,xy P C x P C y 2 , yH.

Consequently, P C is nonexpansive and monotone.

Let λ be a number in (0,1] and let μ>0. Associating with a nonexpansive mapping T:CH, we define the mapping T λ :CH by

T λ x:=TxλμF(Tx),xC,

where F:HH is an operator such that, for some positive constants κ,η>0, F is κ-Lipschitzian and η-strongly monotone on H, that is, F satisfies the conditions:

FxFyκxyandFxFy,xyη x y 2

for all x,yH.

Lemma 2.4 [[36], Lemma 3.1]

T λ is a contraction provided 0<μ< 2 η κ 2 , that is,

T λ x T λ y (1λτ)xy,x,yC,

where τ=1 1 μ ( 2 η μ κ 2 ) (0,1].

Lemma 2.5 Let A:CH be a monotone mapping. In the context of the variational inequality problem the characterization of the projection (see Proposition  2.3(a)) implies

uVI(C,A)u= P C (uλAu),λ>0.

Let C be a nonempty closed convex subset of H and Θ:C×CR satisfy the following conditions.

  • (A1) T(x,x)=0, ?x?C;

  • (A2) T is monotone, that is, T(x,y)+T(y,x)=0, ?x,y?C;

  • (A3) T is upper-hemicontinuous, that is, ?x,y,z?C,

    lim?sup t ? 0 + T ( t z + ( 1 - t ) x , y ) =T(x,y);
  • (A4) T(x,·) is convex and lower semicontinuous, for each x?C.

Let φ:CR be a lower semicontinuous and convex function satisfying either (B1) or (B2), where

  • (B1) for each x?H and r>0, there exist a bounded subset D x ?C and y x ?C such that, for any z?C\ D x ,

    T(z, y x )+f( y x )-f(z)+ 1 r y x -z,z-x><0;
  • (B2) C is a bounded set.

Given a positive number r>0. Let T r ( Θ , φ ) :HC be the solution set of the auxiliary mixed equilibrium problem, that is, for each xH,

T r ( Θ , φ ) (x):= { y C : Θ ( y , z ) + φ ( z ) φ ( y ) + 1 r y x , z y 0 , z C } .

Next we list some elementary conclusions for the MEP.

Proposition 2.4 [37]

Assume that Θ:C×CR satisfies (A1)-(A4) and let φ:CR be a proper lower semicontinuous and convex function. Assume that either (B1) or (B2) holds. For r>0 and xH, define a mapping T r ( Θ , φ ) :HC as follows:

T r ( Θ , φ ) (x):= { z C : Θ ( z , y ) + φ ( y ) φ ( z ) + 1 r y z , z x 0 , y C }

for all xH. Then

  1. (i)

    for each xH, T r ( Θ , φ ) (x) is nonempty and single-valued;

  2. (ii)

    T r ( Θ , φ ) is firmly nonexpansive, that is, for any x,yH,

    T r ( Θ , φ ) x T r ( Θ , φ ) y 2 T r ( Θ , φ ) x T r ( Θ , φ ) y , x y ;
  3. (iii)

    Fix( T r ( Θ , φ ) )=MEP(Θ,φ);

  4. (iv)

    MEP(Θ,φ) is closed and convex;

  5. (v)

    T s ( Θ , φ ) x T t ( Θ , φ ) x 2 s t s T s ( Θ , φ ) x T t ( Θ , φ ) x, T s ( Θ , φ ) xx, for all s,t>0 and xH.

We need some facts and tools in a real Hilbert space H which are listed as lemmas below.

Lemma 2.6 Let X be a real inner product space. Then we have the following inequality:

x + y 2 x 2 +2y,x+y,x,yX.

Lemma 2.7 Let H be a real Hilbert space. Then the following hold:

  1. (a)

    x y 2 = x 2 y 2 2xy,y, for all x,yH;

  2. (b)

    λ x + μ y 2 =λ x 2 +μ y 2 λμ x y 2 , for all x,yH and λ,μ[0,1] with λ+μ=1;

  3. (c)

    if { x n } is a sequence in H such that x n x, it follows that

    lim sup n x n y 2 = lim sup n x n x 2 + x y 2 ,yH.

Lemma 2.8 [38]

Let { a n } be a sequence of nonnegative real numbers satisfying the property

a n + 1 (1 s n ) a n + s n b n + t n ,n1,

where { s n }(0,1] and { b n } are such that:

  1. (i)

    n = 1 s n =;

  2. (ii)

    either lim sup n b n 0 or n = 0 | s n b n |<;

  3. (iii)

    n = 1 t n < where t n 0, for all n1.

Then lim n a n =0.

Recall that a set-valued mapping T:D(T)H 2 H is called monotone if, for all x,yD(T), fTx, and gTy imply fg,xy0. A set-valued mapping T is called maximal monotone if T is monotone and (I+λT)D(T)=H, for each λ>0, where I is the identity mapping of H. We denote by G(T) the graph of T. It is well known that a monotone mapping T is maximal if and only if, for (x,f)H×H, fg,xy0 for every (y,g)G(T) implies fTx.

Next we provide an example to illustrate the concept of maximal monotone mapping.

Let A:CH be a monotone, k-Lipschitz-continuous mapping and let N C v be the normal cone to C at vC, that is,

N C v= { u H : v p , u 0 , p C } .

Define

T ˜ v= { A v + N C v , if  v C , , if  v C .

Then T ˜ is maximal monotone (see [16]) such that

0 T ˜ vvVI(C,A).
(2.2)

Let R:D(R)H 2 H be a maximal monotone mapping. Let λ,μ>0 be two positive numbers.

Lemma 2.9 [39]

We have the resolvent identity

J R , λ x= J R , μ ( μ λ x + ( 1 μ λ ) J R , λ x ) ,xH.

Remark 2.1 For λ,μ>0, we have the following relation:

J R , λ x J R , μ yxy+|λμ| ( 1 λ J R , λ x y + 1 μ x J R , μ y ) ,x,yH.
(2.3)

The following property for the resolvent operator J R , λ :H D ( R ) ¯ was considered in [17, 18].

Lemma 2.10 J R , λ is single-valued and firmly nonexpansive, that is,

J R , λ x J R , λ y,xy J R , λ x J R , λ y 2 ,x,yH.

Consequently, J R , λ is nonexpansive and monotone.

Lemma 2.11 [20]

Let R be a maximal monotone mapping with D(R)=C. Then, for any given λ>0, uC is a solution of problem (1.6) if and only if uC satisfies

u= J R , λ (uλBu).

Lemma 2.12 [18]

Let R be a maximal monotone mapping with D(R)=C and let B:CH be a strongly monotone, continuous and single-valued mapping. Then, for each zH, the equation z(B+λR)x has a unique solution x λ for λ>0.

Lemma 2.13 [20]

Let R be a maximal monotone mapping with D(R)=C and B:CH be a monotone, continuous and single-valued mapping. Then (I+λ(R+B))C=H, for each λ>0. In this case, R+B is maximal monotone.

3 Algorithms and convergence results

Let H be a real Hilbert space and f:HR be a function. Then the minimization problem

min x C f(x):= 1 2 A x P Q A x 2

is ill-posed. Xu [40] considered the following Tikhonov’s regularization problem:

min x C f α (x):= 1 2 A x P Q A x 2 + 1 2 α x 2 ,

where α>0 is the regularization parameter. It is clear that the gradient

f α =f+αI= A (I P Q )A+αI

is (α+ A 2 )-Lipschitz continuous.

Throughout the paper, unless otherwise specified, M, N are positive integers and C be a nonempty closed convex subset of a real Hilbert space H.

Algorithm 3.1 The notations and symbols are the same as in Problem 1.2. Start with a given arbitrary x 0 H, and compute a sequence { x n } by

{ u n = T r M , n ( Θ M , φ M ) ( I r M , n A M ) T r M 1 , n ( Θ M 1 , φ M 1 ) ( I r M 1 , n A M 1 ) T r 1 , n ( Θ 1 , φ 1 ) ( I r 1 , n A 1 ) x n , v n = J R N , λ N , n ( I λ N , n B N ) J R N 1 , λ N 1 , n ( I λ N 1 , n B N 1 ) J R 1 , λ 1 , n ( I λ 1 , n B 1 ) u n , y n = β n x n + γ n P C ( I λ n f α n ) v n + σ n T P C ( I λ n f α n ) v n , x n + 1 = ϵ n γ ( δ n V x n + ( 1 δ n ) S x n ) + ( I ϵ n μ F ) y n , n 0 ,
(3.1)

where f α n = α n I+f.

The following result provides the strong convergence of the sequence generated by Algorithm 3.1.

Theorem 3.1 For each k{1,2,,M}, let Θ k :C×CR be a bifunction satisfying conditions (A1)-(A4) and φ k :CR{+} be a proper lower semicontinuous and convex function with restriction (B1) or (B2). For each k{1,2,,M} and i{1,2,,N}, let R i :C 2 H be a maximal monotone mapping and let A k :HH and B i :CH be μ k -inverse strongly monotone and η i -inverse strongly monotone, respectively. Let T:HH be a ξ-strictly pseudocontractive mapping, S:HH be a nonexpansive mapping and V:HH be a ρ-contraction with coefficient ρ[0,1). Let F:HH be κ-Lipschitzian and η-strongly monotone with positive constants κ,η>0 such that 0<γτ and 0<μ< 2 η κ 2 , where τ=1 1 μ ( 2 η μ κ 2 ) . Assume that the solution set Ξ of HVIP (1.7) is nonempty where Ω:=( k = 1 M GMEP( Θ k , φ k , A k ))( i = 1 N I( B i , R i ))Fix(T)Γ. Let { λ n }[a,b](0, 2 A 2 ), { α n }(0,) with n = 0 α n <, { ϵ n },{ δ n },{ β n },{ γ n },{ σ n }(0,1) with β n + γ n + σ n =1, and { λ i , n }[ a i , b i ](0,2 η i ), { r k , n }[ c k , d k ](0,2 μ k ) where i{1,2,,N} and k{1,2,,M}. Suppose that

  • (C1) lim n ? 8 d n =0, lim n ? 8 ? n =0, lim n ? 8 | ? n - ? n - 1 | d n ? n 2 ? n - 1 =0 and ? n = 0 8 ? n d n =8;

  • (C2) ? n = 1 8 | d n - d n - 1 |<8 or lim n ? 8 | d n - d n - 1 |/( d n ? n )=0;

  • (C3) ? n = 1 8 | ß n - ß n - 1 | ? n <8 or lim n ? 8 | ß n - ß n - 1 |/( d n ? n 2 )=0;

  • (C4) ? n = 1 8 | ? n - ? n - 1 | ? n <8 or lim n ? 8 | ? n - ? n - 1 |/( d n ? n 2 )=0;

  • (C5) ? n = 1 8 | ? n - ? n - 1 | ? n <8 or lim n ? 8 | ? n - ? n - 1 |/( d n ? n 2 )=0;

  • (C6) ? n = 1 8 | ? n a n - ? n - 1 a n - 1 | ? n <8 or lim n ? 8 | ? n a n - ? n - 1 a n - 1 |/( d n ? n 2 )=0;

  • (C7) { ß n }?[c,d]?(0,1), ( ? n + s n )?= ? n and lim?inf n ? 8 s n >0;

  • (C8) for each i=1,2,,N, ? n = 1 8 | ? i , n - ? i , n - 1 | ? n <8 or lim n ? 8 | ? i , n - ? i , n - 1 |/( d n ? n 2 )=0;

  • (C9) for each k=1,2,,M, ? n = 1 8 | r k , n - r k , n - 1 | ? n <8 or lim n ? 8 | r k , n - r k , n - 1 |/( d n ? n 2 )=0;

  • (C10) there exist positive constants ?, k ¯ >0 such that lim n ? 8 ? n 1 / ? / d n =0 and ? x n -T x n ?= k ¯ [ d ( x n , O ) ] ? , ?x?C for sufficiently large n=0.

If { x n } is a sequence generated by Algorithm 3.1 and {S x n } is bounded, then

  1. (a)

    x n + 1 x n =o( ϵ n );

  2. (b)

    ω w ( x n )Ω;

  3. (c)

    { x n } converges strongly to a point x Ω provided x n y n + α n =o( ϵ n 2 ), which is the unique solution of Problem 1.2.

Proof First of all, taking into account Ξ, we know that Ω. Observe that

μ η τ μ η 1 1 μ ( 2 η μ κ 2 ) 1 μ ( 2 η μ κ 2 ) 1 μ η 1 2 μ η + μ 2 κ 2 1 2 μ η + μ 2 η 2 κ 2 η 2 κ η

and

( μ F γ V ) x ( μ F γ V ) y , x y = μ F x F y , x y γ V x V y , x y μ η x y 2 γ ρ x y 2 = ( μ η γ ρ ) x y 2 , x , y H .

Since τγ>0 and κη, we deduce that μητγ>γρ and hence the mapping μFγV is (μηγρ)-strongly monotone. Moreover, it is clear that the mapping μFγV is (μκ+γρ)-Lipschitzian. Thus, there exists a unique solution x in Ξ to the VIP

( μ F γ V ) x , p x 0,pΞ,

that is, { x }=VI(Ξ,μFγV). Now, we put

Δ n k = T r k , n ( Θ k , φ k ) (I r k , n A k ) T r k 1 , n ( Θ k 1 , φ k 1 ) (I r k 1 , n A k 1 ) T r 1 , n ( Θ 1 , φ 1 ) (I r 1 , n A 1 ) x n

for all k{1,2,,M} and n0,

Λ n i = J R i , λ i , n (I λ i , n B i ) J R i 1 , λ i 1 , n (I λ i 1 , n B i 1 ) J R 1 , λ 1 , n (I λ 1 , n B 1 )

for all i{1,2,,N}, Δ n 0 =I, and Λ n 0 =I, where I is the identity mapping on H. Then we have u n = Δ n M x n and v n = Λ n N u n .

Now, we show that P C (Iλ f α ) is ζ-averaged, for each λ(0, 2 α + A 2 ), where

ζ= 2 + λ ( α + A 2 ) 4 (0,1).

Indeed, it is easy to see that f= A (I P Q )A is 1 A 2 -ism, that is,

f ( x ) f ( y ) , x y 1 A 2 f ( x ) f ( y ) 2 .

Observe that

( α + A 2 ) f α ( x ) f α ( y ) , x y = ( α + A 2 ) [ α x y 2 + f ( x ) f ( y ) , x y ] = α 2 x y 2 + α f ( x ) f ( y ) , x y + α A 2 x y 2 + A 2 f ( x ) f ( y ) , x y α 2 x y 2 + 2 α f ( x ) f ( y ) , x y + f ( x ) f ( y ) 2 = α ( x y ) + f ( x ) f ( y ) 2 = f α ( x ) f α ( y ) 2 .

Hence, it follows that f α =αI+ A (I P Q )A is 1 α + A 2 -ism. Thus, by Proposition 2.1(b), λ f α is 1 λ ( α + A 2 ) -ism. From Proposition 2.1(c), the complement Iλ f α is λ ( α + A 2 ) 2 -averaged. Therefore, noting that P C is 1 2 -averaged and utilizing Proposition 2.2(d), we see that, for each λ(0, 2 α + A 2 ), P C (Iλ f α ) is ζ-averaged with

ζ= 1 2 + λ ( α + A 2 ) 2 1 2 λ ( α + A 2 ) 2 = 2 + λ ( α + A 2 ) 4 (0,1).

This shows that P C (Iλ f α ) is nonexpansive. Taking into account that { λ n }[a,b](0, 2 A 2 ) and α n 0, we get

lim sup n 2 + λ n ( α n + A 2 ) 4 2 + b A 2 4 <1.

Without loss of generality, we may assume that ζ n := 2 + λ n ( α n + A 2 ) 4 <1, for each n0. So, P C (I λ n f α n ) is nonexpansive, for each n0. Similarly, since

lim sup n λ n ( α n + A 2 ) 2 b A 2 2 <1,

it may be confirmed that I λ n f α n is nonexpansive, for each n0.

We divide the rest of the proof into several steps.

Step 1. We prove that { x n } is bounded.

Indeed, take a fixed pΩ arbitrarily. Utilizing (2.1) and Proposition 2.4(b), we have

u n p = T r M , n ( Θ M , φ M ) ( I r M , n B M ) Δ n M 1 x n T r M , n ( Θ M , φ M ) ( I r M , n B M ) Δ n M 1 p ( I r M , n B M ) Δ n M 1 x n ( I r M , n B M ) Δ n M 1 p Δ n M 1 x n Δ n M 1 p Δ n 0 x n Δ n 0 p = x n p .
(3.2)

Utilizing (2.1) and Lemma 2.10, we have

v n p = J R N , λ N , n ( I λ N , n A N ) Λ n N 1 u n J R N , λ N , n ( I λ N , n A N ) Λ n N 1 p ( I λ N , n A N ) Λ n N 1 u n ( I λ N , n A N ) Λ n N 1 p Λ n N 1 u n Λ n N 1 p Λ n 0 u n Λ n 0 p = u n p .
(3.3)

Combining (3.2) and (3.3), we have

v n p x n p.
(3.4)

For simplicity, put t n = P C (I λ n f α n ) v n , for each n0. Note that P C (Iλf)p=p for λ(0, 2 A 2 ). Hence, from (3.4), it follows that

t n p = P C ( I λ n f α n ) v n P C ( I λ n f ) p P C ( I λ n f α n ) v n P C ( I λ n f α n ) p + P C ( I λ n f α n ) p P C ( I λ n f ) p v n p + ( I λ n f α n ) p ( I λ n f ) p = v n p + λ n α n p x n p + λ n α n p .
(3.5)

Since T is a ξ-strictly pseudocontractive mapping and ( γ n + σ n )ξ γ n , for all n0, by Lemma 2.2, we obtain from (3.1) and (3.5) that

y n p = β n x n + γ n t n + σ n T t n p = β n ( x n p ) + γ n ( t n p ) + σ n ( T t n p ) β n x n p + γ n ( t n p ) + σ n ( T t n p ) β n x n p + ( γ n + σ n ) t n p β n x n p + ( γ n + σ n ) [ x n p + λ n α n p ] β n x n p + ( γ n + δ n ) x n p + λ n α n p = x n p + λ n α n p .
(3.6)

Noticing the boundedness of {S x n }, we get sup n 0 γS x n μFp M ˆ for some M ˆ >0. Moreover, utilizing Lemma 2.4 and (3.1), (3.6), we deduce that { λ n }[a,b](0, 2 A 2 ) and 0<γτ that, for all n0,

x n + 1 p = ϵ n γ ( δ n V x n + ( 1 δ n ) S x n ) + ( I ϵ n μ F ) y n p = ϵ n γ ( δ n V x n + ( 1 δ n ) S x n ) ϵ n μ F p + ( I ϵ n μ F ) y n ( I ϵ n μ F ) p ϵ n γ ( δ n V x n + ( 1 δ n ) S x n ) ϵ n μ F p + ( I ϵ n μ F ) y n ( I ϵ n μ F ) p = ϵ n δ n ( γ V x n μ F p ) + ( 1 δ n ) ( γ S x n μ F p ) + ( I ϵ n μ F ) y n ( I ϵ n μ F ) p ϵ n [ δ n γ V x n μ F p + ( 1 δ n ) γ S x n μ F p ] + ( 1 ϵ n τ ) y n p ϵ n [ δ n ( γ V x n γ V p + γ V p μ F p ) + ( 1 δ n ) M ˆ ] + ( 1 ϵ n τ ) y n p ϵ n [ δ n γ ρ x n p + δ n γ V p μ F p + ( 1 δ n ) M ˆ ] + ( 1 ϵ n τ ) [ x n p + λ n α n p ] ϵ n [ δ n γ ρ x n p + max { M ˆ , γ V p μ F p } ] + ( 1 ϵ n τ ) [ x n p + λ n α n p ] ϵ n γ ρ x n p + ϵ n max { M ˆ , γ V p μ F p } + ( 1 ϵ n τ ) x n p + λ n α n p = [ 1 ( τ γ ρ ) ϵ n ] x n p + ϵ n max { M ˆ , γ V p μ F p } + λ n α n p = [ 1 ( τ γ ρ ) ϵ n ] x n p + ( τ γ ρ ) ϵ n max { M ˆ τ γ ρ , γ V p μ F p τ γ ρ } + λ n α n p max { x n p , M ˆ τ γ ρ , γ V p μ F p τ γ ρ } + α n b p .

By induction, we get

x n + 1 pmax { x 0 p , M ˆ τ γ ρ , γ V p μ F p τ γ ρ } + j = 0 n α j bp,n0.

Thus, { x n } is bounded since n = 0 α n <, and so are the sequences { t n }, { u n }, { v n }, and { y n }.

Step 2. We prove that lim n x n + 1 x n ϵ n =0.

Indeed, utilizing (2.1) and (2.3), we obtain

v n + 1 v n = Λ n + 1 N u n + 1 Λ n N u n = J R N , λ N , n + 1 ( I λ N , n + 1 B N ) Λ n + 1 N 1 u n + 1 J R N , λ N , n ( I λ N , n B N ) Λ n N 1 u n J R N , λ N , n + 1 ( I λ N , n + 1 B N ) Λ n + 1 N 1 u n + 1 J R N , λ N , n + 1 ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 + J R N , λ N , n + 1 ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 J R N , λ N , n ( I λ N , n B N ) Λ n N 1 u n ( I λ N , n + 1 B N ) Λ n + 1 N 1 u n + 1 ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 + ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 ( I λ N , n B N ) Λ n N 1 u n + | λ N , n + 1 λ N , n | × ( 1 λ N , n + 1 J R N , λ N , n + 1 ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 ( I λ N , n B N ) Λ n N 1 u n + 1 λ N , n ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 J R N , λ N , n ( I λ N , n B N ) Λ n N 1 u n ) | λ N , n + 1 λ N , n | ( B N Λ n + 1 N 1 u n + 1 + M ˜ ) + Λ n + 1 N 1 u n + 1 Λ n N 1 u n | λ N , n + 1 λ N , n | ( B N Λ n + 1 N 1 u n + 1 + M ˜ ) + | λ N 1 , n + 1 λ N 1 , n | ( B N 1 Λ n + 1 N 2 u n + 1 + M ˜ ) + Λ n + 1 N 2 u n + 1 Λ n N 2 u n | λ N , n + 1 λ N , n | ( B N Λ n + 1 N 1 u n + 1 + M ˜ ) + | λ N 1 , n + 1 λ N 1 , n | ( B N 1 Λ n + 1 N 2 u n + 1 + M ˜ ) + + | λ 1 , n + 1 λ 1 , n | ( B 1 Λ n + 1 0 u n + 1 + M ˜ ) + Λ n + 1 0 u n + 1 Λ n 0 u n M ˜ 0 i = 1 N | λ i , n + 1 λ i , n | + u n + 1 u n ,
(3.7)

where

sup n 0 { 1 λ N , n + 1 J R N , λ N , n + 1 ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 ( I λ N , n B N ) Λ n N 1 u n + 1 λ N , n ( I λ N , n B N ) Λ n + 1 N 1 u n + 1 J R N , λ N , n ( I λ N , n B N ) Λ n N 1 u n } M ˜

for some M ˜ >0 and sup n 0 { i = 1 N B i Λ n + 1 i 1 u n + 1 + M ˜ } M ˜ 0 for some M ˜ 0 >0.

Utilizing Proposition 2.4(b), (e), we deduce that

u n + 1 u n = Δ n + 1 M x n + 1 Δ n M x n = T r M , n + 1 ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 T r M , n ( Θ M , φ M ) ( I r M , n A M ) Δ n M 1 x n T r M , n + 1 ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 T r M , n ( Θ M , φ M ) ( I r M , n A M ) Δ n + 1 M 1 x n + 1 + T r M , n ( Θ M , φ M ) ( I r M , n A M ) Δ n + 1 M 1 x n + 1 T r M , n ( Θ M , φ M ) ( I r M , n A M ) Δ n M 1 x n T r M , n + 1 ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 T r M , n ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 + T r M , n ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 T r M , n ( Θ M , φ M ) ( I r M , n A M ) Δ n + 1 M 1 x n + 1 + ( I r M , n A M ) Δ n + 1 M 1 x n + 1 ( I r M , n A M ) Δ n M 1 x n | r M , n + 1 r M , n | r M , n + 1 T r M , n + 1 ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 + | r M , n + 1 r M , n | A M Δ n + 1 M 1 x n + 1 + Δ n + 1 M 1 x n + 1 Δ n M 1 x n = | r M , n + 1 r M , n | [ A M Δ n + 1 M 1 x n + 1 + 1 r M , n + 1 T r M , n + 1 ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 ] + Δ n + 1 M 1 x n + 1 Δ n M 1 x n | r M , n + 1 r M , n | [ A M Δ n + 1 M 1 x n + 1 + 1 r M , n + 1 T r M , n + 1 ( Θ M , φ M ) ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 ( I r M , n + 1 A M ) Δ n + 1 M 1 x n + 1 ] + + | r 1 , n + 1 r 1 , n | [ A 1 Δ n + 1 0 x n + 1 + 1 r 1 , n + 1 T r 1 , n + 1 ( Θ 1 , φ 1 ) ( I r 1 , n + 1 A 1 ) Δ n + 1 0 x n + 1 ( I r 1 , n + 1 A 1 ) Δ n + 1 0 x n + 1 ] + Δ n + 1 0 x n + 1 Δ n 0 x n M ˜ 1 k = 1 M | r k , n + 1 r k , n | + x n + 1 x n ,
(3.8)

where M ˜ 1 >0 is a constant such that, for each n0,

k = 1 M [ A k Δ n + 1 k 1 x n + 1 + 1 r k , n + 1 T r k , n + 1 ( Θ k , φ k ) ( I r k , n + 1 A k ) Δ n + 1 k 1 x n + 1 ( I r k , n + 1 A k ) Δ n + 1 k 1 x n + 1 ] M ˜ 1 .

Furthermore, we define y n = β n x n +(1 β n ) w n for all n0. It follows that

w n + 1 w n = y n + 1 β n + 1 x n + 1 1 β n + 1 y n β n x n 1 β n = γ n + 1 t n + 1 + σ n + 1 T t n + 1 1 β n + 1 γ n t n + σ n T t n 1 β n = γ n + 1 ( t n + 1 t n ) + σ n + 1 ( T t n + 1 T t n ) 1 β n + 1 + ( γ n + 1 1 β n + 1 γ n 1 β n ) t n + ( σ n + 1 1 β n + 1 σ n 1 β n ) T t n .
(3.9)

Since T is a ξ-strictly pseudocontractive mapping and ( γ n + σ n )ξ γ n , for all n0, by Lemma 2.2, we obtain

γ n + 1 ( t n + 1 t n ) + σ n + 1 ( T t n + 1 T t n ) ( γ n + 1 + σ n + 1 ) t n + 1 t n .
(3.10)

Also, utilizing the nonexpansivity of P C (I λ n f α n ), we have

t n + 1 t n = P C ( I λ n + 1 f α n + 1 ) v n + 1 P C ( I λ n f α n ) v n P C ( I λ n + 1 f α n + 1 ) v n + 1 P C ( I λ n + 1 f α n + 1 ) v n + P C ( I λ n + 1 f α n + 1 ) v n P C ( I λ n f α n ) v n v n + 1 v n + ( I λ n + 1 f α n + 1 ) v n ( I λ n f α n ) v n v n + 1 v n + | λ n + 1 α n + 1 λ n α n | v n + | λ n + 1 λ n | f ( v n ) .
(3.11)

Hence, from (3.7)-(3.11), it follows that

w n + 1 w n γ n + 1 ( t n + 1 t n ) + σ n + 1 ( T t n + 1 T t n ) 1 β n + 1 + | γ n + 1 1 β n + 1 γ n 1 β n | t n + | σ n + 1 1 β n + 1 σ n 1 β n | T t n ( γ n + 1 + σ n + 1 ) t n + 1 t n 1 β n + 1 + | γ n + 1 1 β n + 1 γ n 1 β n | t n + | σ n + 1 1 β n + 1 σ n 1 β n | T t n = t n + 1 t n + | γ n + 1 1 β n + 1 γ n 1 β n | ( t n + T t n ) v n + 1 v n + | λ n + 1 α n + 1 λ n α n | v n + | λ n + 1 λ n | f ( v n ) + | γ n + 1 1 β n + 1 γ n 1 β n | ( t n + T t n ) M ˜ 0 i = 1 N | λ i , n + 1 λ i , n | + u n + 1 u n + | λ n + 1 α n + 1 λ n α n | v n + | λ n + 1 λ n | f ( v n ) + | γ n + 1 1 β n + 1 γ n 1 β n | ( t n + T t n ) M ˜ 0 i = 1 N | λ i , n + 1 λ i , n | + M ˜ 1 k = 1 M | r k , n + 1 r k , n | + x n + 1 x n + | λ n + 1 α n + 1 λ n α n | v n + | λ n + 1 λ n | f ( v n ) + | γ n + 1 1 β n + 1 γ n 1 β n | ( t n + T t n ) .
(3.12)

In the meantime, simple calculation shows that

y n + 1 y n = β n ( x n + 1 x n )+(1 β n )( w n + 1 w n )+( β n + 1 β n )( x n + 1 w n + 1 ).

So, it follows from (3.12) that

y n + 1 y n β n x n + 1 x n + ( 1 β n ) w n + 1 w n + | β n + 1 β n | x n + 1 w n + 1 β n x n + 1 x n + ( 1 β n ) [ M ˜ 0 i = 1 N | λ i , n + 1 λ i , n | + M ˜ 1 k = 1 M | r k , n + 1 r k , n | + x n + 1 x n + | γ n + 1 1 β n + 1 γ n 1 β n | ( t n + T t n ) + | λ n + 1 α n + 1 λ n α n | v n + | λ n + 1 λ n | f ( v n ) ] + | β n + 1 β n | x n + 1 w n + 1 x n + 1 x n + M ˜ 0 i = 1 N | λ i , n + 1 λ i , n | + M ˜ 1 k = 1 M | r k , n + 1 r k , n | + | γ n + 1 γ n | ( 1 β n ) + γ n | β n + 1 β n | 1 β n + 1 ( t n + T t n ) + | λ n + 1 α n + 1 λ n α n | v n + | λ n + 1 λ n | f ( v n ) + | β n + 1 β n | x n + 1 w n + 1 x n + 1 x n + M ˜ 0 i = 1 N | λ i , n + 1 λ i , n | + M ˜ 1 k = 1 M | r k , n + 1 r k , n | + | γ n + 1 γ n | t n + T t n 1 d + | β n + 1 β n | ( x n + 1 w n + 1 + t n + T t n 1 d ) + | λ n + 1 α n + 1 λ n α n | v n + | λ n + 1 λ n | f ( v n ) x n + 1 x n + M ˜ 2 ( i = 1 N | λ i , n + 1 λ i , n | + k = 1 M | r k , n + 1 r k , n | + | γ n + 1 γ n | + | β n + 1 β n | + | λ n + 1 α n + 1 λ n α n | + | λ n + 1 λ n | ) ,
(3.13)

where sup n 0 { x n + 1 w n + 1 + t n + T t n 1 d + v n +f( v n )+ M ˜ 0 + M ˜ 1 } M ˜ 2 for some M ˜ 2 >0.

On the other hand, we define z n := δ n V x n +(1 δ n )S x n , for all n0. Then it is well known that x n + 1 = ϵ n γ z n +(I ϵ n μF) y n , for all n0. Simple calculations show that

{ z n + 1 z n = ( δ n + 1 δ n ) ( V x n S x n ) + δ n + 1 ( V x n + 1 V x n ) z n + 1 z n = + ( 1 δ n + 1 ) ( S x n + 1 S x n ) , x n + 2 x n + 1 = ( ϵ n + 1 ϵ n ) ( γ z n μ F y n ) + ϵ n + 1 γ ( z n + 1 z n ) x n + 2 x n + 1 = + ( I λ n + 1 μ F ) y n + 1 ( I λ n + 1 μ F ) y n .

Since V is a ρ-contraction with coefficient ρ[0,1) and S is a nonexpansive mapping, we conclude that

z n + 1 z n | δ n + 1 δ n | V x n S x n + δ n + 1 V x n + 1 V x n + ( 1 δ n + 1 ) S x n + 1 S x n | δ n + 1 δ n | V x n S x n + δ n + 1 ρ x n + 1 x n + ( 1 δ n + 1 ) x n + 1 x n = ( 1 δ n + 1 ( 1 ρ ) ) x n + 1 x n + | δ n + 1 δ n | V x n S x n ,

which together with (3.13) and 0<γτ implies that

x n + 2 x n + 1 | ϵ n + 1 ϵ n | γ z n μ F y n + ϵ n + 1 γ z n + 1 z n + ( I ϵ n + 1 μ F ) y n + 1 ( I ϵ n + 1 μ F ) y n | ϵ n + 1 ϵ n | γ z n μ F y n + ϵ n + 1 γ z n + 1 z n + ( 1 ϵ n + 1 τ ) y n + 1 y n | ϵ n + 1 ϵ n | γ z n μ F y n + ϵ n + 1 γ [ ( 1 δ n + 1 ( 1 ρ ) ) x n + 1 x n + | δ n + 1 δ n | V x n S x n ] + ( 1 ϵ n + 1 τ ) [ x n + 1 x n + M ˜ 2 ( i = 1 N | λ i , n + 1 λ i , n | + k = 1 M | r k , n + 1 r k , n | + | γ n + 1 γ n | + | β n + 1 β n | + | λ n + 1 α n + 1 λ n α n | + | λ n + 1 λ n | ) ] ( 1 ϵ n + 1 ( τ γ ) ϵ n + 1 δ n + 1 ( 1 ρ ) γ ) x n + 1 x n + | ϵ n + 1 ϵ n | γ z n μ F y n + ϵ n + 1 | δ n + 1 δ n | V x n S x n + M ˜ 2 ( i = 1 N | λ i , n + 1 λ i , n | + k = 1 M | r k , n + 1 r k , n | + | γ n + 1 γ n | + | β n + 1 β n | + | λ n + 1 α n + 1 λ n α n | + | λ n + 1 λ n | ) ( 1 ϵ n + 1 δ n + 1 ( 1 ρ ) γ ) x n + 1 x n + M ˜ 3 { i = 1 N | λ i , n + 1 λ i , n | + k = 1 M | r k , n + 1 r k , n | + | ϵ n + 1 ϵ n | + ϵ n + 1 | δ n + 1 δ n | + | β n + 1 β n | + | γ n + 1 γ n | + | λ n + 1 α n + 1 λ n α n | + | λ n + 1 λ n | } ,

where sup n 0 {γ z n μF y n +V x n S x n + M ˜ 2 } M ˜ 3 for some M ˜ 3 >0. Consequently,

x n + 1 x n ϵ n ( 1 ϵ n δ n ( 1 ρ ) γ ) x n x n 1 ϵ n + M ˜ 3 { i = 1 N | λ i , n λ i , n 1 | ϵ n + k = 1 M | r k , n r k , n 1 | ϵ n + | ϵ n ϵ n 1 | ϵ n + | δ n δ n 1 | + | β n β n 1 | ϵ n + | γ n γ n 1 | ϵ n + | λ n α n λ n 1 α n 1 | ϵ n + | λ n λ n 1 | ϵ n } = ( 1 ϵ n δ n ( 1 ρ ) γ ) x n x n 1 ϵ n 1 + ( 1 ϵ n δ n ( 1 ρ ) γ ) x n x n 1 ( 1 ϵ n 1 ϵ n 1 ) + M ˜ 3 { i = 1 N | λ i , n λ i , n 1 | ϵ n + k = 1 M | r k , n r k , n 1 | ϵ n + | ϵ n ϵ n 1 | ϵ n + | δ n δ n 1 | + | β n β n 1 | ϵ n + | γ n γ n 1 | ϵ n + | λ n α n λ n 1 α n 1 | ϵ n + | λ n λ n 1 | ϵ n } ( 1 ϵ n δ n ( 1 ρ ) γ ) x n x n 1 ϵ n 1 + ϵ n δ n ( 1 ρ ) γ M ˜ 4 ( 1 ρ ) γ { | ϵ n ϵ n 1 | δ n ϵ n 2 ϵ n 1 + i = 1 N | λ i , n λ i , n 1 | δ n ϵ n 2 + k = 1 M | r k , n r k , n 1 | δ n ϵ n 2 + | ϵ n ϵ n 1 | δ n ϵ n 2 + | δ n δ n 1 | δ n ϵ n + | β n β n 1 | δ n ϵ n 2 + | γ n γ n 1 | δ n ϵ n 2 + | λ n α n λ n 1 α n 1 | δ n ϵ n 2 + | λ n λ n 1 | δ n ϵ n 2 } ,
(3.14)

where sup n 1 { x n x n 1 + M ˜ 3 } M ˜ 4 for some M ˜ 4 >0. Utilizing Lemma 2.8, we conclude from conditions (C1)-(C6) and (C8)-(C9) that n = 0 ϵ n δ n (1ρ)γ= and

lim n x