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Constructed nets with perturbations for equilibrium and fixed point problems

Abstract

In this paper, an implicit net with perturbations for solving the mixed equilibrium problems and fixed point problems has been constructed and it is shown that the proposed net converges strongly to a common solution of the mixed equilibrium problems and fixed point problems. Also, as applications, some corollaries for solving the minimum-norm problems are also included.

MSC:47J05, 47J25, 47H09.

1 Introduction

The present paper is devoted to solving the following mixed equilibrium problem: Find uC such that

F(u,v)+Au,vu0
(1.1)

for all vC, where C is a nonempty closed convex subset of a real Hilbert space H, F:C×CR is a bifunction and A:CH is a nonlinear operator. The solution set of (1.1) is denoted by S(MEP).

This problem (1.1) includes optimization problems, variational inequalities, minimax problems, and Nash equilibrium problems in noncooperative games as special cases.

Case 1. If A=0 in (1.1), then (1.1) reduces to the following equilibrium problem: Find uC such that

F(u,v)0
(1.2)

for all vC. The solution of (1.2) is denoted by S(EP).

Case 2. If F=0 in (1.1), then (1.1) reduces to the variational inequality problem: Find zC such that

Au,vu0
(1.3)

for all vC. The solution of (1.2) is denoted by S(VI).

In the literature, there are a large number of references associated with some equilibrium problems and variational inequality problems (see, for instance, [132]).

The main purpose of the present paper is to construct the following implicit net with perturbations for solving the mixed equilibrium (1.1) and the fixed point problem:

F( z t ,y)+ 1 λ y z t , z t ( t u t + ( 1 t ) T z t λ A T z t ) 0

for all yC. Also, it is shown that the proposed net { z t } converges strongly to a common solution of the mixed equilibrium problems and fixed point problems. As applications, some corollaries for solving the minimum-norm problems are also included.

2 Preliminaries

Let H be a real Hilbert space with an inner product , and a norm , respectively, and C be a nonempty closed convex subset of a real Hilbert space H.

  1. (1)

    A mapping T:CC is said to be nonexpansive if

    TuTvuv

for all u,vC. F(T) denotes the set of fixed points of T.

  1. (2)

    A mapping A:CH is said to be α-inverse-strongly monotone if there exists a positive real number α>0 such that

    AuAv,uvα A u A v 2

for all u,vC. It is clear that any α-inverse-strongly monotone mapping is monotone and 1 α -Lipschitz continuous.

Throughout this paper, we assume that a bifunction F:C×CR satisfies the following conditions:

(C1) F(u,u)=0 for all uC;

(C2) F is monotone, i.e., F(u,v)+F(v,u)0 for all u,vC;

(C3) for each u,v,wC, lim t 0 F(tw+(1t)u,v)F(u,v);

(C4) for each uC, vF(u,v) is convex and lower semicontinuous.

In fact, some efforts to construct the algorithms for solving the equilibrium problems have been carried out. For instance, Moudafi [15] presented an iterative algorithm and proved a weak convergence theorem for solving the mixed equilibrium problem (1.1). Takahashi and Takahashi [24] constructed the following iterative algorithm:

{ F ( z n , y ) + A x n , y z n + 1 λ n y z n , z n x n 0 , x n + 1 = β n x n + T ( α n u + ( 1 β n ) z n )
(2.1)

for all yC and n0, where T:CC is a nonexpansive mapping. They proved that the sequence { x n } generated by (2.1) converges strongly to z= Proj F ( T ) S ( MEP ) (u).

Plubtieng and Punpaeng [20] introduced and considered the following two iterative schemes for finding a common element of the set of solutions of the problem (1.2) and the set of fixed points of a nonexpansive mapping in a Hilbert space H.

Implicit iterative algorithm { x n }:

{ F ( u n , y ) + 1 r n y u n , u n x n 0 , x n = α n γ f ( x n ) + ( I α n A ) T u n
(2.2)

for all yH and n1.

Explicit iterative algorithm { x n }:

{ F ( u n , y ) + 1 r n y u n , u n x n 0 , x n + 1 = α n γ f ( x n ) + ( I α n A ) T u n
(2.3)

for all yH and n1.

They proved that, under certain conditions, the sequences { x n } generated by (2.2) and (2.3) converge strongly to the unique solution of the variational inequality:

( A γ f ) z , x z 0

for all xF(T)S(EP), which is the optimality condition for the minimization problem:

min x F ( T ) S ( EP ) 1 2 Ax,xh(x),

where h is a potential function for γf.

We know that there are perturbations always occurring in the iterative processes because the manipulations are inaccurate. Recently, Chuang et al. ([8]) constructed the following iteration process with perturbations for finding a common element of the set of solutions of the equilibrium problem and the set of fixed points for a quasi-nonexpansive mapping with perturbation: q 1 H and

{ x n C such that F ( x n , y ) + 1 λ n y x n , x n q n 0 , q n + 1 = α n u n + ( 1 α n ) ( β n x n + ( 1 β n ) T x n )

for all yC and n0. They shown that the sequence { q n } converges strongly to Proj F ( T ) S ( EP ) .

Now, we need the following useful lemmas for our main results.

Lemma 2.1 [11]

Let C be a nonempty closed convex subset of a real Hilbert space H. Let F:C×CR be a bifunction which satisfies the conditions (C1)-(C4). Let r>0 and xH. Then there exists zC such that

F(z,y)+ 1 r yz,zx0

for all yC. Further, if

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

then the following hold:

  1. (1)

    T r is single-valued and T r is firmly nonexpansive, i.e., for any x,yH,

    T r x T r y 2 T r x T r y,xy;
  2. (2)

    S(EP) is closed and convex and S(EP)=F( T r ).

Lemma 2.2 [24]

Let C, H, F, and T r x be as in Lemma  2.1. Then the following holds:

T s x T t x 2 s t s T s x T t x, T s xx

for all s,t>0 and xH.

Lemma 2.3 [24]

Let C be a nonempty closed convex subset of a real Hilbert space H. Let a mapping A:CH be α-inverse-strongly monotone and r>0 be a constant. Then we have

( I r A ) x ( I r A ) y 2 x y 2 +r(r2α) A x A y 2

for all x,yC. In particular, if 0r2α, then IrA is nonexpansive.

Lemma 2.4 [33]

Let C be a closed convex subset of a real Hilbert space H and let T:CC be a nonexpansive mapping. Then the mapping IT is demiclosed, that is, if { x n } is a sequence in C such that x n u weakly and (IT) x n v strongly, then (IT)u=v.

3 Convergence results

In this section, first, we give our main results.

Part I: Assumptions on the setting of C, F, A, and T:

(A1) C is a nonempty closed convex subset of a real Hilbert space H;

(A2) F:C×CR is a bifunction satisfying the conditions (C1)-(C4);

(A3) A:CH is an α-inverse-strongly monotone mapping;

(A4) T:CC is a nonexpansive mapping.

Part II: Parameter restrict:

λ is a constant satisfying aλb, where [a,b](0,2α).

Part III: Perturbations:

{ u t }H is a net satisfying lim t 0 + u t =uH.

Algorithm 3.1 For any t(0,1 λ 2 α ), define a net { z t }C by the implicit manner:

F( z t ,y)+ 1 λ y z t , z t ( t u t + ( 1 t ) T z t λ A T z t ) 0
(3.1)

for all yC.

Remark 3.2 We show that the net { z t } is well defined. Next, we prove that (3.1) can be rewritten as

z t = T λ ( t u t + ( 1 t ) T z t λ A T z t )
(3.2)

for all t(0,1 λ 2 α ).

In fact, for any t(0,1 λ 2 α ), u t H, and xH, we find z such that, for all yC,

F(z,y)+ 1 λ y z , z ( t u t + ( 1 t ) T x λ A T x ) 0.

From Lemma 2.1, we get immediately

z= T λ ( t u t + ( 1 t ) T x λ A T x ) .

Now, we can define a mapping

ϑ t := T λ ( t u t + ( 1 t ) T λ A T )

for all t(0,1 λ 2 α ). Again, from Lemma 2.1, we know that T λ is nonexpansive. Thus, for any x,yC, we have

ϑ t x ϑ t y = T λ ( t u t + ( 1 t ) T x λ A T x ) T λ ( t u t + ( 1 t ) T y λ A T y ) ( t u t + ( 1 t ) T x λ A T x ) ( t u t + ( 1 t ) T y λ A T y ) = ( 1 t ) ( I λ 1 t A ) T x ( I λ 1 t A ) T y .
(3.3)

From Lemma 2.3, I λ 1 t A is nonexpansive for all t(0,1 λ 2 α ). Note that T is also nonexpansive. By (3.3), we deduce

ϑ t x ϑ t y(1t)xy

for all x,yC. This indicates that ϑ t is a contraction on C and so it has a unique fixed point, denoted by z t , in C. That is, z t = T λ (t u t +(1t)T z t λAT z t ). Hence { z t } is well defined.

Theorem 3.3 Suppose that F(T)S(MEP). Then the net { z t } defined by (3.1) converges strongly as t0+ to P F ( T ) S ( MEP ) (u).

Proof Pick up zF(T)S(MEP). It is obvious that z= T λ (zλAz) for all λ>0. So, we have

z=Tz= T λ (zλAz)= T λ (TzλATz)= T λ ( t T z + ( 1 t ) ( T z λ 1 t A T z ) )

for all t(0,1 λ 2 α ). Then we have

z t z 2 = T λ ( t u t + ( 1 t ) T z t λ A T z t ) z 2 = T λ ( t u t + ( 1 t ) ( T z t λ 1 t A T z t ) ) T λ ( t z + ( 1 t ) ( T z λ 1 t A T z ) ) 2 ( t u t + ( 1 t ) ( T z t λ 1 t A T z t ) ) ( t z + ( 1 t ) ( T z λ 1 t A T z ) ) 2 = ( 1 t ) ( ( T z t λ 1 t A T z t ) ( T z λ 1 t A T z ) ) + t ( u t z ) 2 .
(3.4)

Using the convexity of and the inverse-strong monotonicity of A, we derive

( 1 t ) ( ( T z t λ 1 t A T z t ) ( T z λ 1 t A T z ) ) + t ( u t z ) 2 ( 1 t ) ( T z t λ 1 t A T z t ) ( T z λ 1 t A T z ) 2 + t u t z 2 = ( 1 t ) ( T z t T z ) λ ( A T z t A T z ) / ( 1 t ) 2 + t u t z 2 = ( 1 t ) ( T z t T z 2 2 λ 1 t A T z t A T z , T z t T z + λ 2 ( 1 t ) 2 A T z t A T z 2 ) + t u t z 2 ( 1 t ) ( T z t T z 2 2 α λ 1 t A T z t A T z 2 + λ 2 ( 1 t ) 2 A T z t A T z 2 ) + t u t z 2 = ( 1 t ) ( T z t T z 2 + λ ( 1 t ) 2 ( λ 2 ( 1 t ) α ) A T z t A T z 2 ) + t u t z 2 ( 1 t ) ( z t z 2 + λ ( 1 t ) 2 ( λ 2 ( 1 t ) α ) A T z t A T z 2 ) + t u t z 2 .
(3.5)

By the assumption, we have λ2(1t)α0 for all t(0,1 λ 2 α ). Then, from (3.4) and (3.5), it follows that

z t z 2 ( 1 t ) ( z t z 2 + λ ( 1 t ) 2 ( λ 2 ( 1 t ) α ) A T z t A T z 2 ) + t u t z 2 ( 1 t ) z t z 2 + t u t z 2
(3.6)

and so

z t z u t z.
(3.7)

Since lim t 0 + u t =u, there exists a positive constant M>0 such that sup t { u t }M. Then, from (3.7), we deduce that { z t } is bounded. Hence {T z t } and {AT z t } are also bounded.

From (3.4) and (3.5), we obtain

z t z 2 (1t) z t z 2 + λ ( 1 t ) ( λ 2 ( 1 t ) α ) A T z t A z 2 +t u t z 2

and so

λ ( 1 t ) ( 2 ( 1 t ) α λ ) A T z t A z 2 t u t z 2 0.

This implies that

lim t 0 + AT z t Az=0.
(3.8)

Next, we show z t T z t 0. Since T λ is firmly nonexpansive (see Lemma 2.1), we have

z t z 2 = T λ ( t u t + ( 1 t ) T z t λ A T z t ) z 2 = T λ ( t u t + ( 1 t ) T z t λ A T z t ) T λ ( T z λ A T z ) 2 t u t + ( 1 t ) T z t λ A T z t ( T z λ A T z ) , z t z = 1 2 ( t u t + ( 1 t ) T z t λ A T z t ( T z λ A T z ) 2 + z t z 2 t u t + ( 1 t ) T z t λ ( A T z t λ A T z ) z t 2 ) .

Since IλA/(1t) is nonexpansive, we have

t u t + ( 1 t ) T z t λ A T z t ( T z λ A T z ) 2 = ( 1 t ) ( ( T z t λ A T z t / ( 1 t ) ) ( T z λ A T z / ( 1 t ) ) ) + t ( u t z ) 2 ( 1 t ) ( T z t λ A T z t / ( 1 t ) ) ( T z λ A T z / ( 1 t ) ) 2 + t u t z 2 ( 1 t ) T z t T z 2 + t u t z 2 ( 1 t ) z t z 2 + t u t z 2 .

Thus we have

z t z 2 1 2 ( ( 1 t ) z t z 2 + t u t z 2 + z t z 2 t u t + ( 1 t ) T z t z t λ ( A T z t A T z ) 2 ) .

It follows that

0 t u t z 2 t u t + ( 1 t ) T z t z t λ ( A T z t A T z ) 2 = t u t z 2 t u t + ( 1 t ) T z t z t 2 + 2 λ t u t + ( 1 t ) T z t z t , A T z t A T z λ 2 A T z t A T z 2 t u t z 2 t u t + ( 1 t ) T z t z t 2 + 2 λ t u t + ( 1 t ) T z t z t A T z t A T z

and so

t u t + ( 1 t ) T z t z t 2 t u t z 2 +2λ t u t + ( 1 t ) T z t z t AT z t Az.

Since AT z t Az0 by (3.8), we deduce

lim t 0 + t u t + ( 1 t ) T z t z t =0.

Therefore, we have

lim t 0 + z t T z t =0.
(3.9)

From (3.4), it follows that

z t z 2 ( 1 t ) ( ( T z t λ 1 t A T z t ) ( T z λ 1 t A T z ) ) + t ( u t z ) 2 = ( 1 t ) 2 ( T z t λ 1 t A T z t ) ( T z λ 1 t A T z ) 2 + 2 t ( 1 t ) u t z , ( T z t λ 1 t A T z t ) ( T z λ 1 t A T z ) + t 2 u t z 2 ( 1 t ) 2 z t z 2 + 2 t ( 1 t ) u t z , T z t λ 1 t ( A T z t A z ) z + t 2 u t z 2 = ( 1 2 t ) z t z 2 + 2 t { ( 1 t ) u t z , T z t z λ 1 t ( A T z t A z ) + t 2 ( u t z 2 + z t z 2 ) } ,

which implies that

z t z 2 u t z , T z t z λ 1 t ( A T z t A z ) + t 2 ( u t z 2 + z t z 2 ) + t u t z T z t z λ 1 t ( A T z t A z ) z u , z T z t + λ 1 t u t z A T z t A z + ( t + u t u ) M 1 ,
(3.10)

where M 1 is a constant such that

sup { u t z 2 + z t z 2 + u t z T z t z λ 1 t ( A T z t A z ) : t ( 0 , 1 λ 2 α ) } M 1 .

Next, we show that { z t } is relatively norm-compact as t0+. Assume that { t n }(0,1) is a sequence such that t n 0+ as n. Put z n := z t n . From (3.10), it follows that

z n z 2 z u , z T z n + λ 1 t n u n z A T z n A z + ( t n + u n u ) M 1
(3.11)

for all zF(T)S(MEP). Since { z n } is bounded, without loss of generality, we may assume that z n x ˜ C. From (3.9), we have

lim n z n T z n =0.
(3.12)

We can use Lemma 2.4 to (3.12) to deduce x ˜ F(T). Further, we show that x ˜ is also in S(MEP). Since z n = T λ ( t n u n +(1 t n )T z n λAT z n ) for any yC, we have

F( z n ,y)+AT z n ,y z n + 1 λ y z n , z n ( t n u n + ( 1 t n ) T z n ) 0.

From (C2), it follows that

AT z n ,y z n + 1 λ y z n , z n ( t n u n + ( 1 t n ) T z n ) F(y, z n ).
(3.13)

Put x t =ty+(1t) x ˜ for all t(0,1 λ 2 α ) and yC. Then we have x t C and so, from (3.13), it follows that

x t z n , A x t x t z n , A x t x t z n , A T z n 1 λ x t z n , z n ( t n u n + ( 1 t n ) T z n ) + F ( x t , z n ) = x t z n , A x t A z n + x t z n , A z n A T z n 1 λ x t z n , z n ( t n u n + ( 1 t n ) T z n ) + F ( x t , z n ) .

Since z n T z n 0, we have A z n AT z n 0. Further, since A is monotone, we have x t z n ,A x t A z n 0. So, from (C4), it follows that, as n,

x t x ˜ ,A x t F( x t , x ˜ ).
(3.14)

Also, it follows from (C1), (C4), and (3.14) that

0 = F ( x t , x t ) t F ( x t , y ) + ( 1 t ) F ( x t , x ˜ ) t F ( x t , y ) + ( 1 t ) x t x ˜ , A x t = t F ( x t , y ) + ( 1 t ) t y x ˜ , A x t

and hence

0F( x t ,y)+(1t)y x ˜ ,A x t .

Letting t0, we have

0F( x ˜ ,y)+y x ˜ ,A x ˜

for all yC. This implies x ˜ EP. Therefore, we can substitute x ˜ for z in (3.8) to get

z n x ˜ 2 x ˜ u , x ˜ T z n + λ 1 t n u n x ˜ A T z n A x ˜ + ( t n + u n x ˜ ) M 1

for all x ˜ F(T)S(MEP). By (3.5), we know that AT z n Az0 for any zF(T)S(MEP). Then we get AT z n A x ˜ 0. Consequently, the weak convergence of { z n } (and {T z n }) to x ˜ actually implies that z n x ˜ . This proves the relative norm-compactness of the net { z t } as t0+.

Now, we return to (3.11) and take the limit as n to get

x ˜ z 2 zu,z x ˜

for all zF(T)S(MEP). Equivalently, we have

u x ˜ ,z x ˜ 0

for all zF(T)S(MEP). This clearly implies that

x ˜ = P F ( T ) S ( MEP ) (u).

Therefore, x ˜ is the unique cluster point of the net { z t }. Hence the whole net { z t } converges strongly to x ˜ = P F ( T ) S ( MEP ) (u). This completes the proof. □

4 Induced algorithms and corollaries

  1. (I)

    Taking T=I in (3.1), we get the following.

Algorithm 4.1 For any t(0,1 λ 2 α ), define a net { z t }C by the implicit manner:

F( z t ,y)+ 1 λ y z t , z t ( t u t + ( 1 t ) z t λ A z t ) 0
(4.1)

for all yC.

Corollary 4.2 Suppose that S(MEP). Then the net { z t } defined by (4.1) converges strongly as t0+ to P S ( MEP ) (u).

  1. (II)

    Taking F=0 in (4.1), we get the following.

Algorithm 4.3 For any t(0,1 λ 2 α ), define a net { z t }C by the implicit manner:

y z t , z t ( t u t + ( 1 t ) z t λ A z t ) 0
(4.2)

for all yC.

Corollary 4.4 Suppose that S(VI). Then the net { z t } defined by (4.2) converges strongly as t0+ to P S ( VI ) (u).

  1. (III)

    Taking A=0 in (4.1), we get the following.

Algorithm 4.5 For any t(0,1), define a net { z t }C by the implicit manner:

F( z t ,y)+ t λ y z t , z t u t 0
(4.3)

for all yC.

Corollary 4.6 Suppose that S(EP). Then the net { z t } defined by (4.3) converges strongly as t0+ to P S ( EP ) (u).

5 Minimum-norm solutions

In many problems, one needs to find a solution with the minimum norm. In an abstract way, we may formulate such problems as finding a point x with the property:

x C, x 2 = min x C x 2 ,

where C is a nonempty closed convex subset of a real Hilbert space H. In other words, x is the (nearest point or metric) projection of the origin onto C, that is,

x = P C (0),

where P C is the metric (or nearest point) projection from H onto C.

A typical example is the least-squares solution of the constrained linear inverse problem:

{ A x = b , x C ,

where A is a bounded linear operator from H to another real Hilbert space H 1 and b is a given point in H 1 . The least-squares solution is the least-norm minimizer of the minimization problem:

min x C A x b 2 .

Motivated by the above least-squares solution of the constrained linear inverse problems, we study the general case of finding the minimum-norm solutions for the mixed equilibrium problem (1.1), the equilibrium problem (1.2), the variational inequality (1.3), and the fixed point problem.

Now, we state our algorithms which can be inducted from the above section.

  1. (I)

    Taking u t =0 for all t in (3.1), we get the following.

Algorithm 5.1 For any t(0,1 λ 2 α ), define a net { z t }C by the implicit manner:

F( z t ,y)+ 1 λ y z t , z t ( ( 1 t ) T z t λ A T z t ) 0
(5.1)

for all yC.

Corollary 5.2 Suppose that F(T)S(MEP). Then the net { z t } defined by (5.1) converges strongly as t0+ to P F ( T ) S ( MEP ) (0), which is the minimum-norm element in F(T)S(MEP).

  1. (II)

    Taking u t =0 for all t in (4.1), we get the following.

Algorithm 5.3 For any t(0,1 λ 2 α ), define a net { z t }C by the implicit manner:

F( z t ,y)+ 1 λ y z t , z t ( ( 1 t ) z t λ A z t ) 0
(5.2)

for all yC.

Corollary 5.4 Suppose that S(MEP). Then the net { z t } defined by (5.2) converges strongly as t0+ to P S ( MEP ) (0), which is the minimum-norm element in S(MEP).

  1. (III)

    Taking u t =0 for all t in (4.2), we get the following.

Algorithm 5.5 For any t(0,1 λ 2 α ), define a net { z t }C by the implicit manner:

y z t , z t ( ( 1 t ) z t λ A z t ) 0
(5.3)

for all yC.

Corollary 5.6 Suppose that S(VI). Then the net { z t } defined by (5.3) converges strongly as t0+ to P S ( VI ) (0), which is the minimum-norm element in S(VI).

  1. (IV)

    Taking A=0 in (5.1), we get the following.

Algorithm 5.7 For any t(0,1), define a net { z t }C by the implicit manner:

F( z t ,y)+ 1 λ y z t , z t ( 1 t ) T z t 0
(5.4)

for all yC.

Corollary 5.8 Suppose that F(T)S(EP). Then the net { z t } defined by (5.4) converges strongly as t0+ to P F ( T ) S ( EP ) (0), which is the minimum-norm element in F(T)S(EP).

  1. (V)

    Taking A=0 in (5.2), we get the following.

Algorithm 5.9 For any t(0,1), define a net { z t }C by the implicit manner:

F( z t ,y)+ t λ y z t , z t 0
(5.5)

for all yC.

Corollary 5.10 Suppose that S(EP). Then the net { z t } defined by (5.5) converges strongly as t0+ to P S ( EP ) (0), which is the minimum-norm element in S(EP).

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Acknowledgements

The first author was supported in part by NSFC 11071279 and NSFC 71161001-G0105, the second author was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number: NRF-2013053358) and the third author was supported in part by NSC 101-2628-E-230-001-MY3 and NSC 101-2622-E-230-005-CC3.

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Yao, Y., Cho, Y.J., Liou, YC. et al. Constructed nets with perturbations for equilibrium and fixed point problems. J Inequal Appl 2014, 334 (2014). https://doi.org/10.1186/1029-242X-2014-334

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Keywords

  • equilibrium problem
  • fixed point problem
  • minimization problem
  • nonexpansive mapping