# Proximal algorithms for a class of mixed equilibrium problems

- Yisheng Song
^{1, 2}Email author and - Qingnian Zhang
^{3}

**2012**:166

https://doi.org/10.1186/1687-1812-2012-166

© Song and Zhang; licensee Springer 2012

**Received: **21 February 2012

**Accepted: **17 September 2012

**Published: **2 October 2012

## Abstract

We present two proximal algorithms for solving the mixed equilibrium problems. Under some simpler framework, the strong and weak convergence of the sequences defined by two general algorithms is respectively obtained. In particular, we deal with several iterative schemes in a united way and apply our algorithms for solving the classical equilibrium problem, the minimization problem, the classical variational inequality problem and the generalized variational inequality problem. Our results properly include some corresponding results in this field as a special case.

**MSC:**47H06, 47J05, 47J25, 47H10, 90C33, 90C25, 49M45, 65C25, 49J40, 65J15, 47H09.

## Keywords

## 1 Introduction

*H*is a real Hilbert space with inner product $\u3008\cdot ,\cdot \u3009$ and induced norm $\parallel \cdot \parallel $. Let

*K*be a nonempty closed convex subset of

*H*, $F:K\times K\to \mathbb{R}$ be a bifunction and $\phi :K\to \mathbb{R}\cup \{+\mathrm{\infty}\}$ be a proper generalized real valued function, where $\mathbb{R}$ is the set of real numbers. Our interest is in finding a solution to the following problem which is referred to as the

*mixed equilibrium problem*(for short, MEP) for

*F*,

*φ*

*equilibrium problem*(for short, EP) for

*F*when $\phi \equiv 0$

The set $MEP(F,0)$ of solutions of EP (1.2) is denoted by $EP(F)$.

*minimization problem*(for short, MP) for a function

*φ*

and $MEP(0,\phi )$ is denoted by $Argmin(\phi )$.

*i.e.*, EP (1.2) turns into a classical

*variational inequality problem*(for short, VIP) for

*T*

*generalized variational inequality problem*(for short, GVIP) for a mapping

*T*and a function

*φ*

and $VIP(T,K)=GVIP(T,0,K)$.

The mixed equilibrium problem (MEP) is very interesting because it covers mathematical programs and optimization problems over equilibrium constraints, hierarchical minimization problems, variational inequality, complementarity problems, monotone inclusion problems, saddle point problems, Nash equilibria in noncooperative games as well as certain fixed point problems. In other words, the mixed equilibrium problem unifies several problems arising from engineering, physics, statistics, computer science, optimization theory, operation research, economics and others. The interest of this problem is that it unites all these particular problems in a convenient way. Moreover, many methods devoted to solving one of these problems can be extended, with suitable modifications, to solving the mixed equilibrium problem (MEP).

In the last 20 years or so, many mathematical works have been devoted to studying the method for finding an approximate solution of $EP(F)$ with various types of additional conditions. Moudafi [1] extended the proximal method to monotone equilibrium problems and Konnov [2] used the proximal method to solve equilibrium problems with weakly monotone bifunctions. In 2005, Combettes and Hirstoaga [3] introduced an iterative scheme of finding the best approximation to the initial data when $EP(F)$ is nonempty and they also proved a strong convergence theorem under the condition ${lim}_{n\to \mathrm{\infty}}{r}_{n}=+\mathrm{\infty}$. Song and Zheng [4] and Song, Kang and Cho [5] considered the convergence of a Halpern-type iteration for $EP(F)$. The bundle methods and extragradient methods were extended to equilibrium problems in [6] and [7]. In 2007, Takahashi and Takahashi [8] introduced the viscosity approximation method for finding a common element of the set of solutions of an equilibrium problem $EP(F)$ and the set of fixed points of a nonexpansive mapping *T* under the conditions ${lim\hspace{0.17em}inf}_{n\to \mathrm{\infty}}{r}_{n}=+\mathrm{\infty}$ and ${\sum}_{n=0}^{+\mathrm{\infty}}\parallel {r}_{n}-{r}_{n+1}\parallel <+\mathrm{\infty}$. Subsequently, the above viscosity approximation method was studied by different mathematicians using varying environmental conditions; see references [9–13] for details. Recently, Moudafi [14] showed weak convergence of proximal methods for a class of bilevel monotone equilibrium problems. For other solution methods regarding the equilibrium problems see [2, 15–22].

In this paper, our main objective is to show the mixed equilibrium problem (1.1) can be solved by two very simple proximal methods under simpler conditions, where a bifunction *F* and a function *φ* satisfy the following standard assumptions.

**Condition 1.1** The function $\phi :K\to \mathbb{R}\cup \{+\mathrm{\infty}\}$ is a proper lower semicontinuous convex function and the bifunction $F:K\times K\to \mathbb{R}$ satisfies the following:

(A1) $F(x,x)=0$ for all $x\in K$;

(A2) *F* is monotone, *i.e.*, $F(x,y)+F(y,x)\le 0$ for all $x,y\in K$;

(A3) for each $x,y,z\in K$, ${lim\hspace{0.17em}sup}_{t\to {0}^{+}}F(tz+(1-t)x,y)\le F(x,y)$;

(A4) for each $x\in K$, $y\mapsto F(x,y)$ is convex and lower semicontinuous.

To this end, we introduce two proximal algorithms.

**Algorithm 1**For any anchor $u\in K$ and initialization ${x}_{0}\in K$, if ${x}_{n}$ ($n\ge 0$) is the current iterate and ${\alpha}_{n}$, ${r}_{n}$ are the current parameters, then ${x}_{n+1}\in K$ is defined iteratively by

**Algorithm 2**For any initialization ${x}_{0}\in K$, if ${x}_{n}$ ($n\ge 0$) is the current iterate and ${\alpha}_{n}$, ${r}_{n}$ are the current parameters, then ${x}_{n+1}\in K$ is given iteratively by

Some other proximal methods and the related strong and weak convergence results can be derived from our main theorem by particularizing the bifunction *F*, the function *H* and the parameters ${\alpha}_{n}$, ${r}_{n}$. Our results treat several iterative schemes in a united way and properly include some results of [3–5, 8, 20, 21, 23, 24] as a special case.

The rest of the paper is organized as follows. In Section 2, we introduce some necessary lemmas and results, and show our algorithms are well defined. In Section 3, we study the strong convergence of a sequence iteratively given by Algorithm 1. In Section 4, we deal with the weak convergence of an iterative scheme defined by Algorithm 2. Some other proximal methods and the related results derived from our main theorems and some concluding remarks can be found in Section 5.

## 2 Preliminaries and basic results

*H*. We write ${x}_{n}\rightharpoonup x$ to indicate that the sequence $\{{x}_{n}\}$ weakly converges to

*x*; as usual, ${x}_{n}\to x$ will symbolize strong convergence. Let $F(T)=\{x\in H;Tx=x\}$ be the set of all fixed points for a mapping

*T*. In a real Hilbert space

*H*, we have

*K*be a nonempty closed convex subset of

*H*. Then for any $x\in H$, there exists a unique nearest point in

*K*, denoted by ${P}_{K}(x)$, such that

*H*onto

*K*. We know that ${P}_{K}$ is firmly nonexpansive,

*i.e.*, for any $x,y\in H$,

For a bifunction $\mathrm{\Psi}:K\times K\to \mathbb{R}$, we have the following lemmas which were also given in [17].

**Lemma 2.1** (Blum-Oettli [[17], Corollary 1])

*Let*

*K*

*be a nonempty closed convex subset of*

*H*

*and*Ψ

*be a bifunction of*$K\times K$

*into*$\mathbb{R}$

*satisfying*(A1)-(A4)

*in Condition*1.1.

*Let*$r>0$

*and*$x\in H$.

*Then there exists*$z\in K$

*such that*

Using a similar proof technique of Combettes-Hirstoaga [[3], Lemma 2.12], also see Ceng and Yao [[15], Lemma 3.1], Peng, Liou and Yao [[16], Lemma 2.2] obtained the following lemma which guarantees that our algorithms are well defined.

**Lemma 2.2** ([[16], Lemma 2.2])

*Let*

*K*

*be a nonempty closed convex subset of*

*H*.

*Suppose that the bifunction*$F:K\times K\to \mathbb{R}$

*and the function*$\phi :K\to \mathbb{R}\cup \{+\mathrm{\infty}\}$

*satisfy Condition*1.1.

*For*$r>0$

*and*$x\in H$,

*define a mapping*${T}_{r}:H\to {2}^{K}$

*as follows*:

*Then the following hold*:

- (1)
*the domain of*${T}_{r}$*is**H*. - (2)${T}_{r}$
*is single*-*valued and firmly nonexpansive*,*i*.*e*.,*for any*$x,y\in H$,${\parallel {T}_{r}(x)-{T}_{r}(y)\parallel}^{2}\le \u3008{T}_{r}(x)-{T}_{r}(y),x-y\u3009$

*or equivalently*,

- (3)
$F({T}_{r})=MEP(F,\phi )$.

- (4)
$MEP(F,\phi )$

*is closed and convex*.

Lemma 2.3 was showed and used by several authors. For detailed proofs, see Liu [25] and Xu [26, 27]. Furthermore, a variant of Lemma 2.3 has already been used by Reich in [[28], Theorem 1].

**Lemma 2.3**

*Let*$\{{a}_{n}\}$

*be a sequence of nonnegative real numbers satisfying the property*

*where* $\{{t}_{n}\}$ *and* $\{{c}_{n}\}$ *satisfy the restrictions* ${\sum}_{n=0}^{\mathrm{\infty}}{t}_{n}=\mathrm{\infty}$ *and* ${lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{c}_{n}\le 0$. *Then* $\{{a}_{n}\}$ *converges to zero as* $n\to \mathrm{\infty}$.

## 3 Strong convergence of Algorithm 1

In this section, we deal with an iterative scheme given by Algorithm 1 for finding an element of the set of solutions of the MEP (1.1) in a Hilbert space.

**Theorem 3.1**

*Let*

*K*

*be a nonempty closed convex subset of a real Hilbert space*

*H*.

*Assume that a bifunction*

*F*

*and a function*

*φ*

*satisfy Condition*1.1

*and*$MEP(F,\phi )\ne \mathrm{\varnothing}$.

*For any anchor*$u\in K$

*and initialization*${x}_{0}\in K$,

*let*$\{{x}_{n}\}$

*and*$\{{u}_{n}\}$

*be two sequences generated iteratively by*

*If only*$\{{\alpha}_{n}\}\subset (0,1)$

*and*$\{{r}_{n}\}\subset (0,+\mathrm{\infty})$

*satisfy*

*then* $\{{x}_{n}\}$ *converge strongly to some element* ${x}^{\ast}={P}_{MEP(F,\phi )}(u)$ *of* $MEP(F,\phi )$.

*Proof*At first, we show that $\{{x}_{n}\}$ and $\{{u}_{n}\}$ are bounded. Taking $p\in MEP(F,\phi )$, it follows from Lemma 2.2 that ${u}_{n}={T}_{{r}_{n}}({x}_{n})$ and

Thus, $\{{x}_{n}\}$ is bounded, and hence so is $\{{u}_{n}\}$.

*n*, we also have

where $M={\parallel u-{x}^{\ast}\parallel}^{2}$. Following the proof technique in Mainge [[29], Lemma 3.2, Theorem 3.1], the proof may be divided into two cases.

*n*by ${n}_{k}$, we obtain

*t*with $0<t\le 1$ and $y\in K$, let ${y}_{t}=ty+(1-t)x$. Since $y\in K$ and $x\in K$, we have ${y}_{t}\in K$, and hence $0\ge F({y}_{t},x)+\phi (x)-\phi ({y}_{t})$. So, by virtue of (A1), (A4) of the bifunction

*F*and convexity of the function

*φ*, we get

*F*and lower-semicontinuity of the function

*φ*, we have

So, an application of Lemma 2.3 onto (3.5) yields ${x}_{n}\to {x}^{\ast}$.

*n*large enough,

*n*(${n}_{k}$) is replaced by $\tau (n)$ ($\tau ({n}_{k})$, respectively), we have

Now, it follows from (3.6) that ${\mathrm{\Gamma}}_{n}=\parallel {x}_{n}-{x}^{\ast}\parallel \to 0$. The proof is completed. □

## 4 Weak convergence of Algorithm 2

In this section, we show a weak convergence theorem which solves the MEP (1.1) in a Hilbert space.

**Theorem 4.1**

*Let*

*K*

*be a nonempty closed convex subset of a real Hilbert space*

*H*.

*Assume that a bifunction*

*F*

*and a function*

*φ*

*satisfy Condition*1.1

*and*$MEP(F,\phi )\ne \mathrm{\varnothing}$.

*For any initialization*${x}_{0}\in K$,

*if*${x}_{n}$ ($n\ge 0$)

*is the current iterate and positive real numbers*${\alpha}_{n}$, ${r}_{n}$

*are the current parameters*,

*then*${x}_{n+1}\in K$

*is given iteratively by*

*Assume that*$\{{\alpha}_{n}\}\subset (0,1)$

*satisfies*(C4) ${lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{\alpha}_{n}<1$.

*Then*

*If*, *in addition*, $\{{r}_{n}\}\subset (0,+\mathrm{\infty})$ *satisfies* (C3) ${lim\hspace{0.17em}inf}_{n\to \mathrm{\infty}}{r}_{n}>0$, *then* $\{{x}_{n}\}$ *and* $\{{u}_{n}\}$ *converge weakly to some element* *x* *of* $MEP(F,\phi )$.

*Proof*Take $p\in MEP(F,\phi )$. It follows from Lemma 2.2 that ${u}_{n}={T}_{{r}_{n}}({x}_{n})$ and

So, $\{{x}_{n}\}$ and $\{{u}_{n}\}$ are bounded. Moreover, the limit ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}-p\parallel $ exists for each $p\in MEP(F,\phi )$.

*n*,

*N*sufficiently large such that $0<{\alpha}_{n}\le a<1$ for all $n>N$, which implies $(1-a){\parallel {x}_{n}-{u}_{n}\parallel}^{2}\le (1-{\alpha}_{n}){\parallel {x}_{n}-{u}_{n}\parallel}^{2}$. Consequently,

Therefore, the limit ${lim}_{n\to \mathrm{\infty}}\parallel {u}_{n}-p\parallel $ exists for each $p\in MEP(F,\phi )$ since $\parallel {x}_{n}-p\parallel -\parallel {x}_{n}-{u}_{n}\parallel \le \parallel {u}_{n}-p\parallel \le \parallel {x}_{n}-p\parallel $.

By properties of Hilbert spaces, the boundedness of $\{{u}_{n}\}$ means that the sequence $\{{u}_{n}\}$ is weak compact in *H*, and hence there exists a subsequence $\{{u}_{{n}_{k}}\}$ of $\{{u}_{n}\}$ such that ${u}_{{n}_{k}}\rightharpoonup x$. Then using the same argument as in the proof of Theorem 3.1 by means of (4.3) and the condition (C3) together with the properties of the bifunction *F* and the function *φ*, we must have $x\in MEP(F,\phi )$.

*x*of the sequence $\{{u}_{n}\}$, that is, ${u}_{n}\rightharpoonup x$. This fact can be reached from the Opial property of

*H*and the existence of ${lim}_{n\to \mathrm{\infty}}\parallel {u}_{n}-p\parallel $ for each $p\in MEP(F,\phi )$. The proof is presented here for completeness. Indeed, let

*z*be another weak cluster point of $\{{u}_{n}\}$, write as ${u}_{{n}_{i}}\rightharpoonup z$. Then we also have $z\in MEP(F,\phi )$. Let us show that $x=z$. For

*x*,

*z*, we have

Adding the above equations, we must have $2{\parallel x-z\parallel}^{2}=0$, and so $x=z$. This implies ${u}_{n}\rightharpoonup x\in MEP(F,\phi )$. By (4.3), it is obvious that $\{{x}_{n}\}$ also converges weakly to *x*. The desired results are reached. □

## 5 Deduced results and some remarks

*K*be a nonempty closed convex subset of a real Hilbert space

*H*. We recall that a mapping $T:K\to H$ is said to be

*monotone*if

A continuous monotone mapping has the following properties which were given in Nilsrakoo and Saejung [23] and Combettes and Hirstoaga [3] independently.

**Lemma 5.1** (Combettes and Hirstoaga [[3], Lemma 2.15], Nilsrakoo and Saejung [[23], Lemmas 19, 20])

*Let*

*K*

*be a nonempty closed convex subset of a real Hilbert space*

*H*.

*Let*

*T*

*be a continuous monotone mapping of*

*K*

*into*

*H*.

*Define a bifunction*$F:K\times K\to \mathbb{R}$

*as follows*:

*Then the following hold*:

- (i)
*F**satisfies*(A1)-(A4)*in Condition*1.1*and*$VIP(K,T)=EP(F)$; - (ii)
*for*$x\in H$, $z\in K$*and*$r>0$,$F(z,y)+\frac{1}{r}\u3008y-z,z-x\u3009\ge 0,\phantom{\rule{1em}{0ex}}\mathrm{\forall}y\in K\phantom{\rule{1em}{0ex}}\iff \phantom{\rule{1em}{0ex}}z={P}_{K}(x-rTz).$

Using Theorems 3.1 and 4.1 along with Lemma 5.1, we have the following theorems which solve GVIP (1.5) and VIP (1.4) under the simpler framework.

**Theorem 5.2**

*Let*

*K*

*be a nonempty closed convex subset of*

*H*

*and*

*T*

*be a continuous monotone mapping of*

*K*

*such that*$VIP(K,T)\ne \mathrm{\varnothing}$.

*For any anchor*$u\in K$

*and initialization*${x}_{0}\in K$,

- (1)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}{u}_{n}={P}_{K}({x}_{n}-{r}_{n}T{u}_{n}),\hfill \\ {x}_{n+1}={\alpha}_{n}u+(1-{\alpha}_{n}){u}_{n}.\hfill \end{array}$(5.1)

*Assume that*${\alpha}_{n}\in (0,1)$

*and*${r}_{n}\in (0,+\mathrm{\infty})$

*satisfy*

*Then*$\{{x}_{n}\}$

*converges strongly to*${x}^{\ast}\in VIP(K,T)$,

*which is the nearest point from*

*K*

*to*$VIP(K,T)$;

- (2)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}{u}_{n}={P}_{K}({x}_{n}-{r}_{n}T{u}_{n}),\hfill \\ {x}_{n+1}={\alpha}_{n}{x}_{n}+(1-{\alpha}_{n}){u}_{n},\hfill \end{array}$(5.2)

*where* ${r}_{n}\in (0,+\mathrm{\infty})$ *and* ${\alpha}_{n}\in (0,1)$ *satisfy* (C3) *and* (C4) ${lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{\alpha}_{n}<1$. *Then* $\{{x}_{n}\}$ *converges weakly to some* ${x}^{\ast}\in VIP(K,T)$.

*Proof*Let $F(x,y)=\u3008Tx,y-x\u3009$ for all $x,y\in K$. It follows from Lemma 5.1 that two iteration schemes (5.1) and (5.2) respectively turn into

Following Theorems 3.1 and 4.1 ($\phi \equiv 0$), the desired results are proved. □

**Theorem 5.3**

*Let*

*K*

*be a nonempty closed convex subset of*

*H*,

*φ*

*be a convex and lower semicontinuous function from*

*K*

*to*$\mathbb{R}$,

*and*

*T*

*be a continuous monotone mapping of*

*K*

*such that*$GVIP(K,\phi ,T)\ne \mathrm{\varnothing}$.

*For any anchor*$u\in K$

*and initialization*${x}_{0}\in K$,

- (1)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}\u3008T{u}_{n},y-{u}_{n}\u3009+\phi (y)-\phi ({u}_{n})+\frac{1}{{r}_{n}}\u3008y-{u}_{n},{u}_{n}-{x}_{n}\u3009\ge 0,\hfill \\ {x}_{n+1}={\alpha}_{n}u+(1-{\alpha}_{n}){u}_{n},\phantom{\rule{1em}{0ex}}\mathrm{\forall}y\in K,n\ge 0,\hfill \end{array}$(5.3)

*where*${\alpha}_{n}\in (0,1)$

*and*${r}_{n}\in (0,+\mathrm{\infty})$

*satisfy*(C1), (C2)

*and*(C3).

*Then*$\{{x}_{n}\}$

*converges strongly to*${x}^{\ast}\in GVIP(K,\phi ,T)$,

*which is the nearest point from*

*K*

*to*$GVIP(K,\phi ,T)$;

- (2)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}\u3008T{u}_{n},y-{u}_{n}\u3009+\phi (y)-\phi ({u}_{n})+\frac{1}{{r}_{n}}\u3008y-{u}_{n},{u}_{n}-{x}_{n}\u3009\ge 0,\hfill \\ {x}_{n+1}={\alpha}_{n}{x}_{n}+(1-{\alpha}_{n}){u}_{n},\phantom{\rule{1em}{0ex}}\mathrm{\forall}y\in K,n\ge 0,\hfill \end{array}$(5.4)

*where* ${r}_{n}\in (0,+\mathrm{\infty})$ *and* ${\alpha}_{n}\in (0,1)$ *satisfy* (C3) *and* (C4). *Then* $\{{x}_{n}\}$ *converges weakly to some* ${x}^{\ast}\in GVIP(K,\phi ,T)$.

*Proof* Let $F(x,y)=\u3008Tx,y-x\u3009$ for all $x,y\in K$. It follows from Lemma 5.1, Theorems 3.1 and 4.1, the desired results are obtained. □

For the classical equilibrium problem (1.2), the following is obvious ($\phi \equiv 0$ in Theorems 3.1 and 4.1).

**Theorem 5.4**

*Let*

*K*

*be a nonempty closed convex subset of*

*H*

*and*

*F*

*be a bifunction from*$K\times K$

*to*$\mathbb{R}$

*such that*(A1)-(A4)

*of Condition*1.1

*and*$EP(F)\ne \mathrm{\varnothing}$.

*For any anchor*$u\in K$

*and initialization*${x}_{0}\in K$,

- (1)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}F({u}_{n},y)+\frac{1}{{r}_{n}}\u3008y-{u}_{n},{u}_{n}-{x}_{n}\u3009\ge 0,\phantom{\rule{1em}{0ex}}\mathrm{\forall}y\in K,\hfill \\ {x}_{n+1}={\alpha}_{n}u+(1-{\alpha}_{n}){u}_{n},\phantom{\rule{1em}{0ex}}n\ge 0,\hfill \end{array}$(5.5)

*where*${\alpha}_{n}\in (0,1)$

*and*${r}_{n}\in (0,+\mathrm{\infty})$

*satisfy*(C1), (C2)

*and*(C3).

*Then*$\{{x}_{n}\}$

*converges strongly to*${x}^{\ast}\in EP(F)$,

*which is the nearest point from*

*K*

*to*$EP(F)$;

- (2)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}F({u}_{n},y)+\frac{1}{{r}_{n}}\u3008y-{u}_{n},{u}_{n}-{x}_{n}\u3009\ge 0,\phantom{\rule{1em}{0ex}}\mathrm{\forall}y\in K,\hfill \\ {x}_{n+1}={\alpha}_{n}{x}_{n}+(1-{\alpha}_{n}){u}_{n},\phantom{\rule{1em}{0ex}}n\ge 0,\hfill \end{array}$(5.6)

*where* ${r}_{n}\in (0,+\mathrm{\infty})$ *and* ${\alpha}_{n}\in (0,1)$ *satisfy* (C3) *and* (C4). *Then* $\{{x}_{n}\}$ *converges weakly to some* ${x}^{\ast}\in EP(F)$.

When $F\equiv 0$ in Theorems 3.1 and 4.1, we also have better approximated algorithm about solving the minimization problem (1.3) of a function *φ*.

**Theorem 5.5**

*Let*

*K*

*be a nonempty closed convex subset of*

*H*

*and*

*φ*

*be a convex and lower*-

*continuous function from*

*K*

*to*$\mathbb{R}$

*such that*$Argmin(\phi )\ne \mathrm{\varnothing}$.

*For any anchor*$u\in K$

*and initialization*${x}_{0}\in K$,

- (1)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}\phi (y)-\phi ({u}_{n})+\frac{1}{{r}_{n}}\u3008y-{u}_{n},{u}_{n}-{x}_{n}\u3009\ge 0,\phantom{\rule{1em}{0ex}}\mathrm{\forall}y\in K,\hfill \\ {x}_{n+1}={\alpha}_{n}u+(1-{\alpha}_{n}){u}_{n},\phantom{\rule{1em}{0ex}}n\ge 0,\hfill \end{array}$(5.7)

*where*${\alpha}_{n}\in (0,1)$

*and*${r}_{n}\in (0,+\mathrm{\infty})$

*satisfy*(C1), (C2)

*and*(C3).

*Then*$\{{x}_{n}\}$

*converges strongly to*${x}^{\ast}\in Argmin(\phi )$,

*which is the nearest point from*

*K*

*to*$Argmin(\phi )$;

- (2)
*let*$\{{x}_{n}\}$*and*$\{{u}_{n}\}$*be sequences generated iteratively by*$\{\begin{array}{c}\phi (y)-\phi ({u}_{n})+\frac{1}{{r}_{n}}\u3008y-{u}_{n},{u}_{n}-{x}_{n}\u3009\ge 0,\phantom{\rule{1em}{0ex}}\mathrm{\forall}y\in K,\hfill \\ {x}_{n+1}={\alpha}_{n}{x}_{n}+(1-{\alpha}_{n}){u}_{n},\phantom{\rule{1em}{0ex}}n\ge 0,\hfill \end{array}$(5.8)

*where* ${r}_{n}\in (0,+\mathrm{\infty})$ *and* ${\alpha}_{n}\in (0,1)$ *satisfy* (C3) *and* (C4). *Then* $\{{x}_{n}\}$ *converges weakly to some* ${x}^{\ast}\in Argmin(\phi )$.

We also observe the condition (C4) that ${lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{\alpha}_{n}<1$ includes the case that ${\alpha}_{n}=0$ as a special case. Therefore, our iteration scheme contains several proximal point algorithms that compute the solution ${x}_{n+1}\in K$ of some regularized problem.

**Theorem 5.6**

*Let*

*K*

*be a nonempty closed convex subset of a real Hilbert space*

*H*.

*Assume that a bifunction*

*F*

*and a function*

*φ*

*satisfy Condition*1.1

*and*$MEP(F,\phi )\ne \mathrm{\varnothing}$.

*Let*

*T*

*be a continuous monotone mapping of*

*K*

*such that*$GVIP(K,\phi ,T)\ne \mathrm{\varnothing}$.

*For any initialization*${x}_{0}\in K$,

*if*${x}_{n}$ ($n\ge 0$)

*is the current iterate and a positive real number*${r}_{n}$

*is the current parameter*,

*then*${x}_{n+1}$

*is defined by computing the solution of the regularized problem*

*or*

*Then*

*If*,

*in addition*, $\{{r}_{n}\}\subset (0,+\mathrm{\infty})$

*satisfies*(C3) ${lim\hspace{0.17em}inf}_{n\to \mathrm{\infty}}{r}_{n}>0$,

*then*

- (i)
$\{{x}_{n}\}$,

*given by*(5.9)*converge weakly to some element**x**of*$MEP(F,\phi )$; - (ii)
$\{{x}_{n}\}$,

*given by*(5.10)*converge weakly to some element**x**of*$GVIP(K,\phi ,T)$.

*Proof* It follows from Lemma 2.2 or Lemma 5.1 together with (5.9) or (5.10) that ${x}_{n+1}={T}_{{r}_{n}}({x}_{n})$. Then by Theorem 4.1, the desired results are obtained. □

**Remark 5.7**(1) By particularizing the bifunction

*F*, the function

*φ*and the parameters ${\alpha}_{n}$, ${r}_{n}$, some other methods and related results can be derived from our main theorems. For example, taking $\phi \equiv 0$ in (5.9) and ${lim}_{n\to \mathrm{\infty}}{r}_{n}=+\mathrm{\infty}$ in (5.5), we obtain several main results in Combettes and Hirstoaga [3], and in the case where (C3), ${\sum}_{n=0}^{+\mathrm{\infty}}\parallel {r}_{n}-{r}_{n+1}\parallel <+\mathrm{\infty}$ and ${\sum}_{n=0}^{+\mathrm{\infty}}{\alpha}_{n}(1-{\alpha}_{n})=+\mathrm{\infty}$ in (5.6), we have the main result in Nilsrakoo and Saejung [23], and when taking ${\alpha}_{n}\in [a,b]\subset (0,1)$ in (5.6), a result is proved by Tada and Takahashi [21], and if ${\alpha}_{n}\equiv 0$ in (5.2), then we obtain Algorithm 2 and Theorem 5 in Solodov and Svaiter [24]; also see [4, 5, 8, 20] and others for varying versions.

- (2)
All strong convergence theorems of this paper remain true if one replaces the anchor point

*u*by a contraction*f*(that is, so-called viscosity approximation methods with a contraction*f*) since Song [30] has showed their equivalency. So our main results could recover or develop some viscosity approximation results such as ones in references [8, 10, 11, 13] as well as others not referenced here. - (3)
The framework of holding our conclusions is more general and our results treat several iterative schemes and corresponding results in a united way. Consequently, our main results could be considered as recovering, developing and improving some known related convergence results in this field.

## Declarations

### Acknowledgements

The authors would like to thank the editor and the anonymous referee for useful comments and valuable suggestions on the language and structure of our manuscript. This work is supported by the National Natural Science Foundation of P.R. China (11071279, 11171094, 11271112).

## Authors’ Affiliations

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