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Approximate solutions to variational inequality over the fixed point set of a strongly nonexpansive mapping
Fixed Point Theory and Applications volume 2014, Article number: 51 (2014)
Abstract
Variational inequality problems over fixed point sets of nonexpansive mappings include many practical problems in engineering and applied mathematics, and a number of iterative methods have been presented to solve them. In this paper, we discuss a variational inequality problem for a monotone, hemicontinuous operator over the fixed point set of a strongly nonexpansive mapping on a real Hilbert space. We then present an iterative algorithm, which uses the strongly nonexpansive mapping at each iteration, for solving the problem. We show that the algorithm potentially converges in the fixed point set faster than algorithms using firmly nonexpansive mappings. We also prove that, under certain assumptions, the algorithm with slowly diminishing stepsize sequences converges to a solution to the problem in the sense of the weak topology of a Hilbert space. Numerical results demonstrate that the algorithm converges to a solution to a concrete variational inequality problem faster than the previous algorithm.
MSC:47H06, 47J20, 47J25.
1 Introduction
The paper presents an iterative algorithm for the variational inequality problem [1–8] for a monotone, hemicontinuous operator A over a nonempty, closed convex subset C of a real Hilbert space H with inner product $\u3008\cdot \phantom{\rule{0.2em}{0ex}},\cdot \u3009$ and its induced norm $\parallel \phantom{\rule{0.2em}{0ex}}\cdot \phantom{\rule{0.2em}{0ex}}\parallel $,
Problem (1) can be solved by using convex optimization techniques. A typical iterative procedure for Problem (1) is the projected gradient method [7, 9], and it is expressed as ${x}_{1}\in C$ and ${x}_{n+1}={P}_{C}(I{r}_{n}A){x}_{n}$ for $n=1,2,\dots $ , where ${P}_{C}$ stands for the metric projection onto C, I is the identity mapping on H, and $\{{r}_{n}\}\subset (0,\mathrm{\infty})$. However, as the method requires repetitive use of ${P}_{C}$, it can only be applied when the explicit form of ${P}_{C}$ is known (e.g., C is a closed ball or a closed cone). The following method, called the hybrid steepest descent method (HSDM) [10], enables us to consider the case in which C has a more complicated form: ${x}_{1}\in H$ and
for all $n=1,2,\dots $ , where $\{{r}_{n}\}\subset (0,1]$ and $T:H\to H$ is an easily implemented nonexpansive mapping satisfying $Fix(T):=\{x\in H:Tx=x\}=C$. HSDM converges strongly to a unique solution to the variational inequality problem over $Fix(T)$,
when $A:H\to H$ is strongly monotone and Lipschitz continuous. Problem (2) contains many applications such as signal recovery problems [11], beamforming problems [12], powercontrol problems [13, 14], bandwidth allocation problems [15–17], and optimal control problems [18]. References [11, 19], and [20] presented acceleration methods for solving Problem (2) when A is strongly monotone and Lipschitz continuous. Algorithms were presented to solve Problem (2) when A is (strictly) monotone and Lipschitz continuous [15, 17]. When $H={\mathbb{R}}^{N}$ and $A:{\mathbb{R}}^{N}\to {\mathbb{R}}^{N}$ is continuous (and is not necessarily monotone), a simple algorithm, ${x}_{n+1}:={\alpha}_{n}{x}_{n}+(1{\alpha}_{n})(1/2)(I+T)({x}_{n}{r}_{n}A{x}_{n})$ (${\alpha}_{n},{r}_{n}\in [0,1]$), was presented in [14] and the algorithm converges to a solution to Problem (2) under some conditions.
Reference [21] proposed an iterative algorithm for solving Problem (2) when $A:H\to H$ is monotone and hemicontinuous and showed that the algorithm weakly converges to a solution to the problem under certain assumptions. The results in [21] are summarized as follows: suppose that $F:H\to H$ is a firmly nonexpansive mapping with $Fix(F)\ne \mathrm{\varnothing}$ and that $A:H\to H$ is a monotone, hemicontinuous mapping with
Define a sequence $\{{x}_{n}\}\subset H$ by ${x}_{1}\in H$ and
for all $n=1,2,\dots $ , where $\{{\alpha}_{n}\}\subset [0,1)$ and $\{{r}_{n}\}\subset (0,1)$. Assume that $\{A{x}_{n}\}$ in algorithm (3) is bounded, and that there exists ${n}_{0}\in \mathbb{N}$ such that $VI(Fix(F),A)\subset \mathrm{\Omega}:={\bigcap}_{n={n}_{0}}^{\mathrm{\infty}}\{x\in Fix(F):\u3008{x}_{n}x,A{x}_{n}\u3009\ge 0\}$. If $\{{\alpha}_{n}\}$ and $\{{r}_{n}\}$ satisfy ${lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{\alpha}_{n}<1$, ${\sum}_{n=1}^{\mathrm{\infty}}{r}_{n}^{2}<\mathrm{\infty}$, and ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}{y}_{n}\parallel /{r}_{n}=0$, then $\{{x}_{n}\}$ weakly converges to a point in $VI(Fix(F),A)$. To relax the strong monotonicity condition of A considered in [10], a firmly nonexpansive mapping F is used in algorithm (3) in place of a nonexpansive mapping T. From the fact that a firmly nonexpansive mapping F can be represented by the form, $F=(1/2)(I+T)$, for some nonexpansive mapping T, algorithm (3) when ${\alpha}_{n}:=0$ and $F:=(1/2)(I+T)$ can be simplified as follows: ${x}_{1}\in H$ and
In constrained optimization problems, one is required to satisfy constraint conditions early in the process of executing an iterative algorithm. From this viewpoint, we introduce the following algorithm with a weighted parameter α, which is more than $1/2$:
Algorithm (5) potentially converges in the fixed point set faster than algorithm (4). Here, we can see that the mapping, $S:=(1\alpha )I+\alpha T$, satisfies the strong nonexpansivity condition [22], which is a weaker condition than firm nonexpansivity. This implies that the previous algorithms in [14, 21], which can be applied to Problem (2) when T is firmly nonexpansive, cannot solve Problem (2) when T is strongly nonexpansive.
In this paper, we present an iterative algorithm for solving the variational inequality problem with a monotone, hemicontinuous operator over the fixed point set of a strongly nonexpansive mapping and show that the algorithm weakly converges to a solution to the problem under certain assumptions.
The rest of the paper is organized as follows. Section 2 covers the mathematical preliminaries. Section 3 presents the algorithm for solving the variational inequality problem for a monotone, hemicontinuous operator over the fixed point set of a strongly nonexpansive mapping, and its convergence analyses. Section 4 provides numerical comparisons of the algorithm with the previous algorithm in [21] and shows that the algorithm converges to a solution to a concrete variational inequality problem faster than the previous algorithm. Section 5 concludes the paper.
2 Preliminaries
Throughout this paper, we will denote the set of all positive integers by ℕ and the set of all real numbers by ℝ. Let H be a real Hilbert space with inner product $\u3008\cdot \phantom{\rule{0.2em}{0ex}},\cdot \u3009$ and its induced norm $\parallel \phantom{\rule{0.2em}{0ex}}\cdot \phantom{\rule{0.2em}{0ex}}\parallel $. We denote the strong convergence and weak convergence of $\{{x}_{n}\}$ to $x\in H$ by ${x}_{n}\to x$ and ${x}_{n}\rightharpoonup x$, respectively. It is well known that H satisfies the following condition, called Opial’s condition [23]: for any $\{{x}_{n}\}\subset H$ satisfying ${x}_{n}\rightharpoonup {x}_{0}$, ${lim\hspace{0.17em}inf}_{n\to \mathrm{\infty}}\parallel {x}_{n}{x}_{0}\parallel <{lim\hspace{0.17em}inf}_{n\to \mathrm{\infty}}\parallel {x}_{n}y\parallel $ holds for all $y\in H$ with $y\ne {x}_{0}$; see also [5, 6, 24]. To prove our main theorems, we need the following lemma, which was proven in [25]; see also [5, 6, 26].
Lemma 2.1 ([25])
Assume that $\{{s}_{n}\}$ and $\{{e}_{n}\}$ are sequences of nonnegative numbers such that ${s}_{n+1}\le {s}_{n}+{e}_{n}$ for all $n\in \mathbb{N}$. If ${\sum}_{n=1}^{\mathrm{\infty}}{e}_{n}<\mathrm{\infty}$, then ${lim}_{n\to \mathrm{\infty}}{s}_{n}$ exists.
2.1 Strong nonexpansivity and fixed point set
Let T be a mapping of H into itself. We denote the fixed point set of T by $Fix(T)$; i.e., $Fix(T)=\{z\in H:Tz=z\}$. A mapping $T:H\to H$ is said to be nonexpansive if $\parallel TxTy\parallel \le \parallel xy\parallel $ for all $x,y\in H$. $Fix(T)$ is closed and convex when T is nonexpansive [5, 6, 24, 27]. $T:H\to H$ is said to be strongly nonexpansive [22] if T is nonexpansive and if, for bounded sequences $\{{x}_{n}\},\{{y}_{n}\}\subset H$, $\parallel {x}_{n}{y}_{n}\parallel \parallel T{x}_{n}T{y}_{n}\parallel \to 0$ implies $\parallel {x}_{n}{y}_{n}(T{x}_{n}T{y}_{n})\parallel \to 0$. The following properties for strongly nonexpansive mappings were shown in [22]:

$Fix(T)$ is closed and convex when $T:H\to H$ is strongly nonexpansive because T is also nonexpansive.

A strongly nonexpansive mapping, $T:H\to H$, with $Fix(T)\ne \mathrm{\varnothing}$ is asymptotically regular[24, 28], i.e., for each $x\in H$, ${lim}_{n\to \mathrm{\infty}}\parallel {T}^{n}x{T}^{n+1}x\parallel =0$.

If $S,T:H\to H$ are strongly nonexpansive, then ST is also strongly nonexpansive, and $Fix(ST)=Fix(S)\cap Fix(T)$ when $Fix(S)\cap Fix(T)\ne \mathrm{\varnothing}$.

If $S:H\to H$ is strongly nonexpansive and if $T:H\to H$ is nonexpansive, then $\alpha S+(1\alpha )T$ is strongly nonexpansive for $\alpha \in (0,1)$. If $Fix(S)\cap Fix(T)\ne \mathrm{\varnothing}$, then $Fix(\alpha S+(1\alpha )T)=Fix(S)\cap Fix(T)$[29]. In particular, since the identity mapping I is strongly nonexpansive, the mapping $U:=\alpha I+(1\alpha )T$ is strongly nonexpansive. Such U is said to be averaged nonexpansive.
Example 2.1 Let $D\subset H$ be a closed convex set, which is simple in the sense that ${P}_{D}$ can be calculated explicitly. Furthermore, let $f:H\to \mathbb{R}$ be Fréchet differentiable and $\mathrm{\nabla}f:H\to H$ be Lipschitz continuous; i.e., there exists $L>0$ such that $\parallel \mathrm{\nabla}f(x)\mathrm{\nabla}f(y)\parallel \le L\parallel xy\parallel $ for all $x,y\in H$. Then, for $r\in (0,2/L]$, ${S}_{r}:={P}_{D}(Ir\mathrm{\nabla}f)$ is nonexpansive [30], [[31], Lemma 2.1]. Define $T:H\to H$ by
Then T is strongly nonexpansive and $Fix(T)=\{x\in D:f(x)={min}_{y\in D}f(y)\}$.
Example 2.2 Let ${D}_{i}\subset H$ ($i=0,1,\dots ,m$) be a closed convex set, which is simple in the sense that ${P}_{{D}_{i}}$ can be calculated explicitly. Define $\mathrm{\Phi}(x):=(1/2){\sum}_{i=1}^{m}{\omega}_{i}\phantom{\rule{0.2em}{0ex}}d(x,{D}_{i})$ for all $x\in H$, where ${\omega}_{i}\in (0,1)$ with ${\sum}_{i=1}^{m}{\omega}_{i}=1$ and $d(x,{D}_{i}):=min\{\parallel xy\parallel :y\in {D}_{i}\}$ ($i=1,2,\dots ,m$). Also, we define $S:H\to H$ and $T:H\to H$ as
Then S is nonexpansive [[10], Proposition 4.2] and $Fix(S)={C}_{\mathrm{\Phi}}:=\{x\in {D}_{0}:\mathrm{\Phi}(x)={min}_{y\in {D}_{0}}\mathrm{\Phi}(y)\}$. Hence, T is strongly nonexpansive and $Fix(T)={C}_{\mathrm{\Phi}}$. ${C}_{\mathrm{\Phi}}$ is referred to as a generalized convex feasible set [10, 32] and is defined as the subset of ${D}_{0}$ that is closest to ${D}_{1},{D}_{2},\dots ,{D}_{m}$ in the mean square sense. Even if ${\bigcap}_{i=0}^{m}{D}_{i}=\mathrm{\varnothing}$, ${C}_{\mathrm{\Phi}}$ is well defined. ${C}_{\mathrm{\Phi}}={\bigcap}_{i=0}^{m}{D}_{i}$ holds when ${\bigcap}_{i=0}^{m}{D}_{i}\ne \mathrm{\varnothing}$. Accordingly, ${C}_{\mathrm{\Phi}}$ is a generalization of ${\bigcap}_{i=0}^{m}{D}_{i}$.
A mapping $F:H\to H$ is said to be firmly nonexpansive [33] if ${\parallel FxFy\parallel}^{2}\le \u3008xy,FxFy\u3009$ for all $x,y\in H$ (see also [24, 27, 34]). Every firmly nonexpansive mapping F can be expressed as $F=(1/2)(I+T)$ given some nonexpansive mapping T [24, 27, 34]. Hence, the class of averaged nonexpansive mappings includes the class of firmly nonexpansive mappings.
2.2 Variational inequality
An operator $A:H\to H$ is said to be monotone if $\u3008xy,AxAy\u3009\ge 0$ for all $x,y\in H$. $A:H\to H$ is said to be hemicontinuous [[5], p.204] if, for any $x,y\in H$, the mapping $g:[0,1]\to H$ defined by $g(t)=A(tx+(1t)y)$ is continuous, where H has a weak topology. Let C be a nonempty, closed convex subset of H. The variational inequality problem [2, 4] for a monotone operator $A:H\to H$ is as follows (see also [1, 3, 5–8]):
We denote the solution set of the variational inequality problem by $VI(C,A)$. The monotonicity and hemicontinuity of A imply that $VI(C,A)=\{z\in C:\u3008yz,Ay\u3009\ge 0\text{for all}y\in C\}$ [[5], Subsection 7.1]. This means that $VI(C,A)$ is closed and convex. $VI(C,A)$ is nonempty when $A:H\to H$ is monotone and hemicontinuous, and $C\subset H$ is nonempty, compact, and convex [[5], Theorem 7.1.8].
Example 2.3 Let $g:H\to \mathbb{R}$ be convex and continuously Fréchet differentiable and $A:=\mathrm{\nabla}g$. Then A is monotone and hemicontinuous.
(i) Suppose that $f:H\to \mathbb{R}$ is as in Example 2.1 and $T:H\to H$ is defined as in (6) and set $G:=\{z\in D:f(z)={min}_{w\in D}f(w)\}$. Then
A solution of this problem is a minimizer of g over the set of all minimizers of f over D. Therefore, the problem has a triplex structure [16, 31, 35].
(ii) Suppose that $T:H\to H$ is defined as in (7). Then
This problem is to find a minimizer of g over the generalized convex feasible set [10, 13, 14, 16, 18].
3 Optimization of variational inequality over fixed point set
In this section, we present an iterative algorithm for solving the variational inequality problem for a monotone, hemicontinuous operator over the fixed point set of a strongly nonexpansive mapping and its convergence analyses. We assume that $T:H\to H$ is a strongly nonexpansive mapping with $Fix(T)\ne \mathrm{\varnothing}$ and that $A:H\to H$ is a monotone, hemicontinuous operator.
Algorithm 3.1
Step 0. Choose ${x}_{1}\in H$, ${r}_{1}\in (0,1)$, and ${\alpha}_{1}\in [0,1)$ arbitrarily, and let $n:=1$.
Step 1. Given ${x}_{n}\in H$, choose ${r}_{n}\in (0,1)$ and ${\alpha}_{n}\in [0,1)$ and compute ${x}_{n+1}\in H$ as
Step 2. Update $n:=n+1$, and go to Step 1.
To prove our main theorems, we need the following lemma.
Lemma 3.1 Suppose that $\{{x}_{n}\}$ is a sequence generated by Algorithm 3.1 and that $\{A{x}_{n}\}$ is bounded. Moreover, assume that

(A)
${\sum}_{n=1}^{\mathrm{\infty}}{r}_{n}<\mathrm{\infty}$, or

(B)
${\sum}_{n=1}^{\mathrm{\infty}}{r}_{n}^{2}<\mathrm{\infty}$, $VI(Fix(T),A)\ne \mathrm{\varnothing}$, and the existence of an ${n}_{0}\in \mathbb{N}$ satisfying $VI(Fix(T),A)\subset \mathrm{\Omega}:={\bigcap}_{n={n}_{0}}^{\mathrm{\infty}}\{x\in Fix(T):\u3008{x}_{n}x,A{x}_{n}\u3009\ge 0\}$.
Then $\{{x}_{n}\}$ is bounded.
Proof Put ${z}_{n}:={x}_{n}{r}_{n}A{x}_{n}$ for all $n\in \mathbb{N}$. We first assume that condition (A) is satisfied and choose $u\in Fix(T)$ arbitrarily. Accordingly, we see that, for any $n\in \mathbb{N}$,
From ${\sum}_{n=1}^{\mathrm{\infty}}{r}_{n}<\mathrm{\infty}$, the boundedness of $\{A{x}_{n}\}$, and Lemma 2.1, the limit of $\{\parallel {x}_{n}u\parallel \}$ exists for all $u\in Fix(T)$, which implies that $\{{x}_{n}\}$ is bounded.
Next, suppose that condition (B) is satisfied, and let $u\in Fix(T)$. Then, from the monotonicity of A, we find that, for any $n\in \mathbb{N}$,
where $K:=sup\{{\parallel A{x}_{n}\parallel}^{2}:n\in \mathbb{N}\}<\mathrm{\infty}$. Especially in the case of $u\in VI(Fix(T),A)\subset \mathrm{\Omega}$, it follows from condition (B) that, for any $n\ge {n}_{0}$,
Hence, the condition, ${\sum}_{n=1}^{\mathrm{\infty}}{r}_{n}^{2}<\mathrm{\infty}$, and Lemma 2.1 guarantee that the limit of $\{\parallel {x}_{n}u\parallel \}$ exists for all $u\in VI(Fix(T),A)$. We thus conclude that $\{{x}_{n}\}$ is bounded. □
Now, we are in the position to perform the convergence analysis on Algorithm 3.1 under condition (A) in Lemma 3.1.
Theorem 3.1 Let $\{{x}_{n}\}$ be a sequence generated by Algorithm 3.1 and assume that $\{A{x}_{n}\}$ is bounded and that the sequences $\{{\alpha}_{n}\}\subset [0,1)$ and $\{{r}_{n}\}\subset (0,1)$ satisfy
Then Algorithm 3.1 converges weakly to a point in $VI(Fix(T),A)$.
Proof Put ${z}_{n}:={x}_{n}{r}_{n}A{x}_{n}$ for all $n\in \mathbb{N}$. The proof consists of the following steps:

(a)
Prove that $\{{x}_{n}\}$ and $\{{z}_{n}\}$ are bounded.

(b)
Prove that ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}{y}_{n}\parallel =0$ and ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}T{x}_{n}\parallel =0$ hold.

(c)
Prove that $\{{x}_{n}\}$ converges weakly to a point in $\mathrm{VI}(Fix(T),A)$.

(a)
Choose $u\in Fix(T)$ arbitrarily. From the inequality, $\parallel {z}_{n}u\parallel =\parallel ({x}_{n}{r}_{n}A{x}_{n})u\parallel \le \parallel {x}_{n}u\parallel +{r}_{n}\parallel A{x}_{n}\parallel $, and Lemma 3.1, we deduce that $\{{z}_{n}\}$ is bounded.

(b)
Put $c:={lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}u\parallel $ for any $u\in Fix(T)$. Then, from ${\sum}_{n=1}^{\mathrm{\infty}}{r}_{n}<\mathrm{\infty}$, for any $\epsilon >0$, we can choose $m\in \mathbb{N}$ such that $\parallel {x}_{n}u\parallel c\le \epsilon $, and ${r}_{n}\le \epsilon $ for all $n\ge m$. Also, there exists $a>0$ such that ${\alpha}_{n}<a<1$ for all $n\ge m$ because of ${lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{\alpha}_{n}<1$. Since ${y}_{n}=(1/(1{\alpha}_{n})){x}_{n+1}({\alpha}_{n}/(1{\alpha}_{n})){x}_{n}$, we have
$$\parallel {y}_{n}u\parallel \ge \frac{1}{1{\alpha}_{n}}\parallel {x}_{n+1}u\parallel \frac{{\alpha}_{n}}{1{\alpha}_{n}}\parallel {x}_{n}u\parallel $$
for all $n\in \mathbb{N}$. We find that, for any $n\ge m$,
Hence, for any $u\in Fix(T)$ and for any $n\ge m$, we have
where $K=sup\{{\parallel A{x}_{n}\parallel}^{2}:n\in \mathbb{N}\}<\mathrm{\infty}$, which implies that ${lim}_{n\to \mathrm{\infty}}(\parallel {z}_{n}u\parallel \parallel T{z}_{n}Tu\parallel )=0$. Since T is strongly nonexpansive, we get
From (10) and $\parallel {x}_{n}{z}_{n}\parallel ={r}_{n}\parallel A{x}_{n}\parallel \to 0$ as $n\to \mathrm{\infty}$, we also get
From $\parallel {x}_{n}T{x}_{n}\parallel \le \parallel {x}_{n}{y}_{n}\parallel +\parallel {y}_{n}T{x}_{n}\parallel \le \parallel {x}_{n}{y}_{n}\parallel +\parallel {z}_{n}{x}_{n}\parallel $, and (11), we deduce that

(c)
From the boundedness of $\{{x}_{n}\}$, there exists a subsequence $\{{x}_{{n}_{i}}\}$ of $\{{x}_{n}\}$ such that $\{{x}_{{n}_{i}}\}$ converges weakly to a point $v\in H$. From the nonexpansivity of T and (12), it is guaranteed that T is demiclosed (i.e., ${x}_{n}\rightharpoonup u$ and $\parallel {x}_{n}T{x}_{n}\parallel \to 0$ imply $u\in Fix(T)$). Hence, we have $v\in Fix(T)$. From (9), we get, for any $u\in Fix(T)$ and for any $n\in \mathbb{N}$,
$$\begin{array}{rl}0\le & (\parallel {x}_{n}u\parallel +\parallel {x}_{n+1}u\parallel )(\parallel {x}_{n}u\parallel \parallel {x}_{n+1}u\parallel )\\ +2{r}_{n}(1{\alpha}_{n})\u3008u{x}_{n},Au\u3009+K{r}_{n}^{2},\end{array}$$
which means
where $L:=sup\{\parallel {x}_{n}u\parallel +\parallel {x}_{n+1}u\parallel :n\in \mathbb{N}\}<\mathrm{\infty}$. From $\parallel {x}_{n}{y}_{n}\parallel /{r}_{n}\to 0$, ${x}_{n}\rightharpoonup v$, ${lim\hspace{0.17em}sup}_{n}{\alpha}_{n}<1$, and ${r}_{n}\to 0$, we have
The monotonicity and hemicontinuity of A imply that $v\in \mathrm{VI}(Fix(T),A)$. Finally, we show that $\{{x}_{n}\}$ converges weakly to $v\in VI(Fix(T),A)$. Assume that another subsequence $\{{x}_{{n}_{j}}\}$ of $\{{x}_{n}\}$ converges weakly to w. Then, from the discussion above, we also get $w\in VI(Fix(T),A)$. If $v\ne w$, Opial’s theorem [23] guarantees that
This is a contradiction. Thus, $v=w$. This implies that every subsequence of $\{{x}_{n}\}$ converges weakly to the same point in $VI(Fix(T),A)$. Therefore, $\{{x}_{n}\}$ converges weakly to $v\in VI(Fix(T),A)$. This completes the proof. □
Remark 3.1 The numerical examples in [14, 16, 21] show that Algorithm 3.1 satisfies ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}{y}_{n}\parallel /{r}_{n}=0$ when T is firmly nonexpansive and ${r}_{n}:=1/{n}^{\alpha}$ ($1\le \alpha <2$). However, when $\alpha \ge 2$, there are counterexamples that do not satisfy ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}{y}_{n}\parallel /{r}_{n}=0$ [14, 16, 21].
Remark 3.2 If the sequence $\{{x}_{n}\}$ satisfies the assumptions in Theorem 3.1, we need not assume that $VI(Fix(T),A)\ne \mathrm{\varnothing}$ or that ${n}_{0}\in \mathbb{N}$ exists such that $VI(Fix(T),A)\subset \mathrm{\Omega}$ in condition (B) (see also [[14], Remark 7(c)]).
Remark 3.3 Let us provide the sufficient condition of the boundedness of $\{A{x}_{n}\}$. Suppose that $Fix(T)$ is bounded and A is Lipschitz continuous. Then we can set a bounded set V with $Fix(T)\subset V$ onto which the projection can be computed within a finite number of arithmetic operations (e.g., V is a closed ball with a large enough radius). Accordingly, we can compute
instead of ${x}_{n+1}$ in Algorithm 3.1. Since $\{{x}_{n}\}\subset V$ and V is bounded, $\{{x}_{n}\}$ is bounded. The Lipschitz continuity of A means that $\parallel A{x}_{n}Ax\parallel \le L\parallel {x}_{n}x\parallel $ ($x\in H$), where L (>0) is a constant, and hence, $\{A{x}_{n}\}$ is bounded. We can prove that Algorithm 3.1 with Equation (13) and $\{{\alpha}_{n}\}$ and $\{{r}_{n}\}$ satisfying the conditions in Theorem 3.1 (or Theorem 3.2) weakly converges to a point in $VI(Fix(T),A)$ by referring to the proof of Theorem 3.1 (or Theorem 3.2).
We prove the following theorem under condition (B) in Lemma 3.1. The essential parts of a proof are similar those of Lemma 3.1 and Theorem 3.1, so we will only give an outline of the proof below.
Theorem 3.2 Let $\{{x}_{n}\}$ be a sequence generated by Algorithm 3.1. Assume that $\{A{x}_{n}\}$ is bounded and that $\{{\alpha}_{n}\}\subset [0,1)$ and $\{{r}_{n}\}\subset (0,1)$ satisfy
If $VI(Fix(T),A)\ne \mathrm{\varnothing}$ and if there exists ${n}_{0}\in \mathbb{N}$ such that $VI(Fix(T),A)\subset {\bigcap}_{n={n}_{0}}^{\mathrm{\infty}}\{x\in Fix(T):\u3008{x}_{n}x,A{x}_{n}\u3009\ge 0\}$, then the sequence $\{{x}_{n}\}$ converges weakly to a point in $VI(Fix(T),A)$.
Proof Put ${z}_{n}:={x}_{n}{r}_{n}A{x}_{n}$ for all $n\in \mathbb{N}$. As in the proof of Theorem 3.1, we proceed with the following steps:

(a)
Prove that $\{{x}_{n}\}$ and $\{{z}_{n}\}$ are bounded.

(b)
Prove that ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}T{x}_{n}\parallel =0$ holds.

(c)
Prove that $\{{x}_{n}\}$ converges weakly to a point in $\mathrm{VI}(Fix(T),A)$.

(a)
From Lemma 3.1, it follows that the limit of $\{\parallel {x}_{n}u\parallel \}$ exists for all $u\in VI(Fix(T),A)$, and hence $\{{x}_{n}\}$ and $\{{z}_{n}\}$ are bounded.

(b)
Let $u\in VI(Fix(T),A)$ and put $c:={lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}u\parallel $. Since ${\sum}_{n=1}^{\mathrm{\infty}}{r}_{n}^{2}<\mathrm{\infty}$, the condition, ${r}_{n}\to 0$, holds. As in the proof of Theorem 3.1(b), for any $\epsilon >0$, there exists $m\in \mathbb{N}$ such that
$$\parallel {x}_{n}u\parallel c\le \epsilon ,\phantom{\rule{1em}{0ex}}\text{and}\phantom{\rule{1em}{0ex}}\parallel {y}_{n}u\parallel \ge c\frac{1+a}{1a}\epsilon $$
for all $n\ge m$. By ${lim\hspace{0.17em}sup}_{n\to \mathrm{\infty}}{\alpha}_{n}<1$, there exists $a>0$ such that ${\alpha}_{n}<a<1$. Since the inequality $\parallel {z}_{n}u\parallel =\parallel ({x}_{n}{r}_{n}A{x}_{n})u\parallel \le \parallel {x}_{n}u\parallel +{r}_{n}\parallel A{x}_{n}\parallel $ holds, we have
where $K=sup\{{\parallel A{x}_{n}\parallel}^{2}:n\in \mathbb{N}\}<\mathrm{\infty}$. This implies that ${lim}_{n\to \mathrm{\infty}}(\parallel {z}_{n}u\parallel \parallel T{z}_{n}Tu\parallel )=0$. From the strong nonexpansivity of T, we get ${lim}_{n\to \mathrm{\infty}}\parallel {z}_{n}T{z}_{n}\parallel =0$. The rest of the proof is the same as the proof of Theorem 3.1(b). Accordingly, we obtain ${lim}_{n\to \mathrm{\infty}}\parallel {x}_{n}T{x}_{n}\parallel =0$.

(c)
Following the proof of Theorem 3.1(c), there exists a subsequence $\{{x}_{{n}_{i}}\}\subset \{{x}_{n}\}$ such that $\{{x}_{{n}_{i}}\}$ converges weakly to $v\in VI(Fix(T),A)$. Assume that another subsequence $\{{x}_{{n}_{j}}\}$ of $\{{x}_{n}\}$ converges weakly to w. Then we also have $w\in VI(Fix(T),A)$. Since the limit of $\{\parallel {x}_{n}u\parallel \}$ exists for $u\in \mathrm{VI}(Fix(T),A)$, Opial’s theorem [23] guarantees that $v=w$. This implies that every subsequence of $\{{x}_{n}\}$ converges weakly to the same point in $VI(Fix(T),A)$, and hence, $\{{x}_{n}\}$ converges weakly to $v\in VI(Fix(T),A)$. This completes the proof. □
As we mentioned in Section 1, to solve constrained optimization problems whose feasible set is the fixed point set of a nonexpansive mapping T, Algorithm 3.1 must converge in $Fix(T)$ early in the execution. Therefore, it would be useful to use a large parameter α ($\in (0,1)$) when a strongly nonexpansive mapping is represented by $(1\alpha )I+\alpha T$. Theorem 3.1 has the following consequences.
Corollary 3.1 Let $T:H\to H$ be a nonexpansive mapping with $Fix(T)\ne \mathrm{\varnothing}$ and let $A:H\to H$ be a monotone, hemicontinuous mapping. Let $\{{x}_{n}\}$ be a sequence generated by ${x}_{1}\in H$ and
for all $n\in \mathbb{N}$, where $\{{\alpha}_{n}\}\subset [0,1)$, $\alpha \in (0,1)$ and $\{{r}_{n}\}\subset (0,1)$. Assume that $\{A{x}_{n}\}$ is a bounded sequence and that
Then $\{{x}_{n}\}$ converges weakly to a point in $VI(Fix(T),A)$.
Proof Since every averaged nonexpansive mapping is strongly nonexpansive and $Fix((1\alpha )I+\alpha T)=Fix(T)$ for $\alpha \in (0,1)$, Theorem 3.1 implies Corollary 3.1. □
By following the proof of Theorem 3.2 and Corollary 3.1, we get the following.
Corollary 3.2 Let $T:H\to H$ be a nonexpansive mapping with $Fix(T)\ne \mathrm{\varnothing}$ and let $A:H\to H$ be a monotone, hemicontinuous mapping. Let $\{{x}_{n}\}$ be a sequence in algorithm (14). Assume that $\{A{x}_{n}\}$ is a bounded sequence and that
If $VI(Fix(T),A)\ne \mathrm{\varnothing}$ and if there exists ${n}_{0}\in \mathbb{N}$ such that $VI(Fix(T),A)\subset {\bigcap}_{n={n}_{0}}^{\mathrm{\infty}}\{x\in Fix(T):\u3008{x}_{n}x,A{x}_{n}\u3009\ge 0\}$, then $\{{x}_{n}\}$ converges weakly to a point in $VI(Fix(T),A)$.
4 Numerical examples
Let us apply Algorithm 3.1 and the algorithm in [21] to the following variational inequality problem.
Problem 4.1 Define $f:{\mathbb{R}}^{1,000}\to \mathbb{R}$ and ${C}_{i}$ ($\subset {\mathbb{R}}^{1,000}$) ($i=1,2$) by
where $Q\in {\mathbb{R}}^{1,000\times 1,000}$ is positive semidefinite, ${a}_{i}:=({a}_{i}^{(1)},{a}_{i}^{(2)},\dots ,{a}_{i}^{(1,000)})\in {\mathbb{R}}^{1,000}$, and ${b}_{i}\in {\mathbb{R}}_{+}$ ($i=1,2$). Find $z\in VI({C}_{1}\cap {C}_{2},\mathrm{\nabla}f)$.
We set Q as a diagonal matrix with diagonal components $0,1,\dots ,999$ and choose ${a}_{i}^{(j)}\in (0,100)$ ($i=1,2$, $j=1,2,\dots ,1,000$) to be Mersenne Twister pseudorandom numbers given by the randomreal function of srfi27, Gauche.^{a} We also set ${b}_{1}:=5,000$ and ${b}_{2}:=4,000$. The compiler used in this experiment was gcc.^{b} The doubleprecision floating points were used for arithmetic processing of real numbers. The language was C.
In the experiment, we used the following algorithm:
where $\alpha \in (0,1)$. Note that the projection ${P}_{{C}_{i}}$ ($i=1,2$) can be computed within a finite number of arithmetic operations [[36], p.406] because ${C}_{i}$ ($i=1,2$) is halfspace. More precisely,
We can see that algorithm (15) with $\alpha :=1/2$ coincides with the previous algorithm in [21]. Hence, we compare^{c} algorithm (15) with $\alpha :=9/10$ with algorithm (15) with $\alpha :=1/2$ and verify that algorithm (15) with $\alpha :=9/10$ converges in ${C}_{1}\cap {C}_{2}=Fix({P}_{{C}_{1}}{P}_{{C}_{2}})$ faster than algorithm (15) with $\alpha :=1/2$. We selected one hundred initial points $x=x(k)\in {\mathbb{R}}^{1,000}$ ($k=1,2,\dots ,100$) as pseudorandom numbers generated by the rand function of the C Standard Library. We executed algorithm (15) with $\alpha :=9/10$ and algorithm (15) with $\alpha :=1/2$ for these initial points. Let $\{{x}_{n}(k)\}$ be the sequence generated by $x(k)$ and algorithm (15). Here, we define
The convergence of $\{{D}_{n}\}$ to 0 implies that algorithm (15) converges to a point in ${C}_{1}\cap {C}_{2}$.
Corollary 3.1 guarantees that algorithm (15) converges to a solution to Problem 4.1 if $\{\mathrm{\nabla}f({x}_{n})\}$ is bounded and if
To verify whether algorithm (15) satisfies condition (16), we employed
where ${y}_{n}(k):=((1\alpha )I+\alpha {P}_{{C}_{1}}{P}_{{C}_{2}})({x}_{n}(k)({10}^{3}/{(n+1)}^{1.001})\mathrm{\nabla}f({x}_{n}(k)))$ ($k=1,2,\dots ,100$, $n\in \mathbb{N}$). The convergence of $\{{X}_{n}\}$ to 0 implies that algorithm (15) satisfies condition (16). We also used
to check that algorithm (15) is stable.
Figure 1 indicates the behaviors of ${D}_{n}$ for algorithm (15) with $\alpha :=9/10$ and algorithm (15) with $\alpha :=1/2$. This figure shows that $\{{D}_{n}\}$ in algorithm (15) with $\alpha :=9/10$ converges to 0 faster than $\{{D}_{n}\}$ in algorithm (15) with $\alpha :=1/2$; i.e., algorithm (15) with $\alpha :=9/10$ converges in ${C}_{1}\cap {C}_{2}$ faster than the previous algorithm in [21].
Figure 2 compares the behaviors of ${X}_{n}$ for algorithm (15) with $\alpha :=9/10$ and algorithm (15) with $\alpha :=1/2$ and shows that the $\{{X}_{n}\}$ generated by each algorithm converges to 0; i.e., they each satisfy (16). Therefore, from Corollary 3.1, we can conclude that they can find a solution to Problem 4.1.
We can see from Figure 3 that $\{{F}_{n}\}$ generated by the two algorithms converge to the same value. Figures 1, 2, and 3 indicate that algorithm (15) with $\alpha :=9/10$ converges to a solution to Problem 4.1 faster than the previous algorithm in [21]. This is because algorithm (15) uses a parameter ($\alpha :=9/10$) that is larger than $1/2$ and algorithm (15) with $\alpha >1/2$ potentially converges in the constraint set ${C}_{1}\cap {C}_{2}$ faster than the previous algorithm in [21] with $\alpha :=1/2$.
5 Conclusion
We studied a variational inequality problem for a monotone, hemicontinuous operator over the fixed point set of a strongly nonexpansive mapping in a Hilbert space and devised an iterative algorithm for solving it. Our convergence analyses guarantee that the algorithm weakly converges to a solution under certain assumptions. We gave numerical results to support the convergence analyses on the algorithm. The results showed that the algorithm converges to a solution to a concrete variational inequality problem faster than the previous algorithm.
6 Endnotes
We used the Gauche scheme shell, 0.9.3.3 [utf8,pthreads], x86_64appledarwin12.4.1.
We used gcc version 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00).
For example, we set a large parameter, i.e., much more than $1/2$: $\alpha =9/10$.
References
 1.
Kinderlehrer D, Stampacchia G: An Introduction to Variational Inequalities and Their Applications. Academic Press, New York; 1980.
 2.
Lions JL, Stampacchia G: Variational inequalities. Commun. Pure Appl. Math. 1967, 20: 493–519. 10.1002/cpa.3160200302
 3.
Rockafellar RT, Wets RJB: Variational Analysis. Springer, Berlin; 1998.
 4.
Stampacchia G: Formes bilinéaires coercitives sur les ensembles convexes. C. R. Math. Acad. Sci. Paris 1964, 258: 4413–4416.
 5.
Takahashi W: Nonlinear Functional Analysis. Fixed Point Theory and Its Applications. Yokohama Publishers, Yokohama; 2000.
 6.
Takahashi W: Introduction to Nonlinear and Convex Analysis. Yokohama Publishers, Yokohama; 2009.
 7.
Zeidler E: Nonlinear Functional Analysis and Its Applications. II/B. Springer, New York; 1990.
 8.
Zeidler E: Nonlinear Functional Analysis and Its Applications. III. Springer, New York; 1985.
 9.
Goldstein AA: Convex programming in Hilbert space. Bull. Am. Math. Soc. 1964, 70: 709–710. 10.1090/S000299041964111782
 10.
Yamada I: The hybrid steepest descent method for the variational inequality problem over the intersection of fixed point sets of nonexpansive mappings. Stud. Comput. Math. 8. In Inherently Parallel Algorithms in Feasibility and Optimization and Their Applications. NorthHolland, Amsterdam; 2001:473–504.
 11.
Combettes PL: A blockiterative surrogate constraint splitting method for quadratic signal recovery. IEEE Trans. Signal Process. 2003, 51: 1771–1782. 10.1109/TSP.2003.812846
 12.
Slavakis K, Yamada I: Robust wideband beamforming by the hybrid steepest descent method. IEEE Trans. Signal Process. 2007, 55: 4511–4522.
 13.
Iiduka H, Yamada I: An ergodic algorithm for the powercontrol games for CDMA data networks. J. Math. Model. Algorithms 2009, 8: 1–18. 10.1007/s1085200890994
 14.
Iiduka H: Fixed point optimization algorithm and its application to power control in CDMA data networks. Math. Program. 2012, 133: 227–242. 10.1007/s101070100427x
 15.
Iiduka H: Fixed point optimization algorithm and its application to network bandwidth allocation. J. Comput. Appl. Math. 2012, 236: 1733–1742. 10.1016/j.cam.2011.10.004
 16.
Iiduka H: Iterative algorithm for triplehierarchical constrained nonconvex optimization problem and its application to network bandwidth allocation. SIAM J. Optim. 2012, 22: 862–878. 10.1137/110849456
 17.
Iiduka H: Fixed point optimization algorithms for distributed optimization in networked systems. SIAM J. Optim. 2013, 23: 1–26. 10.1137/120866877
 18.
Iiduka H, Yamada I: Computational method for solving a stochastic linearquadratic control problem given an unsolvable stochastic algebraic Riccati equation. SIAM J. Control Optim. 2012, 50: 2173–2192. 10.1137/110850542
 19.
Iiduka H: Acceleration method for convex optimization over the fixed point set of a nonexpansive mapping. Math. Program. 2014. 10.1007/s1010701307411
 20.
Iiduka H, Yamada I: A use of conjugate gradient direction for the convex optimization problem over the fixed point set of a nonexpansive mapping. SIAM J. Optim. 2009, 19: 1881–1893. 10.1137/070702497
 21.
Iiduka H: A new iterative algorithm for the variational inequality problem over the fixed point set of a firmly nonexpansive mapping. Optimization 2010, 59: 873–885. 10.1080/02331930902884158
 22.
Bruck RE, Reich S: Nonexpansive projections and resolvents of accretive operators in Banach spaces. Houst. J. Math. 1977, 3: 459–470.
 23.
Opial Z: Weak convergence of the sequence of successive approximations for nonexpansive mappings. Bull. Am. Math. Soc. 1967, 73: 591–597. 10.1090/S000299041967117610
 24.
Goebel K, Kirk WA: Topics in Metric Fixed Point Theory. Cambridge University Press, Cambridge; 1990.
 25.
Tan KK, Xu HK: Approximating fixed points of nonexpansive mappings by the Ishikawa iteration process. J. Math. Anal. Appl. 1993, 178: 301–308. 10.1006/jmaa.1993.1309
 26.
Berinde V Lecture Notes in Mathematics. In Iterative Approximation of Fixed Points. Springer, Berlin; 2007.
 27.
Goebel K, Reich S: Uniform Convexity, Hyperbolic Geometry, and Nonexpansive Mappings. Dekker, New York; 1984.
 28.
Browder FE, Petryshyn WV: The solution by iteration of linear functional equations in Banach spaces. Bull. Am. Math. Soc. 1966, 72: 566–570. 10.1090/S000299041966115434
 29.
Aoyama K, Kimura Y, Takahashi W, Toyoda M: On a strongly nonexpansive sequence in Hilbert spaces. J. Nonlinear Convex Anal. 2007, 8: 471–489.
 30.
Baillon JB, Haddad G: Quelques propriétés des opérateurs anglebornés et n cycliquement monotones. Isr. J. Math. 1977, 26: 137–150. 10.1007/BF03007664
 31.
Iiduka H: Strong convergence for an iterative method for the triplehierarchical constrained optimization problem. Nonlinear Anal. 2009, 71: 1292–1297. 10.1016/j.na.2009.01.133
 32.
Combettes PL, Bondon P: Hardconstrained inconsistent signal feasibility problems. IEEE Trans. Signal Process. 1999, 47: 2460–2468. 10.1109/78.782189
 33.
Browder FE: Convergence theorems for sequences of nonlinear operators in Banach spaces. Math. Z. 1967, 100: 201–225. 10.1007/BF01109805
 34.
Reich S, Shoikhet D: Nonlinear Semigroups, Fixed Points, and Geometry of Domains in Banach Spaces. Imperial College Press, London; 2005.
 35.
Iiduka H: Iterative algorithm for solving triplehierarchical constrained optimization problem. J. Optim. Theory Appl. 2011, 148: 580–592. 10.1007/s109570109769z
 36.
Bauschke HH, Borwein JM: On projection algorithms for solving convex feasibility problems. SIAM Rev. 1996, 38: 367–426. 10.1137/S0036144593251710
Acknowledgements
We are sincerely grateful to the Lead Guest Editor, Qamrul Hasan Ansari, of Special Issue on Variational Analysis and Fixed Point Theory: Dedicated to Professor Wataru Takahashi on the occasion of his 70th birthday, and the two anonymous referees for helping us improve the original manuscript. This work was supported by the Japan Society for the Promotion of Science through a GrantinAid for JSPS Fellows (08J08592) and a GrantinAid for Young Scientists (B) (23760077).
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Iemoto, S., Hishinuma, K. & Iiduka, H. Approximate solutions to variational inequality over the fixed point set of a strongly nonexpansive mapping. Fixed Point Theory Appl 2014, 51 (2014). https://doi.org/10.1186/16871812201451
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Keywords
 variational inequality problem
 fixed point set
 strongly nonexpansive mapping
 monotone operator