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Line search fixed point algorithms based on nonlinear conjugate gradient directions: application to constrained smooth convex optimization
Fixed Point Theory and Applications volume 2016, Article number: 77 (2016)
Abstract
This paper considers the fixed point problem for a nonexpansive mapping on a real Hilbert space and proposes novel line search fixed point algorithms to accelerate the search. The termination conditions for the line search are based on the wellknown Wolfe conditions that are used to ensure the convergence and stability of unconstrained optimization algorithms. The directions to search for fixed points are generated by using the ideas of the steepest descent direction and conventional nonlinear conjugate gradient directions for unconstrained optimization. We perform convergence as well as convergence rate analyses on the algorithms for solving the fixed point problem under certain assumptions. The main contribution of this paper is to make a concrete response to an issue of constrained smooth convex optimization; that is, whether or not we can devise nonlinear conjugate gradient algorithms to solve constrained smooth convex optimization problems. We show that the proposed fixed point algorithms include ones with nonlinear conjugate gradient directions which can solve constrained smooth convex optimization problems. To illustrate the practicality of the algorithms, we apply them to concrete constrained smooth convex optimization problems, such as constrained quadratic programming problems and generalized convex feasibility problems, and numerically compare them with previous algorithms based on the Krasnosel’skiĭMann fixed point algorithm. The results show that the proposed algorithms dramatically reduce the running time and iterations needed to find optimal solutions to the concrete optimization problems compared with the previous algorithms.
1 Introduction
Consider the following fixed point problem (see [1], Chapter 4, [2], Chapter 3, [3], Chapter 1, [4], Chapter 3):
where H stands for a real Hilbert space with inner product \(\langle \cdot,\cdot\rangle\) and its induced norm \(\ \cdot\\), T is a nonexpansive mapping from H into itself (i.e., \(\ T(x)  T(y) \ \leq\xy\\) (\(x,y\in H\))), and one assumes \(\operatorname{Fix}(T) \neq\emptyset\). Problem (1.1) includes convex feasibility problems [5], [1], Example 5.21, constrained smooth convex optimization problems [6], Proposition 4.2, problems of finding the zeros of monotone operators [1], Proposition 23.38, and monotone variational inequalities [1], Subchapter 25.5.
There are useful algorithms for solving Problem (1.1), such as the Krasnosel’skiĭMann algorithm [1], Subchapter 5.2, [7], Subchapter 1.2, [8–10], the Halpern algorithm [7], Subchapter 1.2, [11, 12], and the hybrid method [13] (Solodov and Svaiter [14] proposed the hybrid method to solve problems of finding the zeros of monotone operators). This paper focuses on the Krasnosel’skiĭMann algorithm, which has practical applications, such as analyses of dynamic systems governed by maximal monotone operators [15] and nonsmooth convex variational signal recovery [16], defined as follows: given the current iterate \(x_{n} \in H\) and step size \(\alpha_{n} \in[0,1]\), the next iterate \(x_{n+1}\) of the algorithm is
Assuming that \((\alpha_{n})_{n\in\mathbb{N}}\) satisfies the condition
the sequence \((x_{n})_{n\in\mathbb{N}}\) generated by Algorithm (1.2) weakly converges to a fixed point of T (see, e.g., [1], Theorem 5.14). This result indicates that Algorithm (1.2) with constant step sizes (e.g., \(\alpha_{n} := \alpha\in(0,1)\) (\(n\in\mathbb {N}\))) or diminishing step sizes (e.g., \(\alpha_{n} := 1/(n+1)\) (\(n\in\mathbb{N}\))) can solve Problem (1.1). Propositions 10 and 11 in [8] indicate that Algorithm (1.2) with condition (1.3) has the following rate of convergence: for all \(n\in\mathbb{N}\),
(e.g., \(\x_{n}  T(x_{n})\ = O(1/\sqrt{n+1})\) when \(\alpha_{n} := \alpha\in(0,1)\) (\(n\in\mathbb{N}\))). This fact implies that Algorithm (1.2) with (1.3) does not always have fast convergence and has motivated the development of modifications and variants for the Krasnosel’skiĭMann algorithm in order to accelerate Algorithm (1.2).
One approach to accelerate Algorithm (1.2) with (1.3) is to develop line search methods that can determine a more adequate step size than a step size satisfying (1.3) at each iteration n so that the value of \(\x_{n+1}  T(x_{n+1})\\) decreases dramatically. Magnanti and Perakis proposed an adaptive line search framework [17], Section 2, that can determine step sizes to satisfy weaker conditions [17], Assumptions A1 and A2, than (1.3). On the basis of this framework, they showed that Algorithm (1.2), with step sizes \(\alpha_{n}\) satisfying the following Armijotype condition, converges to a fixed point of T [17], Theorems 4 and 8: given \(x_{n} \in\mathbb{R}^{N}\), \(\beta> 0\), \(D > 0\), and \(b\in(0,1)\), choose the smallest nonnegative integer \(l_{n}\) so that \(\alpha_{n} = b^{l_{n}}\) satisfies the condition
where \(g_{n} \colon[0,1] \to\mathbb{R}\) is a potential function [17], Scheme IV, defined for all \(\alpha\in[0,1]\) by
Theorem 5 in [17] shows that Algorithm (1.2) with the Armijotype condition (1.5) satisfies \(\ x_{n+1}  T(x_{n+1}) \^{2} \leq[1  \beta(\alpha_{n}  1/2)^{2}] \ x_{n} T(x_{n}) \^{2}\) (\(n\in\mathbb{N}\)), which implies that the algorithm has, for all \(n\in\mathbb{N}\),
In this paper, we introduce a line search framework using \(P_{n}\) defined by (1.8), (1.9), and (1.10), which is the simplest of all potential functions including \(g_{n}\) defined as in (1.6): given \(x_{n}, d_{n} \in H\), for all \(\alpha\in[0,1]\),
When \(d_{n} := (x_{n}  T (x_{n}))\) and \(\alpha_{n}\) is given as in (1.3), the point \(x_{n}(\alpha_{n})\) in (1.8) coincides with \(x_{n+1}\) defined by Algorithm (1.2) with (1.3).
Consider the following problem of minimizing \(P_{n}\) over \([0,1]\):
When the solution \(\alpha_{n}\) to Problem (1.11) can be obtained in each iteration, \(P_{n}(\alpha_{n}) \leq P_{n}(0)\) holds for all \(n \in\mathbb{N}\). Accordingly, if the next iterate \(x_{n+1}\) is defined by \(x_{n+1} := x_{n} (\alpha_{n})\), \(\ x_{n+1}  T ( x_{n+1} ) \ \leq\ x_{n}  T (x_{n}) \\) (\(n\in\mathbb {N}\)) holds, i.e., \((\x_{n}  T(x_{n}) \)_{n\in\mathbb{N}}\) is monotone decreasing. Since the exact solution to Problem (1.11) cannot easily be obtained, the step size \(\alpha_{n}\) can be chosen so as to yield an approximate minimum for Problem (1.11) in each iteration, specifically, to satisfy the following Wolfetype conditions [18, 19]: given \(x_{n}, d_{n} \in H\), and \(\delta, \sigma\in(0,1)\) with \(\delta\leq\sigma\),
Condition (1.12) is the Armijotype condition for \(P_{n}\) (see (1.5) for the Armijotype condition with \(d_{n} :=  (x_{n}  T(x_{n}))\) for the potential function \(g_{n}\)). Under the conditions that \(d_{n} :=  (x_{n}  T(x_{n}))\) and \(x_{n+1} := x_{n}(\alpha_{n})\) (\(n\in\mathbb{N}\)), Algorithm (1.2) with (1.12) satisfies \(\ x_{n+1}  T(x_{n+1}) \^{2} \leq(1  \delta\alpha_{n}) \x_{n}  T(x_{n})\^{2}\) (\(n\in\mathbb{N}\)), which implies that, for all \(n\in \mathbb{N}\),^{Footnote 1}
Here, let us see how the step size conditions (1.3), (1.5), (1.12), and (1.13) affect the efficiency of Algorithm (1.2). Algorithm (1.2) with (1.3) satisfies \(\ x_{n+1}  T ( x_{n+1} ) \^{2} \leq\ x_{n}  T (x_{n}) \^{2}\) (\(n\in\mathbb{N}\)) [1], (5.14), while Algorithm (1.2) with each of (1.5) and (1.12) satisfies \(\ x_{n+1}  T ( x_{n+1} ) \^{2} < \ x_{n}  T (x_{n}) \^{2}\) (\(n\in \mathbb{N}\)). Hence, it can be expected that Algorithm (1.2) with each of (1.5) and (1.12) performs better than Algorithm (1.2) with (1.3). Since the Armijotype conditions (1.5) and (1.12) are satisfied for all sufficiently small values of \(\alpha_{n}\) [20], Subchapter 3.1, there is a possibility that Algorithm (1.2) with only the Armijotype condition (1.5) does not make reasonable progress. Meanwhile, (1.13) based on the curvature condition [20], Subchapter 3.1, is used to ensure that \(\alpha_{n}\) is not too small and that unacceptably short steps are ruled out. Therefore, the Wolfetype conditions (1.12) and (1.13) should be used to secure efficiency of the algorithm. Moreover, even when \(\alpha_{n}\) satisfying (1.5) is not small enough, it can be expected that Algorithm (1.2) with the Wolfetype conditions (1.12) and (1.13) will have a better convergence rate than Algorithm (1.2) with the Armijotype condition (1.5) because of (1.7), (1.14), and \((\alpha 1/2)^{2} \leq\alpha\) (\(\alpha\in [(2\sqrt{3})/2,1]\)). Section 3 introduces the line search algorithm [21], Algorithm 4.6, to compute step sizes satisfying (1.12) and (1.13) with appropriately chosen δ and σ and gives performance comparisons of Algorithm (1.2) with each of (1.3) and (1.5) with the one with (1.12) and (1.13).
The main concern regarding this line search is how the direction \(d_{n}\) should be updated to accelerate the search for a fixed point of T. To address this concern, the following problem will be discussed:
where \(f\colon H \to\mathbb{R}\) is convex and Fréchet differentiable and \(\nabla f \colon H \to H\) is Lipschitz continuous with a constant L. Let us define \(T^{(f)} \colon H \to H\) by
where Id stands for the identity mapping on H and \(\lambda> 0\). The mapping \(T^{(f)}\) satisfies the nonexpansivity condition for \(\lambda\in(0,2/L]\) [22], Proposition 2.3, and \(\operatorname{Fix}(T^{(f)})\) coincides with the solution set of Problem (1.15). From \(T^{(f)} (x)x = (x  \lambda\nabla f(x)) x =  \lambda\nabla f(x)\) (\(\lambda> 0\), \(x\in H\)), Algorithm (1.2) for solving Problem (1.15) is
This means that the direction \(d_{n}^{(f)} := (x_{n}  T^{(f)} (x_{n})) =  \lambda\nabla f (x_{n})\) is the steepest descent direction of f at \(x_{n}\) and Algorithm (1.2) with \(T^{(f)}\) (i.e., Algorithm (1.17)) is the steepest descent method [20], Subchapter 3.3, for Problem (1.15).
There are many algorithms with useful search directions [20], Chapters 519, to accelerate the steepest descent method for unconstrained optimization. In particular, algorithms with nonlinear conjugate gradient directions [23], [20], Subchapter 5.2,
where \(\beta_{n} \in\mathbb{R}\), have been widely used as efficient accelerated versions for most gradient methods. Wellknown formulas for \(\beta_{n}\) include the HestenesStiefel (HS) [24], FletcherReeves (FR) [25], PolakRibièrePolyak (PRP) [26, 27], and DaiYuan (DY) [28] formulas:
where \(y_{n} := \nabla f (x_{n+1})  \nabla f(x_{n})\).
Motivated by these observations, we decided to use the following direction to accelerate the search for a fixed point of T, which can be obtained by replacing ∇f in (1.18) with \(\mathrm {Id}  T\) (see also (1.16) for the relationship between ∇f and \(T^{(f)}\)): given the current direction \(d_{n} \in H\), the current iterate \(x_{n} \in H\), and a step size \(\alpha_{n}\) satisfying (1.12) and (1.13), the next direction \(d_{n+1}\) is defined by
where \(\beta_{n}\) is given by one of the formulas in (1.19) when \(\nabla f = \mathrm{Id}  T\).
This paper proposes iterative algorithms (Algorithm 2.1) that use the direction (1.20) and step sizes satisfying the Wolfetype conditions (1.12) and (1.13) for solving Problem (1.1) and describes their convergence analyses (Theorems 2.12.5). We also provide their convergence rate analyses (Theorem 2.6).
The main contribution of this paper is to enable us to propose nonlinear conjugate gradient algorithms for constrained smooth convex optimization which are examples of the proposed line search fixed point algorithms, in contrast to the previously reported results for nonlinear conjugate gradient algorithms for unconstrained smooth nonconvex optimization [20], Subchapter 5.2, [23–29]. Concretely speaking, our nonlinear conjugate gradient algorithms are obtained in the following steps. Given a nonempty, closed, and convex set \(C \subset H\) and a convex function \(f \colon H \to\mathbb{R}\) with the Lipschitz continuous gradient, let us define
where \(\lambda\in(0,2/L]\), L is the Lipschitz constant of ∇f, and \(P_{C}\) stands for the metric projection onto C. Then Proposition 2.3 in [22] indicates that the mapping T is nonexpansive and satisfies
From (1.20) with \(T := P_{C} ( \mathrm{Id}  \lambda\nabla f )\), the proposed nonlinear conjugate gradient algorithms for finding a point in \(\operatorname{Fix}(T) = \operatorname{argmin}_{x\in C} f(x)\) can be expressed as follows: given \(x_{n}, d_{n} \in H\) and \(\alpha_{n}\) satisfying (1.12) and (1.13),
where \(\beta_{n} \in\mathbb{R}\) is each of the following formulas:^{Footnote 2}
where \(y_{n} := (x_{n+1}  P_{C} ( x_{n+1}  \lambda\nabla f (x_{n+1}) ))  (x_{n}  P_{C} ( x_{n}  \lambda\nabla f (x_{n}) ))\). Our convergence analyses are performed by referring to useful results on unconstrained smooth nonconvex optimization (see [18, 19, 23, 28, 30–32] and references therein) because the proposed fixed point algorithms are based on the steepest descent and nonlinear conjugate gradient directions for unconstrained smooth nonconvex optimization (see (1.15)(1.20)). We would like to emphasize that combining unconstrained smooth nonconvex optimization theory with fixed point theory for nonexpansive mappings enables us to develop the novel nonlinear conjugate gradient algorithms for constrained smooth convex optimization. The nonlinear conjugate gradient algorithms are a concrete response to the issue of constrained smooth convex optimization that is whether or not we can present nonlinear conjugate gradient algorithms to solve constrained smooth convex optimization problems.
To verify whether the proposed nonlinear conjugate gradient algorithms are accelerations for solving practical problems, we apply them to constrained quadratic programming problems (Section 3.2) and generalized convex feasibility problems (Section 3.3) (see [6, 33] and references therein for the relationship between the generalized convex feasibility problem and signal processing problems), which are constrained smooth convex optimization problems and particularly interesting applications of Problem (1.1). Moreover, we numerically compare their abilities to solve concrete constrained quadratic programming problems and generalized convex feasibility problems with those of previous algorithms based on the Krasnosel’skiĭMann algorithm (Algorithm (1.2) with step sizes satisfying (1.3) and Algorithm (1.2) with step sizes satisfying (1.5)) and show that they can find optimal solutions to these problems faster than the previous ones.
Throughout this paper, we shall let \(\mathbb{N}\) be the set of zero and all positive integers, \(\mathbb{R}^{d}\) be a ddimensional Euclidean space, H be a real Hilbert space with inner product \(\langle\cdot, \cdot\rangle\) and its induced norm \(\ \cdot\\), and \(T\colon H \to H\) be a nonexpansive mapping with \(\operatorname{Fix}(T) := \{ x\in H \colon T(x) = x \} \neq\emptyset\).
2 Line search fixed point algorithms based on nonlinear conjugate gradient directions
Let us begin by explicitly stating our algorithm for solving Problem (1.1) discussed in Section 1.
Algorithm 2.1
 Step 0.:

Take \(\delta, \sigma\in(0,1)\) with \(\delta\leq\sigma\). Choose \(x_{0} \in H\) arbitrarily and set \(d_{0} := (x_{0}  T(x_{0}))\) and \(n:= 0\).
 Step 1.:

Compute \(\alpha_{n} \in(0,1]\) satisfying
$$\begin{aligned}& \bigl\Vert x_{n} ( \alpha_{n} )  T \bigl( x_{n} ( \alpha _{n} ) \bigr) \bigr\Vert ^{2}  \bigl\Vert x_{n}  T ( x_{n} ) \bigr\Vert ^{2} \leq\delta \alpha_{n} \bigl\langle x_{n}  T (x_{n} ), d_{n} \bigr\rangle , \end{aligned}$$(2.1)$$\begin{aligned}& \bigl\langle x_{n} ( \alpha_{n} )  T \bigl(x_{n} ( \alpha_{n} ) \bigr), d_{n} \bigr\rangle \geq\sigma \bigl\langle x_{n}  T (x_{n} ), d_{n} \bigr\rangle , \end{aligned}$$(2.2)where \(x_{n}(\alpha_{n}) := x_{n} + \alpha_{n} d_{n}\). Compute \(x_{n+1} \in H\) by
$$ x_{n+1} := x_{n} + \alpha_{n} d_{n}. $$(2.3)  Step 2.:

If \(\ x_{n+1}  T(x_{n+1}) \= 0\), stop. Otherwise, go to Step 3.
 Step 3.:

Compute \(\beta_{n} \in\mathbb{R}\) by using each of the following formulas:
$$\begin{aligned}& \beta_{n}^{\mathrm{SD}} := 0, \\& \beta_{n}^{\mathrm{HS}+} := \max \biggl\{ \frac{ \langle x_{n+1}  T (x_{n+1} ), y_{n} \rangle}{ \langle d_{n}, y_{n} \rangle}, 0 \biggr\} , \qquad \beta_{n}^{\mathrm{FR}} := \frac{\Vert x_{n+1}  T (x_{n+1} ) \Vert ^{2}}{\Vert x_{n}  T (x_{n} ) \Vert ^{2}}, \\& \beta_{n}^{\mathrm{PRP}+} := \max \biggl\{ \frac{ \langle x_{n+1}  T (x_{n+1} ), y_{n} \rangle}{ \Vert x_{n}  T (x_{n} ) \Vert ^{2}}, 0 \biggr\} ,\qquad \beta_{n}^{\mathrm{DY}} := \frac{\Vert x_{n+1}  T (x_{n+1} ) \Vert ^{2}}{ \langle d_{n}, y_{n} \rangle}, \end{aligned}$$(2.4)where \(y_{n} := (x_{n+1}  T(x_{n+1}))  (x_{n}  T(x_{n}))\). Generate \(d_{n+1} \in H\) by
$$ d_{n+1} :=  \bigl( x_{n+1}  T (x_{n+1} ) \bigr) + \beta_{n} d_{n}. $$  Step 4.:

Put \(n := n+1\) and go to Step 1.
We need to use appropriate line search algorithms to compute \(\alpha _{n}\) (\(n\in\mathbb{N}\)) satisfying (2.1) and (2.2). In Section 3, we use a useful one (Algorithm 3.1) [21], Algorithm 4.6, that can obtain the step sizes satisfying (2.1) and (2.2) whenever the line search algorithm terminates [21], Theorem 4.7. Although the efficiency of the line search algorithm depends on the parameters δ and σ, thanks to the reference [21], Section 6.1, we can set appropriate δ and σ before executing it [21], Algorithm 4.6, and Algorithm 2.1. See Section 3 for the numerical performance of the line search algorithm [21], Algorithm 4.6, and Algorithm 2.1.
It can be seen that Algorithm 2.1 is well defined when \(\beta _{n}\) is defined by \(\beta_{n}^{\mathrm{SD}}\), \(\beta_{n}^{\mathrm{FR}}\), or \(\beta_{n}^{\mathrm{PRP}+}\). The discussion in Section 2.2 shows that Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{DY}}\) is well defined (Lemma 2.3(i)). Moreover, it is guaranteed that under certain assumptions, Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm {HS}+}\) is well defined (Theorem 2.5).
2.1 Algorithm 2.1 with \(\beta_{n} = \beta _{n}^{\mathrm{SD}}\)
This subsection considers Algorithm 2.1 with \(\beta _{n}^{\mathrm{SD}}\) (\(n\in\mathbb{N}\)), which is based on the steepest descent (SD) direction (see (1.17)), i.e.,
Theorems 4 and 8 in [17] indicate that, if \((\alpha _{n})_{n\in\mathbb{N}}\) satisfies the Armijotype condition (1.5), Algorithm (2.5) converges to a fixed point of T. The following theorem says that Algorithm (2.5), with \((\alpha _{n})_{n\in\mathbb{N}}\) satisfying the Wolfetype conditions (2.1) and (2.2), converges to a fixed point of T.
Theorem 2.1
Suppose that \((x_{n})_{n\in\mathbb{N}}\) is the sequence generated by Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{SD}}\) (\(n\in\mathbb{N}\)). Then \((x_{n})_{n\in\mathbb{N}}\) either terminates at a fixed point of T or
In the latter situation, \((x_{n})_{n\in\mathbb{N}}\) weakly converges to a fixed point of T.
2.1.1 Proof of Theorem 2.1
If \(m \in\mathbb{N}\) exists such that \(\ x_{m}  T(x_{m}) \ = 0\), Theorem 2.1 holds. Accordingly, it can be assumed that, for all \(n\in\mathbb{N}\), \(\ x_{n}  T (x_{n}) \ \neq0\) holds.
First, the following lemma can be proven by referring to [18, 19, 32].
Lemma 2.1
Let \((x_{n})_{n\in\mathbb{N}}\) and \((d_{n})_{n\in\mathbb{N}}\) be the sequences generated by Algorithm 2.1. Assume that \(\langle x_{n}  T(x_{n}), d_{n} \rangle< 0\) for all \(n\in \mathbb{N}\). Then
Proof
The CauchySchwarz inequality and the triangle inequality ensure that, for all \(n\in\mathbb{N}\), \(\langle d_{n}, ( x_{n+1}  T ( x_{n+1}) )  (x_{n}  T (x_{n} ) ) \rangle \leq \ d_{n} \ \ ( x_{n+1}  T ( x_{n+1}) )  (x_{n}  T (x_{n} ) ) \ \leq \ d_{n} \ ( \ T ( x_{n} )  T (x_{n+1} ) \ + \ x_{n+1}  x_{n} \ )\), which, together with the nonexpansivity of T and (2.3), implies that, for all \(n\in\mathbb{N}\),
Moreover, (2.2) means that, for all \(n\in\mathbb{N}\),
Accordingly, for all \(n\in\mathbb{N}\),
Since \(\d_{n}\ \neq0\) (\(n\in\mathbb{N}\)) holds from \(\langle x_{n}  T(x_{n}), d_{n} \rangle< 0\) (\(n\in\mathbb{N}\)), we find that, for all \(n\in\mathbb{N}\),
Condition (2.1) means that, for all \(n\in\mathbb{N}\), \(\ x_{n+1}  T(x_{n+1} )\^{2}  \x_{n}  T (x_{n}) \^{2} \leq\delta\alpha_{n} \langle x_{n}  T (x_{n} ), d_{n} \rangle\), which, together with \(\langle x_{n}  T(x_{n}), d_{n} \rangle< 0\) (\(n\in \mathbb{N}\)), implies that, for all \(n\in\mathbb{N}\),
From (2.6) and (2.7), for all \(n\in\mathbb{N}\),
which implies that, for all \(n\in\mathbb{N}\),
Summing up this inequality from \(n=0\) to \(n=N \in\mathbb{N}\) guarantees that, for all \(N\in\mathbb{N}\),
Therefore, the conclusion in Lemma 2.1 is satisfied. □
Lemma 2.1 leads to the following.
Lemma 2.2
Suppose that the assumptions in Theorem 2.1 are satisfied. Then:

(i)
\(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \= 0\).

(ii)
\((\ x_{n}  x \)_{n\in\mathbb{N}}\) is monotone decreasing for all \(x\in\operatorname{Fix}(T)\).

(iii)
\((x_{n})_{n\in\mathbb{N}}\) weakly converges to a point in \(\operatorname{Fix}(T)\).
Items (i) and (iii) in Lemma 2.2 indicate that Theorem 2.1 holds under the assumption that \(\ x_{n}  T (x_{n}) \ \neq0\) (\(n\in\mathbb{N}\)).
Proof
(i) In the case where \(\beta_{n} := \beta_{n}^{\mathrm{SD}} = 0\) (\(n\in \mathbb{N}\)), \(d_{n} =  (x_{n}  T(x_{n}))\) holds for all \(n\in\mathbb{N}\). Hence, \(\langle x_{n}  T(x_{n}), d_{n} \rangle=  \x_{n}  T(x_{n})\^{2} < 0\) (\(n\in\mathbb{N}\)). Lemma 2.1 thus guarantees that \(\sum_{n=0}^{\infty}\ x_{n}  T ( x_{n} ) \^{2} < \infty\), which implies \(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \= 0\).
(ii) The triangle inequality and the nonexpansivity of T ensure that, for all \(n\in\mathbb{N}\) and for all \(x\in\operatorname{Fix}(T)\), \(\ x_{n+1}  x \ = \ x_{n} + \alpha_{n} ( T (x_{n})  x_{n} )  x \ \leq(1\alpha_{n} ) \ x_{n}  x \ + \alpha_{n} \T (x_{n})  T (x)\ \leq\ x_{n}  x \\).
(iii) Lemma 2.2(ii) means that \(\lim_{n\to\infty} \x_{n}  x\\) exists for all \(x\in\operatorname{Fix}(T)\). Accordingly, \((x_{n})_{n\in \mathbb{N}}\) is bounded. Hence, there is a subsequence \((x_{n_{k}})_{k\in\mathbb{N}}\) of \((x_{n})_{n\in\mathbb{N}}\) such that \((x_{n_{k}})_{k\in\mathbb{N}}\) weakly converges to a point \(x^{*} \in H\). Here, let us assume that \(x^{*} \notin\operatorname{Fix}(T)\). Then Opial’s condition [34], Lemma 1, Lemma 2.2(i), and the nonexpansivity of T guarantee that
which is a contradiction. Hence, \(x^{*} \in\operatorname{Fix}(T)\). Let us take another subsequence \((x_{n_{i}})_{i\in\mathbb{N}}\) (\(\subset(x_{n})_{n\in\mathbb{N}}\)) which weakly converges to \(x_{*} \in H\). A similar discussion to the one for obtaining \(x^{*} \in\operatorname{Fix}(T)\) ensures that \(x_{*} \in\operatorname{Fix}(T)\). Assume that \(x^{*} \neq x_{*}\). The existence of \(\lim_{n\to\infty} \ x_{n}  x \\) (\(x\in\operatorname{Fix}(T)\)) and Opial’s condition [34], Lemma 1, imply that
which is a contradiction. Therefore, \(x^{*} = x_{*}\). Since any subsequence of \((x_{n})_{n\in\mathbb{N}}\) weakly converges to the same fixed point of T, it is guaranteed that the whole \((x_{n})_{n\in\mathbb{N}}\) weakly converges to a fixed point of T. This completes the proof. □
2.2 Algorithm 2.1 with \(\beta_{n} = \beta _{n}^{\mathrm{DY}}\)
The following is a convergence analysis of Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{DY}}\).
Theorem 2.2
Suppose that \((x_{n})_{n\in\mathbb{N}}\) is the sequence generated by Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{DY}}\) (\(n\in\mathbb{N}\)). Then \((x_{n})_{n\in\mathbb{N}}\) either terminates at a fixed point of T or
2.2.1 Proof of Theorem 2.2
Since the existence of \(m\in\mathbb{N}\) such that \(\ x_{m}  T(x_{m}) \ = 0\) implies that Theorem 2.2 holds, it can be assumed that, for all \(n\in\mathbb{N}\), \(\ x_{n}  T (x_{n}) \ \neq0\) holds. Theorem 2.2 can be proven by using the ideas presented in the proof of [28], Theorem 3.3. The proof of Theorem 2.2 is divided into three steps.
Lemma 2.3
Suppose that the assumptions in Theorem 2.2 are satisfied. Then:

(i)
\(\langle x_{n}  T(x_{n}), d_{n} \rangle< 0\) (\(n\in\mathbb{N}\)).

(ii)
\(\liminf_{n\to\infty} \ x_{n}  T(x_{n}) \= 0\).

(iii)
\(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \= 0\).
Proof
(i) From \(d_{0} :=  (x_{0}  T(x_{0}))\), \(\langle x_{0}  T(x_{0}), d_{0} \rangle=  \x_{0}  T(x_{0}) \^{2} < 0\). Suppose that \(\langle x_{n}  T(x_{n}), d_{n} \rangle< 0\) holds for some \(n\in\mathbb{N}\). Accordingly, the definition of \(y_{n}:= (x_{n+1}  T(x_{n+1}))  (x_{n}  T(x_{n}))\) and (2.2) ensure that
which implies that
From the definition of \(d_{n+1} :=  (x_{n+1}  T(x_{n+1})) + \beta _{n}^{\mathrm{DY}} d_{n}\), we have
which, together with the definitions of \(y_{n}\) and \(\beta_{n}^{\mathrm {DY}}\) (>0), implies that
Induction shows that \(\langle x_{n}  T(x_{n}), d_{n} \rangle< 0\) for all \(n\in\mathbb{N}\). This implies \(\beta_{n}^{\mathrm{DY}} > 0\) (\(n\in\mathbb{N}\)); i.e., Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{DY}}\) is well defined.
(ii) Assume that \(\liminf_{n\to\infty} \ x_{n}  T(x_{n}) \ > 0\). Then there exist \(n_{0} \in\mathbb{N}\) and \(\varepsilon> 0\) such that \(\x_{n}  T(x_{n})\ \geq\varepsilon\) for all \(n \geq n_{0}\). Since we have assumed that \(\x_{n}  T(x_{n})\ \neq0\) (\(n\in\mathbb {N}\)), we may further assume that \(\x_{n}  T(x_{n})\ \geq\varepsilon\) for all \(n \in\mathbb{N}\). From the definition of \(d_{n+1} :=  (x_{n+1}  T(x_{n+1})) + \beta _{n}^{\mathrm{DY}} d_{n}\) (\(n\in\mathbb{N}\)), we have, for all \(n\in\mathbb{N}\),
Lemma 2.3(i) and (2.8) mean that, for all \(n\in \mathbb{N}\),
Hence, for all \(n\in\mathbb{N}\),
Summing up this inequality from \(n=0\) to \(n=N\in\mathbb{N}\) yields, for all \(N\in\mathbb{N}\),
which, which together with \(\x_{n}  T(x_{n})\ \geq\varepsilon\) (\(n \in \mathbb{N}\)) and \(d_{0} := (x_{0} T(x_{0}))\), implies that, for all \(N\in\mathbb{N}\),
Since Lemma 2.3(i) implies \(\ d_{n} \ \neq0\) (\(n\in\mathbb{N}\)), we have, for all \(N \in\mathbb{N}\),
Therefore, Lemma 2.1 guarantees that
This is a contradiction. Hence, \(\liminf_{n\to\infty} \x_{n}  T(x_{n})\ =0\).
(iii) Condition (2.1) and Lemma 2.3(i) lead to that, for all \(n\in\mathbb{N}\),
Accordingly, \((\ x_{n}  T(x_{n}) \)_{n\in\mathbb{N}}\) is monotone decreasing; i.e., there exists \(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \\). Lemma 2.3(ii) thus ensures that \(\lim_{n\to\infty} \x_{n}  T(x_{n})\ = 0\). This completes the proof. □
2.3 Algorithm 2.1 with \(\beta_{n} = \beta _{n}^{\mathrm{FR}}\)
To establish the convergence of Algorithm 2.1 when \(\beta_{n} = \beta_{n}^{\mathrm{FR}}\), we assume that the step sizes \(\alpha_{n}\) satisfy the strong Wolfetype conditions, which are (2.1) and the following strengthened version of (2.2): for \(\sigma\leq1/2\),
See [30] on the global convergence of the FR method for unconstrained optimization under the strong Wolfe conditions.
The following is a convergence analysis of Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{FR}}\).
Theorem 2.3
Suppose that \((x_{n})_{n\in\mathbb{N}}\) is the sequence generated by Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{FR}}\) (\(n\in\mathbb {N}\)), where \((\alpha_{n})_{n\in\mathbb{N}}\) satisfies (2.1) and (2.9). Then \((x_{n})_{n\in\mathbb{N}}\) either terminates at a fixed point of T or
2.3.1 Proof of Theorem 2.3
It can be assumed that, for all \(n\in\mathbb{N}\), \(\ x_{n}  T (x_{n}) \ \neq0\) holds. Theorem 2.3 can be proven by using the ideas in the proof of [30], Theorem 2.
Lemma 2.4
Suppose that the assumptions in Theorem 2.3 are satisfied. Then:

(i)
\(\langle x_{n}  T(x_{n}), d_{n} \rangle< 0\) (\(n\in\mathbb{N}\)).

(ii)
\(\liminf_{n\to\infty} \ x_{n}  T(x_{n}) \= 0\).

(iii)
\(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \= 0\).
Proof
(i) Let us show that, for all \(n\in\mathbb{N}\),
From \(d_{0} :=  (x_{0}  T(x_{0}))\), (2.10) holds for \(n:= 0\) and \(\langle x_{0}  T(x_{0}), d_{0} \rangle< 0\). Suppose that (2.10) holds for some \(n\in\mathbb{N}\). Accordingly, from \(\sum_{j=0}^{n} \sigma^{j} < \sum_{j=0}^{\infty}\sigma ^{j} = 1/(1\sigma)\) and \(\sigma\in(0,1/2]\), we have
which implies that \(\langle x_{n}  T (x_{n} ), d_{n} \rangle< 0\). The definitions of \(d_{n+1}\) and \(\beta_{n}^{\mathrm{FR}}\) enable us to deduce that
Since (2.9) satisfies \(\sigma\langle x_{n}  T(x_{n}),d_{n} \rangle\leq\langle x_{n+1}  T(x_{n+1}),d_{n} \rangle\leq \sigma\langle x_{n}  T(x_{n}),d_{n} \rangle\) and (2.10) holds for some n, it is found that
and
Hence,
A discussion similar to the one for obtaining \(\langle x_{n} T(x_{n}), d_{n} \rangle< 0\) guarantees that \(\langle x_{n+1}  T(x_{n+1}), d_{n+1} \rangle< 0\) holds. Induction thus shows that (2.10) and \(\langle x_{n} T(x_{n}), d_{n} \rangle< 0\) hold for all \(n\in\mathbb{N}\).
(ii) Assume that \(\liminf_{n\to\infty} \ x_{n}  T(x_{n}) \ > 0\). A discussion similar to the one in the proof of Lemma 2.3(ii) ensures the existence of \(\varepsilon> 0\) such that \(\x_{n}  T(x_{n})\ \geq\varepsilon\) for all \(n \in\mathbb{N}\). From (2.9) and (2.10), we have, for all \(n\in \mathbb{N}\),
which, together with \(\sum_{j=1}^{n+1} \sigma^{j} < \sum_{j=1}^{\infty} \sigma^{j} = \sigma/(1  \sigma)\) and \(\beta_{n}^{\mathrm{FR}} := \ x_{n+1}  T ( x_{n+1} ) \^{2}/\ x_{n}  T(x_{n}) \^{2}\) (\(n\in\mathbb{N}\)), implies that, for all \(n\in\mathbb{N}\),
Accordingly, from the definition of \(d_{n+1} :=  (x_{n+1}  T(x_{n+1})) + \beta_{n}^{\mathrm{FR}} d_{n}\), we find that, for all \(n\in\mathbb{N}\),
which means that, for all \(n\in\mathbb{N}\),
The sum of this inequality from \(n=0\) to \(n=N \in\mathbb{N}\) and \(d_{0} :=  (x_{0}  T(x_{0}))\) ensure that, for all \(N\in\mathbb{N}\),
From \(\x_{n}  T(x_{n})\ \geq\varepsilon\) (\(n \in\mathbb{N}\)), for all \(N\in\mathbb{N}\),
Therefore, from Lemma 2.4(i) guaranteeing that \(\d_{n}\\neq 0\) (\(n\in\mathbb{N}\)) and \(\sum_{k=1}^{\infty}\varepsilon^{2} ( 1  \sigma)/( ( 1 + \sigma) (k 1) + 2) = \infty\), it is found that
Meanwhile, since (2.10) guarantees that \(\langle x_{n}  T(x_{n}), d_{n} \rangle \leq(2 + \sum_{j=0}^{n} \sigma^{j} ) \ x_{n}  T(x_{n}) \^{2} < ((12 \sigma)/(1\sigma)) \ x_{n}  T(x_{n}) \^{2}\) (\(n\in\mathbb{N}\)), Lemma 2.1 and Lemma 2.4(i) lead to the deduction that
which is a contradiction. Therefore, \(\liminf_{n\to\infty} \ x_{n}  T(x_{n}) \ = 0\).
(iii) A discussion similar to the one in the proof of Lemma 2.3(iii) leads to Lemma 2.4(iii). This completes the proof. □
2.4 Algorithm 2.1 with \(\beta_{n} = \beta _{n}^{\mathrm{PRP}+}\)
It is well known that the convergence of the nonlinear conjugate gradient method with \(\beta_{n}^{\mathrm{PRP}}\) defined as in (1.19) for a general nonlinear function is uncertain [23], Section 5. To guarantee the convergence of the PRP method for unconstrained optimization, the following modification of \(\beta _{n}^{\mathrm{PRP}}\) was presented in [35]: for \(\beta _{n}^{\mathrm{PRP}}\) defined as in (1.19), \(\beta_{n}^{\mathrm {PRP}+} := \max\{ \beta_{n}^{\mathrm{PRP}}, 0 \}\). On the basis of the idea behind this modification, this subsection considers Algorithm 2.1 with \(\beta_{n}^{\mathrm{PRP}+}\) defined as in (2.4).
Theorem 2.4
Suppose that \((x_{n})_{n\in\mathbb{N}}\) and \((d_{n})_{n\in\mathbb{N}}\) are the sequences generated by Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{PRP}+}\) (\(n\in\mathbb{N}\)) and there exists \(c > 0\) such that \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \ x_{n}  T(x_{n}) \^{2}\) for all \(n\in\mathbb{N}\). If \((x_{n})_{n\in\mathbb{N}}\) is bounded, then \((x_{n})_{n\in\mathbb{N}}\) either terminates at a fixed point of T or
2.4.1 Proof of Theorem 2.4
It can be assumed that \(\ x_{n}  T (x_{n}) \ \neq0\) holds for all \(n\in \mathbb{N}\). Let us first show the following lemma by referring to the proof of [31], Lemma 4.1.
Lemma 2.5
Let \((x_{n})_{n\in\mathbb{N}}\) and \((d_{n})_{n\in\mathbb{N}}\) be the sequences generated by Algorithm 2.1 with \(\beta_{n} \geq0\) (\(n\in\mathbb{N}\)) and assume that there exists \(c > 0\) such that \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \x_{n}  T(x_{n})\^{2}\) for all \(n\in\mathbb{N}\). If there exists \(\varepsilon> 0\) such that \(\x_{n}  T(x_{n})\ \geq \varepsilon\) for all \(n\in\mathbb{N}\), then \(\sum_{n=0}^{\infty}\ u_{n+1}  u_{n} \^{2}< \infty\), where \(u_{n} := d_{n}/\d_{n}\\) (\(n\in\mathbb{N}\)).
Proof
Assuming \(\ x_{n}  T (x_{n}) \ \geq\varepsilon\) and \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \x_{n}  T(x_{n})\^{2}\) (\(n\in\mathbb{N}\)), \(\ d_{n} \\neq0\) holds for all \(n\in\mathbb{N}\). Define \(r_{n} :=  (x_{n}  T(x_{n}))/\d_{n}\\) and \(\delta_{n} := \beta_{n} \ d_{n}\/\ d_{n+1} \\) (\(n\in\mathbb{N}\)). From \(\delta_{n} u_{n} = \beta_{n} d_{n} /\ d_{n+1}\\) and \(d_{n+1} =  (x_{n+1}  T(x_{n+1})) + \beta_{n} d_{n}\) (\(n\in\mathbb{N}\)), we have, for all \(n\in\mathbb{N}\),
which, together with \(\ u_{n+1}  \delta_{n} u_{n} \^{2} = \u_{n+1}\^{2} 2 \delta_{n} \langle u_{n+1}, u_{n} \rangle+ \delta_{n}^{2} \u_{n}\^{2} = \u_{n}\^{2} 2 \delta_{n} \langle u_{n}, u_{n+1} \rangle+ \delta_{n}^{2} \u_{n+1} \^{2} = \ u_{n}  \delta_{n} u_{n+1} \^{2}\) (\(n\in\mathbb{N}\)), implies that, for all \(n\in\mathbb{N}\),
Accordingly, the condition \(\beta_{n} \geq0\) (\(n\in\mathbb{N}\)) and the triangle inequality mean that, for all \(n\in\mathbb{N}\),
From Lemma 2.1, \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \ x_{n}  T(x_{n})\^{2}\) (\(n\in\mathbb{N}\)), the definition of \(r_{n}\), and \(\ x_{n}  T(x_{n}) \ \geq\varepsilon\) (\(n\in\mathbb{N}\)), we have
which, together with (2.11), completes the proof. □
The following property, referred to as Property (⋆), is a result of modifying [31], Property (∗), to conform to Problem (1.1).
 Property (⋆).:

Suppose that there exist positive constants γ and γ̄ such that \(\gamma\leq\ x_{n}  T(x_{n}) \ \leq\bar{\gamma}\) for all \(n\in\mathbb{N}\). Then Property (⋆) holds if \(b > 1\) and \(\lambda> 0\) exist such that, for all \(n\in\mathbb{N}\),
$$ \vert \beta_{n} \vert \leq b \quad \text{and}\quad \Vert x_{n+1}  x_{n} \Vert \leq\lambda\quad \text{implies} \quad \vert \beta_{n} \vert \leq\frac{1}{2b}. $$
The proof of the following lemma can be omitted since it is similar to the proof of [31], Lemma 4.2.
Lemma 2.6
Let \((x_{n})_{n\in\mathbb{N}}\) and \((d_{n})_{n\in\mathbb{N}}\) be the sequences generated by Algorithm 2.1 and assume that there exist \(c > 0\) and \(\gamma> 0\) such that \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \x_{n}  T(x_{n})\^{2}\) and \(\x_{n}  T(x_{n})\ \geq\gamma\) for all \(n\in\mathbb{N}\). Suppose also that Property (⋆) holds. Then there exists \(\lambda> 0\) such that, for all \(\Delta\in\mathbb {N} \backslash\{0\}\) and for any index \(k_{0}\), there is \(k \geq k_{0}\) such that \( \mathcal {K}_{k,\Delta}^{\lambda} > \Delta/2\), where \(\mathcal{K}_{k,\Delta}^{\lambda}:= \{ i\in\mathbb{N} \backslash\{ 0\} \colon k \leq i \leq k + \Delta1, \ x_{i}  x_{i1} \ > \lambda\}\) (\(k\in\mathbb{N}\), \(\Delta\in\mathbb{N} \backslash\{0\}\), \(\lambda> 0\)) and \(\mathcal{K}_{k,\Delta}^{\lambda}\) stands for the number of elements of \(\mathcal{K}_{k,\Delta}^{\lambda}\).
The following can be proven by referring to the proof of [31], Theorem 4.3.
Lemma 2.7
Let \((x_{n})_{n\in\mathbb{N}}\) be the sequence generated by Algorithm 2.1 with \(\beta_{n} \geq0\) (\(n\in\mathbb{N}\)) and assume that there exists \(c > 0\) such that \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \x_{n}  T(x_{n})\^{2}\) for all \(n\in\mathbb{N}\) and Property (⋆) holds. If \((x_{n})_{n\in\mathbb{N}}\) is bounded, \(\liminf_{n\to\infty} \x_{n}  T (x_{n} ) \ = 0\).
Proof
Assuming that \(\liminf_{n\to\infty} \x_{n}  T (x_{n} ) \ > 0\), there exists \(\gamma> 0\) such that \(\ x_{n}  T(x_{n}) \ \geq\gamma\) for all \(n\in\mathbb{N}\). Since \(c> 0\) exists such that \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \x_{n}  T(x_{n})\^{2}\) (\(n\in\mathbb{N}\)), \(\ d_{n} \ \neq0\) (\(n\in\mathbb{N}\)) holds. Moreover, the nonexpansivity of T ensures that, for all \(x\in\operatorname{Fix}(T)\), \(\ T (x_{n} )  x \ \leq\ x_{n} x \\), and this, together with the boundedness of \((x_{n})_{n\in\mathbb{N}}\), implies the boundedness of \((T(x_{n}))_{n\in\mathbb{N}}\). Accordingly, \(\bar{\gamma} > 0\) exists such that \(\ x_{n}  T(x_{n}) \ \leq\bar{\gamma}\) (\(n\in\mathbb {N}\)). The definition of \(x_{n}\) implies that, for all \(n\geq1\),
where \(u_{n} := d_{n}/\d_{n}\\) (\(n\in\mathbb{N}\)). Hence, for all \(l, k \in\mathbb{N}\) with \(l \geq k > 0\),
which implies that
From \(\ u_{n} \ = 1\) (\(n\in\mathbb{N}\)) and the triangle inequality, for all \(l, k \in\mathbb{N}\) with \(l \geq k > 0\), \(\sum_{i=k}^{l} \x_{i}  x_{i1} \ \leq\ x_{l}  x_{k1} \ + \sum_{i=k}^{l} \x_{i}  x_{i1} \ \ u_{i1}  u_{k1} \\). Since the boundedness of \((x_{n})_{n\in\mathbb{N}}\) means there is \(M > 0\) satisfying \(\ x_{n+1}  x_{n} \ \leq M\) (\(n\in\mathbb{N}\)), we find that, for all \(l, k \in\mathbb{N}\) with \(l \geq k > 0\),
Let \(\lambda> 0\) be as given by Lemma 2.6 and define \(\Delta := \lceil4M/\lambda\rceil\), where \(\lceil\cdot\rceil\) denotes the ceiling operator. From Lemma 2.5, an index \(k_{0}\) can be chosen such that \(\sum_{i=k_{0}}^{\infty}\ u_{i}  u_{i1} \^{2} \leq1/(4 \Delta)\). Accordingly, Lemma 2.6 guarantees the existence of \(k \geq k_{0}\) such that \( \mathcal{K}_{k,\Delta}^{\lambda} > \Delta/2\). Since the CauchySchwarz inequality implies that \((\sum_{i=1}^{m} a_{i})^{2} \leq m \sum_{i=1}^{m} a_{i}^{2}\) (\(m \geq1\), \(a_{i} \in\mathbb{R}\), \(i=1,2,\ldots,m\)), we have, for all \(i\in[k,k+\Delta1]\),
Putting \(l:= k+\Delta1\), (2.12) ensures that
which implies that \(\Delta< 4M/\lambda\). This contradicts \(\Delta:= \lceil4M/\lambda\rceil\). Therefore, \(\liminf_{n\to\infty} \x_{n}  T (x_{n} ) \ = 0\). □
Now we are in the position to prove Theorem 2.4.
Proof
The condition \(\beta_{n}^{\mathrm{PRP}+} \geq0\) holds for all \(n\in \mathbb{N}\). Suppose that positive constants γ and γ̄ exist such that \(\gamma\leq\x_{n}  T(x_{n})\ \leq\bar{\gamma}\) (\(n\in\mathbb{N}\)) and define \(b:= 2\bar{\gamma}^{2}/\gamma^{2}\) and \(\lambda:= \gamma^{2}/(4\bar{\gamma} b)\). The definition of \(\beta_{n}^{\mathrm{PRP}+}\) and the CauchySchwarz inequality mean that, for all \(n\in\mathbb{N}\),
where the third inequality comes from \(\y_{n}\ \leq\x_{n+1}  T(x_{n+1})\ + \ x_{n}  T(x_{n})\ \leq2 \bar {\gamma}\) and \(\gamma\leq\x_{n}  T(x_{n})\ \leq\bar{\gamma}\) (\(n\in\mathbb{N}\)). When \(\ x_{n+1}  x_{n} \ \leq\lambda\) (\(n\in\mathbb{N}\)), the triangle inequality and the nonexpansivity of T imply that \(\y_{n}\ \leq\x_{n+1}  x_{n}\ + \ T(x_{n})  T(x_{n+1})\ \leq2 \ x_{n+1}  x_{n} \ \leq2 \lambda\) (\(n\in\mathbb{N}\)). Therefore, for all \(n\in\mathbb{N}\),
which implies that Property (⋆) holds. Lemma 2.7 thus guarantees that \(\liminf_{n\to\infty} \ x_{n}  T(x_{n}) \ = 0\) holds. A discussion in the same manner as in the proof of Lemma 2.3(iii) leads to \(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \ = 0\). This completes the proof. □
2.5 Algorithm 2.1 with \(\beta_{n} = \beta _{n}^{\mathrm{HS}+}\)
The convergence properties of the nonlinear conjugate gradient method with \(\beta_{n}^{\mathrm{HS}}\) defined as in (1.19) are similar to those with \(\beta_{n}^{\mathrm{PRP}}\) defined as in (1.19) [23], Section 5. On the basis of this fact and the modification of \(\beta_{n}^{\mathrm {PRP}}\) in Section 2.4, this subsection considers Algorithm 2.1 with \(\beta _{n}^{\mathrm{HS}+}\) defined by (2.4).
Lemma 2.7 leads to the following.
Theorem 2.5
Suppose that \((x_{n})_{n\in\mathbb{N}}\) and \((d_{n})_{n\in\mathbb{N}}\) are the sequences generated by Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{HS}+}\) (\(n\in\mathbb{N}\)) and there exists \(c > 0\) such that \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leq c \ x_{n}  T(x_{n}) \^{2}\) for all \(n\in\mathbb{N}\). If \((x_{n})_{n\in\mathbb{N}}\) is bounded, then \((x_{n})_{n\in\mathbb{N}}\) either terminates at a fixed point of T or
Proof
When \(m\in\mathbb{N}\) exists such that \(\x_{m}  T(x_{m}) \ =0\), Theorem 2.5 holds. Let us consider the case where \(\ x_{n}  T(x_{n}) \ \neq0\) for all \(n\in\mathbb{N}\). Suppose that \(\gamma, \bar{\gamma} > 0\) exist such that \(\gamma\leq \ x_{n}  T(x_{n}) \ \leq\bar{\gamma}\) (\(n\in\mathbb{N}\)) and define \(b:= 2\bar{\gamma}^{2}/((1\sigma)c\gamma^{2})\) and \(\lambda := (1\sigma)c \gamma^{2}/(4\bar{\gamma}b)\). Then (2.2) implies that, for all \(n\in\mathbb{N}\),
which, together with the existence of \(c, \gamma> 0\) such that \(\langle x_{n}  T(x_{n}), d_{n} \rangle\leqc \ x_{n}  T(x_{n}) \^{2}\), and \(\gamma\leq\ x_{n}  T(x_{n}) \\) (\(n\in\mathbb{N}\)), implies that, for all \(n\in\mathbb{N}\),
This means Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm {HS}+}\) is well defined. From \(\x_{n}  T(x_{n})\ \leq\bar{\gamma}\) (\(n\in\mathbb{N}\)) and the definition of \(y_{n}\), we have, for all \(n\in\mathbb{N}\),
When \(\ x_{n+1}  x_{n} \ \leq\lambda\) (\(n\in\mathbb{N}\)), the triangle inequality and the nonexpansivity of T imply that \(\y_{n}\ \leq\x_{n+1}  x_{n}\ + \ T(x_{n})  T(x_{n+1})\ \leq2 \ x_{n+1}  x_{n} \ \leq2 \lambda\) (\(n\in\mathbb{N}\)). Therefore, from \(\ x_{n}  T(x_{n}) \ \leq\bar{\gamma}\) (\(n\in\mathbb {N}\)), for all \(n\in\mathbb{N}\),
which in turn implies that Property (⋆) holds. Lemma 2.7 thus ensures that \(\liminf_{n\to\infty} \ x_{n}  T(x_{n}) \ = 0\) holds. A discussion similar to the one in the proof of Lemma 2.3(iii) leads to \(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \ = 0\). This completes the proof. □
2.6 Convergence rate analyses of Algorithm 2.1
Sections 2.12.5 show that Algorithm 2.1 with equations (2.4) satisfies \(\lim_{n\to\infty} \ x_{n}  T(x_{n}) \ = 0\) under certain assumptions. The next theorem establishes rates of convergence for Algorithm 2.1 with equations (2.4).
Theorem 2.6

(i)
Under the Wolfetype conditions (2.1) and (2.2), Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{SD}}\) satisfies, for all \(n\in\mathbb{N}\),
$$ \bigl\Vert x_{n}  T (x_{n} ) \bigr\Vert \leq \frac{\Vert x_{0}  T (x_{0} ) \Vert }{\sqrt {\delta\sum_{k=0}^{n} \alpha_{k}}}. $$ 
(ii)
Under the strong Wolfetype conditions (2.1) and (2.9), Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{DY}}\) satisfies, for all \(n\in\mathbb{N}\),
$$ \bigl\Vert x_{n}  T (x_{n} ) \bigr\Vert \leq \frac{\Vert x_{0}  T (x_{0} ) \Vert }{\sqrt {\frac{1}{1+\sigma} \delta\sum_{k=0}^{n} \alpha_{k}}}. $$ 
(iii)
Under the strong Wolfetype conditions (2.1) and (2.9), Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{FR}}\) satisfies, for all \(n\in\mathbb{N}\),
$$ \bigl\Vert x_{n}  T (x_{n} ) \bigr\Vert \leq \frac{\Vert x_{0}  T (x_{0} ) \Vert }{\sqrt {\frac{1}{1\sigma} \delta\sum_{k=0}^{n} ( 12\sigma+ \sigma ^{k} ) \alpha_{k}}}. $$ 
(iv)
Under the assumptions in Theorem 2.4, Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{PRP}+}\) satisfies, for all \(n\in\mathbb{N}\),
$$ \bigl\Vert x_{n}  T (x_{n} ) \bigr\Vert \leq \frac{\Vert x_{0}  T (x_{0} ) \Vert }{\sqrt{c \delta\sum_{k=0}^{n} \alpha_{k}}}. $$ 
(v)
Under the assumptions in Theorem 2.5, Algorithm 2.1 with \(\beta_{n} = \beta_{n}^{\mathrm{HS}+}\) satisfies, for all \(n\in\mathbb{N}\),
$$ \bigl\Vert x_{n}  T (x_{n} ) \bigr\Vert \leq \frac{\Vert x_{0}  T (x_{0} ) \Vert }{\sqrt{c \delta\sum_{k=0}^{n} \alpha_{k}}}. $$
Proof
(i) From \(d_{k} =  (x_{k}  T(x_{k}))\) (\(k\in\mathbb{N}\)) and (2.1), we have \(0 \leq\delta\alpha_{k} \x_{k}  T(x_{k})\^{2} \leq\ x_{k}  T(x_{k}) \ ^{2}  \x_{k+1}  T(x_{k+1})\^{2}\) (\(k\in\mathbb{N}\)). Summing up this inequality from \(k=0\) to \(k=n\) guarantees that, for all \(n\in\mathbb{N}\),
which, together with the monotone decreasing property of \((\ x_{n}  T(x_{n}) \^{2})_{n\in\mathbb{N}}\), implies that, for all \(n\in\mathbb{N}\),
This completes the proof.
(ii) Condition (2.9) and Lemma 2.3(i) ensure that \( \sigma\leq\langle x_{k+1}  T(x_{k+1}), d_{k} \rangle/\langle x_{k}  T(x_{k}), d_{k} \rangle\leq\sigma\) (\(k\in\mathbb{N}\)). Accordingly, (2.8) means that, for all \(k\in\mathbb{N}\),
Hence, (2.1) implies that, for all \(k\in\mathbb{N}\),
Summing up this inequality from \(k=0\) to \(k=n\) and the monotone decreasing property of \((\ x_{n}  T(x_{n}) \^{2})_{n\in\mathbb{N}}\) ensure that, for all \(n\in\mathbb{N}\),
which completes the proof.
(iii) Inequality (2.10) guarantees that, for all \(k\in\mathbb{N}\),
which, together with (2.1), implies that, for all \(k\in \mathbb{N}\),
Summing up this inequality from \(k=0\) to \(k=n\) and the monotone decreasing property of \((\ x_{n}  T(x_{n}) \^{2})_{n\in\mathbb{N}}\) ensure that, for all \(n\in\mathbb{N}\),
which completes the proof.
(iv), (v) Since there exists \(c > 0\) such that \(\langle x_{k}  T(x_{k}), d_{k} \rangle \leqc \ x_{k}  T(x_{k})\^{2}\) for all \(k\in\mathbb{N}\), we have from (2.1) and the monotone decreasing property of \((\ x_{n}  T(x_{n}) \^{2})_{n\in\mathbb{N}}\), for all \(n\in\mathbb{N}\),
This concludes the proof. □
The conventional Krasnosel’skiĭMann algorithm (1.2) with a step size sequence \((\alpha_{n})_{n\in\mathbb{N}}\) obeying (1.3) satisfies the following inequality [8], Propositions 10 and 11:
where \(\mathrm{d}(x_{0}, \operatorname{Fix} (T)) := \min_{x\in\operatorname{Fix}(T)} \ x_{0}  x \\). When \(\alpha_{n}\) (\(n\in\mathbb{N}\)) is a constant in the range of \((0,1)\), which is the most tractable choice of step size satisfying (1.3), the Krasnosel’skiĭMann algorithm (1.2) has the rate of convergence,
Meanwhile, according to Theorem 5 in [17], Algorithm (1.2) with \((\alpha_{n})_{n\in\mathbb{N}}\) satisfying the Armijotype condition (1.5) satisfies, for all \(n\in\mathbb{N}\),
In general, the step sizes satisfying (1.3) do not coincide with those satisfying the Armijotype condition (1.5) or the Wolfetype conditions (2.1) and (2.2). This is because the line search methods based on the Armijotype conditions (1.5) and (2.1) determine step sizes at each iteration n so as to satisfy \(\ x_{n+1}  T(x_{n+1}) \ < \x_{n}  T(x_{n})\\), while the constant step sizes satisfying (1.3) do not change at each iteration. Accordingly, it would be difficult to evaluate the efficiency of these algorithms by using only the theoretical convergence rates in (2.13), (2.14), and Theorem 2.6. To verify whether Algorithm 2.1 with the convergence rates in Theorem 2.6 converges faster than the previous algorithms [8], Propositions 10 and 11, [17], Theorem 5, with convergence rates (2.13) and (2.14), the next section numerically compares their abilities to solve concrete constrained smooth convex optimization problems.
3 Application of Algorithm 2.1 to constrained smooth convex optimization
This section considers the following problem:
where \(f \colon\mathbb{R}^{d} \to\mathbb{R}\) is convex, \(\nabla f \colon\mathbb{R}^{d} \to\mathbb{R}^{d}\) is Lipschitz continuous with a constant L, and \(C \subset\mathbb{R}^{d}\) is a nonempty, closed, and convex set onto which the metric projection \(P_{C}\) can be efficiently computed.
3.1 Experimental conditions and fixed point and line search algorithms used in the experiment
Problem (3.1) can be solved by using the conventional Krasnosel’skiĭMann algorithm (1.2) with a nonexpansive mapping \(T := P_{C} (\mathrm{Id}  \lambda\nabla f)\) satisfying \(\operatorname{Fix}(T) = \operatorname {argmin}_{x\in C} f(x)\), where \(\lambda\in(0,2/L]\) [22], Proposition 2.3. It is represented as follows:
where \(x_{0} \in\mathbb{R}^{d}\) and \((\alpha_{n})_{n\in\mathbb{N}}\) is a sequence satisfying (1.3) or the Armijotype condition (1.5). Algorithm 2.1 with \(T := P_{C} (\mathrm{Id}  \lambda\nabla f)\) is as follows:
where \(x_{0}, d_{0} := (x_{0} P_{C}(x_{0}  \lambda\nabla f (x_{0}))) \in \mathbb{R}^{d}\), \((\alpha_{n})_{n\in\mathbb{N}}\) is a sequence satisfying the Wolfetype conditions (2.1) and (2.2), and \((\beta_{n})_{n\in\mathbb{N}}\) is defined by each of equations (2.4) with \(T := P_{C} (\mathrm{Id}  \lambda\nabla f)\) (see also (1.21)).
The best conventional nonlinear conjugate gradient method for unconstrained smooth nonconvex optimization was proposed by Hager and Zhang [29, 36], and it uses the HS formula defined as in (1.19):
Replacing ∇f in the above formula with \(\mathrm{Id}  P_{C} (\mathrm{Id} \lambda\nabla f)\) leads to the HZtype formula for Problem (3.1):
where \(y_{n} := (x_{n+1}  P_{C}(x_{n+1} \lambda\nabla f(x_{n+1}) ) )  (x_{n}  P_{C}(x_{n} \lambda\nabla f(x_{n})) )\) and \(\beta_{n}^{\mathrm{HS}}\) is defined by \(\beta_{n}^{\mathrm{HS}} := \langle x_{n+1}  P_{C}(x_{n+1}  \lambda \nabla f (x_{n+1})), y_{n} \rangle/\langle d_{n}, y_{n} \rangle\). We tested Algorithm (3.3) with \(\beta_{n} := \beta _{n}^{\mathrm{HZ}}\) defined by (3.4) in order to see how it works on Problem (3.1).
We used the Virtual Desktop PC at the Ikuta campus of Meiji University. The PC has 8 GB of RAM memory, 1 core Intel Xeon 2.6 GHz CPU, and a Windows 8.1 operating system. The algorithms used in the experiment were written in MATLAB (R2013b), and they are summarized as follows.
 SD1::

Algorithm (3.2) with constant step sizes \(\alpha_{n} := 0.5\) (\(n\in\mathbb{N}\)) [1], Theorem 5.14.
 SD2::

Algorithm (3.2) with \(\alpha_{n}\) satisfying the Armijotype condition (1.5) when \(\beta= 0.5\) [17], Theorems 4 and 8.
 SD3::

Algorithm (3.3) with \(\alpha_{n}\) satisfying the Wolfetype conditions (2.1) and (2.2) and \(\beta_{n} := \beta_{n}^{\mathrm{SD}}\) (Theorem 2.1).
 FR::

Algorithm (3.3) with \(\alpha_{n}\) satisfying the Wolfetype conditions (2.1) and (2.2) and \(\beta_{n} := \beta_{n}^{\mathrm{FR}}\) (Theorem 2.3).
 PRP+::

Algorithm (3.3) with \(\alpha_{n}\) satisfying the Wolfetype conditions (2.1) and (2.2) and \(\beta_{n} := \beta_{n}^{\mathrm{PRP}+}\) (Theorem 2.4).
 HS+::

Algorithm (3.3) with \(\alpha_{n}\) satisfying the Wolfetype conditions (2.1) and (2.2) and \(\beta_{n} := \beta_{n}^{\mathrm{HS}+}\) (Theorem 2.5).
 DY::

Algorithm (3.3) with \(\alpha_{n}\) satisfying the Wolfetype conditions (2.1) and (2.2) and \(\beta_{n} := \beta_{n}^{\mathrm{DY}}\) (Theorem 2.2).
 HZ::

Algorithm (3.3) with \(\alpha_{n}\) satisfying the Wolfetype conditions (2.1) and (2.2) and \(\beta_{n} := \beta_{n}^{\mathrm{HZ}}\) defined by (3.4) [29, 36].
The experiment used the following line search algorithm [21], Algorithm 4.6, to find step sizes satisfying the Wolfetype conditions (2.1) and (2.2) with \(\delta:= 0.3\) and \(\sigma:= 0.5\) that were chosen by referring to [21], Section 6.1, where, for each n, \(A_{n}(\cdot)\) and \(W_{n}(\cdot)\) are
Algorithm 3.1
([21], Algorithm 4.6)
For Algorithm SD2, we replaced \(A_{n}(\cdot)\) above by
where \(D := \delta= 0.3\) and \(g_{n}\) is defined as in (1.6), and deleted \(W_{n}(\cdot)\) from the line search algorithm. For Algorithms FR, PRP+, HS+, DY, and HZ, if the step sizes satisfying the Wolfetype conditions (2.1) and (2.2) were not computed by using Algorithm 3.1, the step sizes were computed by using Algorithm 3.1 when \(d_{n} := (x_{n}  T(x_{n}))\). This is because Algorithm 3.1 for Algorithm SD3, which uses \(d_{n} := (x_{n}  T(x_{n}))\) (\(n\in\mathbb{N}\)), had a 100% success rate in computing the step sizes satisfying (2.1) and (2.2). Tables 1, 2, 3, and 4 indicate the satisfiability rates (defined below) of computing the step sizes for the algorithms in the experiment.
The stopping condition was
Before describing the results, let us describe the notation used to verify the numerical performance of the algorithms.

I: the number of initial points;

\(x_{0}^{(i)}\): the initial point chosen randomly (\(i=1,2,\ldots, I\));

ALGO: each of Algorithms SD1, SD2, SD3, FR, PRP+, HS+, DY, and HZ (\(\mathrm{ALGO} \in\{\mathrm{SD}\mbox{}1, \mathrm{SD}\mbox{}2, \mathrm{SD}\mbox{}3, \mathrm{FR}, \mathrm{PRP}{+}, \mathrm{HS}{+}, \mathrm{DY}, \mathrm{HZ}\}\));

\(N_{1} (x_{0}^{(i)}, \mathrm{ALGO})\): the number of step sizes computed by Algorithm 3.1 for ALGO with \(x_{0}^{(i)}\) before ALGO satisfies the stopping condition (3.5);

\(N_{2} (x_{0}^{(i)}, \mathrm{ALGO})\): the number of iterations needed to satisfy the stopping condition (3.5) for ALGO with \(x_{0}^{(i)}\).
Note that \(N_{1} (x_{0}^{(i)}, \mathrm{SD}\mbox{}1)\) stands for the number of iterations n satisfying \(A_{n}(0.5)\) and \(W_{n}(0.5)\) before Algorithm SD1 with \(x_{0}^{(i)}\) satisfies the stopping condition (3.5). The satisfiability rate (SR) of Algorithm 3.1 to compute the step sizes for each of the algorithms is defined by
We performed 100 samplings, each starting from different random initial points (i.e., \(I := 100\)) and averaged their results.
3.2 Constrained quadratic programming problem
In this subsection, let us consider the following constrained quadratic programming problem:
Problem 3.1
Suppose that C is a nonempty, closed convex subset of \(\mathbb{R}^{d}\) onto which \(P_{C}\) can be efficiently computed, \(Q \in\mathbb{R}^{d \times d}\) is positive semidefinite with the eigenvalues \(\lambda_{\mathrm{min}} := \lambda_{1}, \lambda_{2}, \ldots, \lambda_{d} =: \lambda_{\mathrm{max}}\) satisfying \(\lambda_{i} \leq\lambda_{j}\) (\(i \leq j\)), and \(b\in\mathbb{R}^{d}\). Our objective is to
Since f above is convex and \(\nabla f(x) = Qx +b\) (\(x\in\mathbb {R}^{d}\)) is Lipschitz continuous such that the Lipschitz constant of ∇f is the maximum eigenvalue \(\lambda _{\mathrm{max}}\) of Q, Problem 3.1 is an example of Problem (3.1).
We compared the proposed algorithms SD3, FR, PRP+, HS+, DY, and HZ with the previous algorithms SD1 and SD2 by applying them to Problem 3.1 (i.e., the fixed point problem for \(T(x) := P_{C} (x  (2/\lambda_{\mathrm{max}}) (Qx + b))\) (\(x\in \mathbb{R}^{d}\))) in the following cases:
We randomly chose \(\lambda_{i} \in[0,d]\) (\(i=2,3,\ldots,d1\)) and set Q as a diagonal matrix with eigenvalues \(\lambda_{1}, \lambda_{2}, \ldots, \lambda _{\mathrm{max}}\). The experiment used two random numbers in the range of \((32,32)^{d}\) for b and c to satisfy \(C \cap\{ x\in\mathbb{R}^{d} \colon\nabla f(x) = 0 \} = \emptyset\). Since C is a closed ball with center c and radius 1, \(P_{C}\) can be computed within a finite number of arithmetic operations. More precisely, \(P_{C} (x) := c + (x c)/\ x c \\) if \(\ x  c \ > 1\), or \(P_{C} (x) := x\) if \(\ x  c \ \leq1\).
Table 1 shows the satisfiability rates as defined by (3.6) for Algorithms SD1, SD2, and SD3 that are applied to Problem 3.1. It can be seen that the step sizes for SD1 (constant step sizes \(\alpha_{n} := 0.5\)) do not always satisfy the Wolfetype conditions (2.1) and (2.2), whereas the step sizes computed by Algorithm 3.1 and SD2 (resp. Algorithm SD3) definitely satisfy the Armijotype condition (1.5) (resp. the Wolfetype conditions (2.1) and (2.2)).
Table 2 showing the satisfiability rates for Algorithms FR, PRP+, HS+, DY, and HZ indicates that Algorithm 3.1 for PRP+ and HS+ has high success rates at computing the step sizes satisfying (2.1) and (2.2), while the SRs of Algorithm 3.1 for other algorithms are low. It can be seen from Tables 1 and 2 that SD3, PRP+, and HS+ are robust in the sense that Algorithm 3.1 can compute the step sizes satisfying the Wolfetype conditions (2.1) and (2.2).
Figure 1 indicates the behaviors of SD1, SD2, and SD3 when \(d:= 10^{3}\). The yaxes in Figures 1(a) and 1(b) represent the value of \(\x_{n}  T(x_{n})\\). The xaxis in Figure 1(a) represents the number of iterations, and the xaxis in Figure 1(b) represents the elapsed time. If the \((\ x_{n}  T(x_{n}) \)_{n\in\mathbb{N}}\) generated by the algorithms converges to 0, they also converge to a fixed point of T. Figure 1(a) shows that SD2 and SD3 terminate at fixed points of T within a finite number of iterations. It can be seen from Figure 1(a) and Figure 1(b) that SD3 reduces the iterations and running time needed to find a fixed point compared with SD2. These figures also show that \((\x_{n}  T(x_{n})\)_{n\in\mathbb {N}}\) generated by SD1 converges slowest and that SD1 cannot find a fixed point of T before the tenth iteration. We can thus see that the use of the step sizes satisfying the Wolfetype conditions is a good way to solve fixed point problems by using the Krasnosel’skiĭMann algorithm. Figure 2 indicates the behaviors of SD1, SD2, and SD3 when \(d:= 10^{4}\). Similarly to what is shown in Figure 1, SD3 finds a fixed point of T faster than SD1 and SD2 can.
Figure 3 is the evaluation of \((\ x_{n}  T(x_{n}) \)_{n\in \mathbb{N}}\) in terms of the number of iterations and elapsed time for Algorithms FR, PRP+, HS+, DY, and HZ when \(d:= 10^{3}\). Figure 3(a) shows that they can find fixed points of T within a finite number of iterations. Figure 3(b) indicates that PRP+ and HS+ find the fixed points of T faster than FR, DY, and HZ. This is because Algorithm 3.1 for each of PRP+ and HS+ has a 100% success rate at computing the step sizes satisfying (2.1) and (2.2), while the SRs of Algorithm 3.1 for FR, DY, and HZ are low (see Table 2); i.e., FR, DY, and HZ require much more time to compute the step sizes than PRP+ and HS+. In fact, we checked that the times to compute the step sizes for FR, DY, and HZ account for 92.672202%, 87.156303%, and 83.700936% of all the computational times, while the times to compute the step sizes for PRP+ and HS+ account for 60.725204% and 60.889635% of all the computational times. Figure 4 indicate the behaviors of FR, PRP+, HS+, DY, and HZ when \(d:= 10^{4}\) and PRP+ and HS+ perform better than FR, DY, and HZ, as seen in Figure 3. Such a trend can also be verified from Table 2 showing that the SRs of Algorithm 3.1 for PRP+ and HS+ are about 100%.
3.3 Generalized convex feasibility problem
This subsection considers the following generalized convex feasibility problem [33], Section I, Framework 2, [37], Section 2.2, [6], Definition 4.1:
Problem 3.2
Suppose that \(C_{i}\) (\(i=0,1,\ldots, m\)) is a nonempty, closed convex subset of \(\mathbb{R}^{d}\) onto which \(P_{C_{i}}\) can be efficiently computed and define the weighted mean square value of the distances from \(x\in \mathbb{R}^{d}\) to \(C_{i}\) (\(i=1,2,\ldots,m\)) as \(f(x)\) below; i.e., for \(w_{i}\in(0,1)\) (\(i=1,2,\ldots,m\)) satisfying \(\sum_{i=1}^{m} w_{i} = 1\),
Our objective is to find a point in the generalized convex feasible set defined by
\(C_{f}\) is a subset of \(C_{0}\) having the elements closest to \(C_{i}\) (\(i=1,2,\ldots,m\)) in terms of the weighted mean square norm. Even if \(\bigcap_{i=0}^{m} C_{i} = \emptyset\), \(C_{f}\) is well defined because \(C_{f}\) is the set of all minimizers of f over \(C_{0}\). The condition \(C_{f} \neq\emptyset\) holds when \(C_{0}\) is bounded [6], Remark 4.3(a). Moreover, \(C_{f} = \bigcap_{i=0}^{m} C_{i}\) holds when \(\bigcap_{i=0}^{m} C_{i} \neq\emptyset\). Accordingly, Problem 3.2 is a generalization of the convex feasibility problem [5] of finding a point in \(\bigcap_{i=0}^{m} C_{i} \neq\emptyset\).
The convex function f in Problem 3.2 satisfies \(\nabla f = \mathrm{Id}  \sum_{i=1}^{m} w_{i} P_{C_{i}}\). Hence, ∇f is Lipschitz continuous when its Lipschitz constant is two. This means Problem 3.2 is an example of Problem (3.1). Since Problem 3.2 can be expressed as the problem of finding a fixed point of \(T = P_{C_{0}} (\mathrm{Id}  \lambda\nabla f) = P_{C_{0}} (\mathrm{Id}  \lambda(\mathrm{Id}  \sum_{i=1}^{m} w_{i} P_{C_{i}}) )\) for \(\lambda\in(0,1]\), we used T with \(\lambda=1\); i.e., \(T := P_{C_{0}} (\sum_{i=1}^{m} w_{i} P_{C_{i}})\).
We applied SD1, SD2, SD3, FR, PRP+, HS+, DY, and HZ to Problem 3.2 in the following cases:
The experiment used one hundred random numbers in the range of \((32,32)^{d}\) for \(c_{i}\), which means \(\bigcap_{i=0}^{m} C_{i} = \emptyset \). Since \(C_{i}\) (\(i=0,1,\ldots,m\)) is a closed ball with center \(c_{i}\) and radius 1, \(P_{i}\) can be computed within a finite number of arithmetic operations.
Table 3 shows the satisfiability rates as defined by (3.6) for Algorithms SD1, SD2, and SD3 applied to Problem 3.2. It can be seen that the step sizes for SD1 do not always satisfy the Wolfetype conditions (2.1) and (2.2), whereas the step sizes computed by Algorithm 3.1 and SD2 (resp. Algorithm SD3) definitely satisfy the Armijotype condition (1.5) (resp. the Wolfetype conditions (2.1) and (2.2)). Such a trend also existed when SD1, SD2, and SD3 were applied to Problem 3.1 (see Table 1).
Table 4 shows the satisfiability rates for Algorithms FR, PRP+, HS+, DY, and HZ. The table indicates that Algorithm 3.1 for PRP+ has a 100% success rate at computing the step sizes satisfying (2.1) and (2.2), while the SRs of Algorithm 3.1 for the other algorithms lie between 50% and about 60%. From Tables 3 and 4, we can see that SD3 and PRP+ are robust in the sense that Algorithm 3.1 can compute the step sizes satisfying the Wolfetype conditions (2.1) and (2.2).
Figure 5 indicates the behaviors of SD1, SD2, and SD3 when \(d:= 10^{3}\). The yaxes represent the value of \(\ x_{n}  T(x_{n}) \ \). The xaxis in Figure 5(a) represents the number of iterations, and the xaxis in Figure 5(b) represents the elapsed time. From Figure 5(a), the iterations needed to satisfy \(\x_{n}  T(x_{n}) \ = 0\) for SD2 and SD3 are, respectively, 3 and 2. It can be seen that SD3 reduces the running time and iterations needed to find a fixed point compared with SD2. These figures also show that the \((\x_{n}  T(x_{n})\)_{n\in\mathbb{N}}\) generated by SD1 converges slowest. Therefore, we can see that the use of the step sizes satisfying the Wolfetype conditions is a good way to solve fixed point problems by using the Krasnosel’skiĭMann algorithm, as seen in Figures 1 and 2 illustrating the behaviors of SD1, SD2, and SD3 on Problem 3.1 when \(d := 10^{3}, 10^{4}\). Figure 6 indicates the behaviors of SD1, SD2, and SD3 when \(d:= 10^{4}\). Similarly to what is shown in Figure 5, SD3 finds a fixed point of T faster than SD1 and SD2 can.
Figure 7(a) is the evaluation of \((\ x_{n}  T(x_{n}) \)_{n\in \mathbb{N}}\) in terms of the number of iterations for Algorithms FR, PRP+, HS+, DY, and HZ when \(d:= 10^{3}\). Except for HS+, the algorithms approximate the fixed points of T very rapidly. It can also be seen that the algorithms other than HS+ satisfy \(\ x_{2}  T(x_{2}) \ = 0\). Figure 7(b) is the evaluation of \((\ x_{n}  T(x_{n}) \)_{n\in \mathbb{N}}\) in terms of the elapsed time. Here, we can see that FR, PRP+, and DY can find fixed points of T faster than SD1 and SD2 (Figure 5). Figure 8 indicates the behaviors of FR, PRP+, HS+, DY, and HZ when \(d:= 10^{4}\). The results in these figures are almost the same as the ones in Figure 7.
From the above numerical results, we can conclude that the proposed algorithms can find optimal solutions to Problems 3.1 and 3.2 faster than the previous fixed point algorithms can. In particular, it can be seen that the algorithms for which the SRs of Algorithm 3.1 are high converge quickly to solutions of Problems 3.1 and 3.2.
4 Conclusion and future work
This paper discussed the fixed point problem for a nonexpansive mapping on a real Hilbert space and presented line search fixed point algorithms for solving it on the basis of nonlinear conjugate gradient methods for unconstrained optimization and their convergence analyses and convergence rate analyses. Moreover, we used these algorithms to solve concrete constrained quadratic programming problems and generalized convex feasibility problems and numerically compared them with the previous fixed point algorithms based on the Krasnosel’skiĭMann fixed point algorithm. The numerical results showed that the proposed algorithms can find optimal solutions to these problems faster than the previous algorithms.
In the experiment, the line search algorithm (Algorithm 3.1) could not compute appropriate step sizes for fixed point algorithms other than Algorithms SD2, SD3, and PRP+. In the future, we should consider modifying the algorithms to enable the line search to compute appropriate step sizes. Or we may need to develop new line searches that can be applied to all of the fixed point algorithms considered in this paper.
The main objective of this paper was to devise linesearch fixedpoint algorithms to accelerate the previous Krasnosel’skiĭMann fixed point algorithm defined by (1.2), i.e., \(x_{n+1} := \lambda_{n} x_{n} + (1\lambda_{n}) T(x_{n})\) (\(n\in\mathbb{N}\)), where \((\lambda_{n})_{n\in\mathbb{N}} \subset[0,1]\) with \(\sum_{n=0}^{\infty}\lambda_{n} (1\lambda_{n}) = \infty\) and \(x_{0} \in H\) is an initial point. Another particularly interesting problem is determining whether or not there are line search fixed point algorithms to accelerate the following Halpern fixed point algorithm [11, 12]: for all \(n\in\mathbb{N}\),
where \((\alpha_{n})_{n\in\mathbb{N}} \subset(0,1)\) satisfies \(\lim_{n\to\infty} \alpha_{n} = 0\) and \(\sum_{n=0}^{\infty}\alpha_{n} = \infty\). The Halpern algorithm can minimize the convex function \(\ \cdot x_{0} \^{2}\) over \(\operatorname{Fix}(T)\) (see, e.g., [7], Theorem 6.17). A previously reported result [38], Theorem 3.1, Proposition 3.2, showed that there is an inconvenient possibility that the Halperntype algorithm with a diminishing step size sequence (e.g., \(\alpha_{n} := 1/(n+1)^{a}\), where \(a \in(0,1]\)) and any of the FR, PRP, HS, and DY formulas used in the conventional conjugate gradient methods may not converge to the minimizer of \(\ \cdot x_{0} \^{2}\) over \(\operatorname{Fix}(T)\). However, there is room for further research into devising line search fixed point algorithms to accelerate the Halpern algorithm with a diminishing step size sequence.
Notes
See Theorem 2.6(i) for the details of the convergence rate of the proposed algorithm when \(d_{n} :=  (x_{n}  T(x_{n}))\) (\(n\in\mathbb{N}\)).
To guarantee the convergence of the PRP and HS methods for unconstrained optimization, the formulas \(\beta_{n}^{\mathrm{PRP}+} := \max\{\beta_{n}^{\mathrm{PRP}}, 0\}\) and \(\beta_{n}^{\mathrm{HS}+} := \max\{\beta_{n}^{\mathrm{HS}}, 0\}\) were presented in [35]. We use the modifications to perform the convergence analyses on the proposed line search fixed point algorithms.
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Acknowledgements
I am sincerely grateful to the editor, Juan Jose Nieto, the anonymous associate editor, and the anonymous reviewers for helping me improve the original manuscript. The author thanks Mr. Kazuhiro Hishinuma for his discussion of the numerical experiments. This work was supported by the Japan Society for the Promotion of Science through a GrantinAid for Scientific Research (C) (15K04763).
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Iiduka, H. Line search fixed point algorithms based on nonlinear conjugate gradient directions: application to constrained smooth convex optimization. Fixed Point Theory Appl 2016, 77 (2016). https://doi.org/10.1186/s1366301605677
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DOI: https://doi.org/10.1186/s1366301605677
MSC
 47H10
 65K05
 90C25
Keywords
 constrained smooth convex optimization
 fixed point problem
 generalized convex feasibility problem
 Krasnosel’skiĭMann fixed point algorithm
 line search method
 nonexpansive mapping
 nonlinear conjugate gradient methods