Next Article in Journal
MP-CE Method for Space-Filling Design in Constrained Space with Multiple Types of Factors
Next Article in Special Issue
On Mann-Type Subgradient-like Extragradient Method with Linear-Search Process for Hierarchical Variational Inequalities for Asymptotically Nonexpansive Mappings
Previous Article in Journal
Jensen-Type Inequalities for (h, g; m)-Convex Functions
Previous Article in Special Issue
Existence and Generic Stability of Strong Noncooperative Equilibria of Vector-Valued Games
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Improved Alternating CQ Algorithm for Solving Split Equality Problems

1
School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China
2
The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province, North Minzu University, Yinchuan 750021, China
3
Health Big Date Research Institute, North Minzu University, Yinchuan 750021, China
*
Author to whom correspondence should be addressed.
Mathematics 2021, 9(24), 3313; https://doi.org/10.3390/math9243313
Submission received: 30 November 2021 / Revised: 16 December 2021 / Accepted: 18 December 2021 / Published: 19 December 2021
(This article belongs to the Special Issue Fixed Point, Optimization, and Applications II)

Abstract

:
The CQ algorithm is widely used in the scientific field and has a significant impact on phase retrieval, medical image reconstruction, signal processing, etc. Moudafi proposed an alternating CQ algorithm to solve the split equality problem, but he only obtained the result of weak convergence. The work of this paper is to improve his algorithm so that the generated iterative sequence can converge strongly.

1. Introduction

Let C H 1 , Q H 2 be two nonempty closed convex subsets, H 1 and H 2 are real Hilbert spaces. For all b H 1 , d H 2 , the equality A b , d = b , A * d is true, A * is called the adjoint operator of A. The split feasibility problem (SFP) can be described as finding b C such that
A b Q ,
where A : H 1 H 2 is a linear bounded operator.
The SFP was first proposed by Censor and Elfving [1]. It is used to model the inverse problems of phase retrieval and medical image reconstruction in finite-dimensional Hilbert spaces. It has a significant impact on signal processing, image reconstruction and radiation therapy, see [2,3,4]. The following CQ algorithm proposed by Byrne [4] is an important method to solve the SFP
u n + 1 = P C ( u n + ρ A * ( P Q I ) A u n ) , n 0
where ρ ( 0 , 2 λ ) , λ represents the largest eigenvalue of the operator A * A . Recently, many other algorithms have appeared to solve problem (1), for example, [5,6,7,8].
Let { C i } i p H 1 and { Q j } j = 1 r H 2 be nonempty closed convex subsets, H 1 and H 2 are real Hilbert spaces, p 1 and r 1 are two non-negative integers. H 3 is also a real Hilbert space. The multiple-sets split equality problem (MSSEP) can be described as finding b C : = i = 1 p C i , d Q : = j = 1 r Q j such that
A b = B d
where B : H 2 H 3 and A : H 1 H 3 are two linear bounded operators.
Remark 1. 
When B = I , the MSSEP is reduced to an MSSFP. The MSSFP is widely used in intensity-modulated radiation therapy (IMRT) [9,10,11,12,13], image reconstruction [14,15,16], signal processing [17,18,19,20,21]. Recently, many other algorithms have appeared to solve the MSSFP, see [22,23,24].
Remark 2. 
When p = r = 1 , the MSSEP is reduced to a split equality problem (SEP).
The SEP can be described as finding b C , d Q such that
A b = B d
The SEP is applied to optimal control and approximation theory [25], in intensity-modulated radiation therapy (IMRT) [26] and game theory [27]. Byrne [28] proposed the following Landweber projection algorithm to study the SEP:
( P L A ) u n + 1 = P C ( u n ρ n A * ( A u n B v n ) ) v n + 1 = P Q ( v n + ρ n B * ( A u n B v n ) ) .
Different from Byrne’s algorithm, Moudafi [29] proposed the following alternating CQ algorithm
( A C Q A ) u n + 1 = P C ( u n ρ n A * ( A u n B v n ) ) v n + 1 = P Q ( v n + ρ n B * ( A u n + 1 B v n ) ) .
However, Moudafi only obtained the result of weak convergence. Inspired by this work, we propose an improved alternating CQ algorithm to solve the SEP. This improved method changes the iterative sequence from weak to strong convergence.
The structure of this article is as follows. In Section 2, we review some of the definitions, properties, and lemmas used to prove the convergence of the method. In Section 3, we propose a new algorithm and prove its strong convergence. In Section 4, at the end of the article, we reach a conclusion.

2. Preliminaries

We define the strong convergence of sequence { u n } n N as u n b and weak convergence as u n b , b H . Let C H be a nonempty closed convex subset, H is a real Hilbert space, b H , the orthogonal projection from H to C is defined by
P C ( b ) = arg min z C b z .
Definition 1 
([30]). Let C H be a nonempty closed convex subset, H is a real Hilbert space, for all b , d H and z C , we have
1. 
b P C b , z P C b 0 ;
2. 
P C b P C d 2 P C b P C d , b d ;
3. 
P C b z 2 b z 2 P C b b 2 .
4. 
P C b P C d 2 b d 2 ( I P C ) ( b ) ( I P C ) ( d ) 2 .
Lemma 1 
([31]). For all b ˜ , d H , H is a real Hilbert space, we have
b ˜ + d 2 = b ˜ 2 + d 2 + 2 b ˜ , d
b ˜ d 2 = b ˜ 2 + d 2 2 b ˜ , d
b ˜ + d 2 b ˜ 2 + 2 d , b ˜ + d
b ˜ d b ˜ + d b ˜ + d .
Lemma 2 
([32]). For all n 0 , assume that the three sequences { α n } , { ρ n } , { δ n } satisfy the following conditions:
1. 
α n 0 ;
2. 
{ δ n } [ 0 , 1 ] and n = 0 δ n = ;
3. 
lim   sup n ρ n 0 ;
4. 
α n + 1 ( 1 δ n ) α n + δ n ρ n .
Then, the following conclusion holds:
l i m n α n = 0 .

3. Main Results

Let the solution set of problem (4) given by Ω = { b C , d Q ; A b = B d } . We propose the following new alternating CQ algorithm to solve problem (4):
u n + 1 = P C ( ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) ) v n + 1 = P Q ( ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) )
Assume that a 0 and b 0 are two given points, the sequence { α n } satisfies { α n } n 0 ( 0 , 1 ) , n = 0 ( 1 α n ) = and lim n α n = 1 . Below, we prove the strong convergence of the sequence generated by Equation (7).
Theorem 1. 
The sequence { ( u n , v n ) } is generated by Equation (7), the sequence { ρ n } is positive and non-increasing, for a sufficiently small ε > 0 , ρ n ( ε , m i n ( 1 ρ ( A * A ) , 1 ρ ( B * B ) ) ε ) . ρ ( A * A ) , ρ ( B * B ) are the spectral radius of A * A and B * B , respectively. Then, the sequence { ( u n , v n ) } strongly converges to a solution ( b , d ) of Equation (4).
Proof. 
Let ( b , d ) Ω , which is, b C , d Q , A b = B d . According to (4) of Definition 1 and Lemma 1, on the one hand, we have
u n + 1 b 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) b 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2 = ( 1 α n ) ( a 0 b ) + α n ( u n ρ n A * ( A u n B v n ) b ) 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2 ( 1 α n ) ( a 0 b ) 2 + α n u n ρ n A * ( A u n B v n ) ) b 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2
It follows that
u n + 1 b 2 ( 1 α n ) ( a 0 b ) 2 + α n u n b 2 + α n ρ n 2 A * ( A u n B v n ) 2 2 α n ρ n A * ( A u n B v n ) , u n b ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2
We consider first
α n ρ n 2 A * ( A u n B v n ) 2 = α n ρ n 2 A u n B v n , A A * ( A u n B v n ) α n ρ ( A * A ) ρ n 2 A u n B v n , A u n B v n = α n ρ ( A * A ) ρ n 2 A u n B v n 2
Then, we consider
2 α n ρ n A * ( A u n B v n ) , u n b = 2 α n ρ n A u n B v n , A u n A b = 2 α n ρ n ( A u n B v n 2 + A u n B v n , B v n A b )
Then, Equation (9) becomes
u n + 1 b 2 ( 1 α n ) ( a 0 b ) 2 + α n u n b 2 2 α n ρ n A u n B v n , B v n A b α n ρ n ( 2 ρ n ρ ( A * A ) ) A u n B v n 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2
On the other hand, we have
v n + 1 d 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) d 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2 = ( 1 α n ) ( b 0 d ) + α n ( v n d + ρ n B * ( A u n + 1 B v n ) ) 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2 ( 1 α n ) ( b 0 d ) 2 + α n v n d + ρ n B * ( A u n + 1 B v n ) 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2
It follows that
v n + 1 d 2 ( 1 α n ) ( b 0 d ) 2 + α n v n d 2 + α n ρ n 2 B * ( A u n + 1 B v n ) 2 + 2 α n ρ n B * ( A u n + 1 B v n ) , v n d ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2
We have
α n ρ n 2 B * ( A u n + 1 B v n ) 2 = α n ρ n 2 A u n + 1 B v n , B B * ( A u n + 1 B v n ) α n ρ ( B * B ) ρ n 2 A u n + 1 B v n , A u n + 1 B v n = α n ρ ( B * B ) ρ n 2 A u n + 1 B v n 2
At the same time, we have
2 α n ρ n B * ( A u n + 1 B v n ) , v n d = 2 α n ρ n A u n + 1 B v n , B v n B d = 2 α n ρ n ( A u n + 1 B v n 2 A u n + 1 B v n , A u n + 1 B d )
Then, Equation (14) becomes
v n + 1 d 2 ( 1 α n ) ( b 0 d ) 2 + α n v n d 2 + 2 α n ρ n A u n + 1 B v n , A u n + 1 B d α n ρ n ( 2 ρ n ρ ( B * B ) ) A u n + 1 B v n 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2
We have
2 A u n B v n , B v n A b = A u n B v n 2 B v n A b 2 + A u n A b 2
and
2 B v n A u n + 1 , A u n + 1 B d = B v n A u n + 1 2 A u n + 1 B d 2 + B v n B d 2
In the light of A b = B d , combining equalities (18) and (19), adding Equations (12) and (17) together, we finally obtain
u n + 1 b 2 + v n + 1 d 2 ( 1 α n ) ( a 0 b ) 2 + ( 1 α n ) ( b 0 d ) 2 + α n u n b 2 + α n v n d 2 α n ρ n A u n A b 2 + α n ρ n + 1 A u n + 1 A b 2 α n ρ n ( 1 ρ n ρ ( A * A ) ) A u n B v n 2 α n ρ n ( 1 ρ n ρ ( B * B ) ) A u n + 1 B v n 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2
It follows that
u n + 1 b 2 + v n + 1 d 2 ( 1 α n ) ( ( a 0 b ) 2 + ( b 0 d ) 2 ) + α n ( u n b 2 + v n d 2 ρ n A u n A b 2 ) + ρ n + 1 A u n + 1 A b 2 α n ρ n ( 1 ρ n ρ ( A * A ) ) A u n B v n 2 α n ρ n ( 1 ρ n ρ ( B * B ) ) A u n + 1 B v n 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2
We assume Ω n ( b , d ) : = u n b 2 + v n d 2 ρ n A u n A b 2 , in view of (21), we then obtain the following result
Ω n + 1 ( b , d ) α n Ω n ( b , d ) + ( 1 α n ) ( ( a 0 b ) 2 + ( b 0 d ) 2 ) α n ρ n ( 1 ρ n ρ ( A * A ) ) A u n B v n 2 α n ρ n ( 1 ρ n ρ ( B * B ) ) A u n + 1 B v n 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) u n + 1 2 ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) v n + 1 2
According to the conditions of sequence { ρ n } , we deduced
Ω n + 1 ( b , d ) α n Ω n ( b , d ) + ( 1 α n ) ( ( a 0 b ) 2 + ( b 0 d ) 2 ) max { Ω n ( b , d ) , ( a 0 b ) 2 + ( b 0 d ) 2 } max { Ω 0 ( b , d ) , ( a 0 b ) 2 + ( b 0 d ) 2 }
We note that
ρ n A u n A b 2 = ρ n u n b , A * A ( u n b ) ρ n ρ ( A * A ) u n b 2
According to the condition of the sequence { ρ n } , we have
Ω n ( b , d ) = u n b 2 + v n d 2 ρ n A u n A b 2 ( 1 ρ n ρ ( A * A ) ) u n b 2 + v n d 2 > ε ρ ( A * A ) u n b 2 + v n d 2 0
According to Equation (23), we obtain that the sequence { Ω n ( b , d ) } is bounded. Therefore, in view of Equation (25), the sequences { u n } and { v n } are bounded.
Let b and d be the convergence points of sequences { u n } and { v n } , respectively. We obtain
u n + 1 b 2 + v n + 1 d 2 ( 1 α n ) a 0 + α n ( u n ρ n A * ( A u n B v n ) ) b 2 + ( 1 α n ) b 0 + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) d 2 = ( 1 α n ) ( a 0 b ) + α n ( u n ρ n A * ( A u n B v n ) b ) 2 + ( 1 α n ) ( b 0 d ) + α n ( v n d + ρ n B * ( A u n + 1 B v n ) ) 2 α n u n ρ n A * ( A u n B v n ) b 2 + 2 ( 1 α n ) a 0 b , u n + 1 b + α n v n + ρ n B * ( A u n + 1 B v n ) d 2 + 2 ( 1 α n ) b 0 d , v n + 1 d
It follows that
u n + 1 b 2 + v n + 1 d 2 α n u n b 2 + α n ρ n A * ( A u n B v n ) 2 + α n v n d 2 2 α n u n b , ρ n A * ( A u n B v n ) + α n ρ n B * ( A u n + 1 B v n ) 2 + 2 α n v n d , ρ n B * ( A u n + 1 B v n ) + 2 ( 1 α n ) ( a 0 b , u n + 1 b + b 0 d , v n + 1 d )
Combining Equations (10), (11), (15), (16), (18) and (19), we obtain
u n + 1 b 2 + v n + 1 d 2 α n u n b 2 + α n v n d 2 α n ρ n A u n A b 2 + α n + 1 ρ n + 1 A u n + 1 A b 2 α n ρ n ( 1 ρ n ρ ( A * A ) ) A u n B v n 2 α n ρ n ( 1 ρ n ρ ( B * B ) ) A u n + 1 B v n 2 + 2 ( 1 α n ) ( a 0 b , u n + 1 b + b 0 d , v n + 1 d )
It follows that
u n + 1 b 2 + v n + 1 d 2 ρ n + 1 A u n + 1 A b 2 α n ( u n b 2 + v n d 2 ρ n A u n A b 2 ) α n ρ n ( 1 ρ n ρ ( A * A ) ) A u n B v n 2 α n ρ n ( 1 ρ n ρ ( B * B ) ) A u n + 1 B v n 2 + 2 ( 1 α n ) ( a 0 b , u n + 1 b + b 0 d , v n + 1 d )
This implies
Ω n + 1 ( b , d ) α n Ω n ( b , d ) + ( 1 α n ) b n
where
b n = 2 ( a 0 b , u n + 1 b + b 0 d , v n + 1 d ) α n ρ n ( 1 ρ n ρ ( A * A ) ) ( 1 α n ) A u n B v n 2 α n ρ n ( 1 ρ n ρ ( B * B ) ) ( 1 α n ) A u n + 1 B v n 2
Because { u n } and { v n } are bounded, we obtain
b n 2 ( a 0 b , u n + 1 b + b 0 d , v n + 1 d ) 2 ( a 0 b u n + 1 b + b 0 d v n + 1 d ) <
It follows that lim   sup n b n < . Let α n = 1 t n , t n ( 0 , 1 ) , we assume that lim   sup n b n < 1 , for all n n 0 , there exists n 0 such that b n 1 . Then, in view of Equation (30), we have
Ω n + 1 ( b , d ) α n Ω n ( b , d ) + ( 1 α n ) b n = ( 1 t n ) Ω n ( b , d ) + t n b n ( 1 t n ) Ω n ( b , d ) t n = Ω n ( b , d ) t n ( Ω n ( b , d ) + 1 ) Ω n ( b , d ) t n Ω n 1 ( b , d ) t n 1 t n Ω n 0 ( b , d ) i = n 0 n t i
Since i = n 0 t i > Ω n 0 ( b , d ) , there exists N > n 0 such that i = n 0 N t i = . We deduced that
Ω N + 1 ( b , d ) Ω n 0 ( b , d ) i = n 0 N t i < 0
In view of Equation (25), we know that Ω n ( b , d ) is a non-negative real sequence, the inequality in Equation (34) contradicts the fact, hence, lim   sup n b n 1 . Since lim   sup n b n has a finite limit, we take a subsequence { n k } such that
lim   sup   n b n = lim k   b n k = lim k 2 ( a 0 b , u n k + 1 b + b 0 d , v n k + 1 d ) lim k α n k ρ n k ( 1 ρ n k ρ ( A * A ) ) ( 1 α n ) A u n k B v n k 2 lim k α n k ρ n k ( 1 ρ n k ρ ( B * B ) ) ( 1 α n k ) A u n k + 1 B v n k 2
We assume that lim k a 0 b , u n k + 1 b and lim k b 0 d , v n k + 1 d have finite limits, then the following limit exists
lim k α n k ρ n k ( 1 ρ n k ρ ( A * A ) ) ( 1 α n k ) A u n k B v n k 2
and
lim k α n k ρ n k ( 1 ρ n k ρ ( B * B ) ) ( 1 α n k ) A u n k + 1 B v n k 2
Since lim k α n k ( 1 α n k ) = , we deduce that
lim k A u n k B v n k = 0
and
lim k A u n k + 1 B v n k = 0
From Equation (38), we obtain that any weak cluster point of { ( u n k , v n k ) } belongs to Ω . Hence, it follows that
lim k u n k + 1 u n k = lim k ( 1 α n k ) ( a 0 u n k ) + α n k ( u n k ρ n k A * ( A u n k B v n k ) u n k ) lim k ( ( 1 α n k ) ( a 0 u n k ) + ρ n k A * ( A u n k B v n k ) ) = 0
and
lim k v n k + 1 v n k = lim k ( 1 α n k ) ( b 0 v n k ) + α n k ( v n k + ρ n k B * ( A u n k + 1 B v n k ) v n k ) lim k ( ( 1 α n k ) ( b 0 v n k ) + ρ n k B * ( A u n k + 1 B v n k ) ) = 0
This implies that any weak cluster point of { ( u n k + 1 , v n k + 1 ) } belongs to Ω . We assume that { ( u n k + 1 , v n k ) } weakly converges to ( b ˜ , d ˜ ) , then, we have
lim sup n b n lim k 2 ( a 0 b , u n k + 1 b + b 0 d , v n k + 1 d ) = 2 ( a 0 b , b ˜ b + b 0 d , d ˜ d ) 0
In the light of Lemma 2, we have lim n Ω n ( b , d ) = 0 . From Equation (25), we obtain
Ω n ( b , d ) ε ρ ( A * A ) u n b 2 + v n d 2 0
Therefore, we obtain
lim n u n b = 0
lim n v n d = 0
Then,
A b B d = lim n + A u n B v n = 0
Hence, ( b , d ) Ω . We obtain that u n b and v n d . This proof has been completed.
Let f 1 and f 2 be two strict contraction mappings with contraction coefficients of c 1 [ 0 , 1 ) and c 2 [ 0 , 1 ) , respectively.
u n + 1 = P C ( ( 1 α n ) f 1 ( u n ) + α n ( u n ρ n A * ( A u n B v n ) ) ) v n + 1 = P Q ( ( 1 α n ) f 2 ( v n ) + α n ( v n + ρ n B * ( A u n + 1 B v n ) ) )
Corollary 1. 
Let Ω 2 be the solution set of Equation (4) and assume that the solution set Ω 2 is not empty. Then, in the light of Theorem 1, the sequence { ( u n , v n ) } generated by Equation (47) exists ( b ˜ , d ˜ ) Ω 2 such that u n b ˜ and v n d ˜ .

4. Conclusions

In this paper, we proposed an improved alternating CQ algorithm to solve the SEP. This improved method changes the generated iterative sequence from weak to strong convergence.

Author Contributions

Validation, N.-N.T.; Writing—original draft, Y.-J.H.; Writing—review & editing, L.-J.Z. The three authors of this article made equal contributions. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (210170121), the construction project of first-class subjects in Ningxia higher education (213170023).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Censor, Y.; Elfving, T. A multiprojection algorithm using Bregman projections in a product space. Numer. Algorithms 1994, 8, 221–239. [Google Scholar] [CrossRef]
  2. Byrne, C. A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Prob. 2004, 18, 103–120. [Google Scholar] [CrossRef] [Green Version]
  3. López, G.; Martín, V.; Wang, F.; Xu, H.K. Solving the split feasibility problem without prior knowledge of matrix norms. Inverse Probl. 2012, 28, 374–389. [Google Scholar] [CrossRef]
  4. Byrne, C. Iterative oblique projection onto convex sets and the split feasibility problem. Inverse Probl. 2002, 18, 441–453. [Google Scholar] [CrossRef]
  5. Petrot, N.; Suwannaprapa, M.; Dadashi, V. Convergence theorems for split feasibility problems on a finite sum of monotone operators and a family of nonexpansive mappings. J. Inequal. Appl. 2018, 1, 2018. [Google Scholar] [CrossRef] [Green Version]
  6. Yao, Y.; Wu, J.; Liou, Y.C. Regularized methods for the split feasibility problem. Abstr. Appl. Anal. 2012, 1, 183–194. [Google Scholar] [CrossRef]
  7. Wang, F. Polyak’s gradient method for split feasibility problem constrained by level sets. Numer. Algorithms 2018, 77, 925–938. [Google Scholar] [CrossRef]
  8. Dong, Q.L.; Tang, Y.C.; Cho, Y.J.; Rassias, T.M. Optimal choice of the step length of the projection and contraction methods for solving the splity feasibility problem. J. Glob. Optim. 2018, 71, 341–360. [Google Scholar] [CrossRef]
  9. Censor, Y.; Xiao, Y.; Galvin, J.M. On linear infeasibility arising in intensity-modulated radiation therapy inverse planning. Linear Algebra Appl. 2008, 428, 1406–1420. [Google Scholar] [CrossRef] [Green Version]
  10. Lopez, G.; Martin, V.; Xu, H.K. Iterative algorithms for the multiple-sets split feasibility problem. Inverse Probl. 2009, 2009, 243–279. [Google Scholar]
  11. Chen, C.; Zhang, X.; Zhang, G.; Zhang, Y. A two-grid finite element method for nonlinear parabolic integro-differential equations. Int. J. Comput. Math. 2018, 96, 2010–2023. [Google Scholar] [CrossRef]
  12. Chen, C.; Liu, H.; Zheng, X.; Wang, H. A two-grid mmoc finite element method for nonlinear variable-order time-fractional mobile/immobile advection-diffusion equations. Comput. Math. Appl. 2020, 79, 2771–2783. [Google Scholar] [CrossRef]
  13. Palta, J.R.; Mackie, T.R.; Chen, Z. Intensity-modulated radiation therapy the state of the art. Med. Phys. 2003, 30, 3265. [Google Scholar] [CrossRef]
  14. Che, H.; Chen, H.; Wang, Y. On the M-eigenvalue estimation of fourth-order partially symmetric tensors. J. Ind. Manag. Optim. 2020, 16, 309–324. [Google Scholar] [CrossRef] [Green Version]
  15. Che, H.; Chen, H.; Wang, Y. C-eigenvalue inclusion theorems for piezoelectric-type tensors. Appl. Math. Lett. 2019, 89, 41–49. [Google Scholar] [CrossRef]
  16. Chen, H.; Huang, Z.H.; Qi, L. Copositivity detection of tensors:theory and algorithm. J. Optim. Theory Appl. 2017, 174, 746–761. [Google Scholar] [CrossRef]
  17. Chen, C.; Li, K.; Chen, Y.; Huang, Y. Two-grid finite element methods combined with Crank-Nicolson scheme for nonlinear Sobolev equations. Adv. Comput. Math. 2019, 45, 611–630. [Google Scholar] [CrossRef]
  18. Zhang, X.; Liu, L.; Wu, Y.; Cui, Y. Existence of infinitely solutions for a modified nonlinear Schrodinger equation via dual approach. Electron. J. Differ. Equ. 2018, 147, 1–15. [Google Scholar]
  19. Zhang, X.; Liu, L.; Wu, Y. Multiple positive solutions of a singular fractional differential equation with negatively perturbed term. Math. Comput. Model. 2012, 55, 1263–1274. [Google Scholar] [CrossRef]
  20. Zhang, X.; Wu, Y.; Cui, Y. Existence and nonexistence of blow-up solutions for a Schrdinger equation involving a nonlinear operator. Appl. Math. Lett. 2018, 82, 85–91. [Google Scholar] [CrossRef]
  21. Che, H.; Chen, H.; Li, M. A new simultaneous iterative method with a parameter for solving the extended split equality problem and the extended split equality fixed point problem. Numer. Algorithms 2018, 79, 1231–1256. [Google Scholar] [CrossRef]
  22. Censor, Y.; Elfving, T.; Kopf, N. The multiple-sets split feasibility problem and its applications for inverse problem. Inverse Probl. 2005, 21, 2071–2084. [Google Scholar] [CrossRef] [Green Version]
  23. Wang, J.; Hu, Y.; Yu, C. A family of projection gradient methods for solving the multiple-sets split feasibility problem. J. Optim. Theory Appl. 2019, 183, 520–534. [Google Scholar] [CrossRef]
  24. Taddele, G.H.; Kumam, P.; Gebrie, A.G. An inertial extrapolation method for multiple-set split feasibility problem. J. Inequalities Appl. 2020, 1, 2020. [Google Scholar] [CrossRef]
  25. Combettes, P.L. The foundations of set theoretic estimation. Proc. IEEE 1993, 81, 182–208. [Google Scholar] [CrossRef]
  26. Censor, Y.; Bortfel, D.; Martin, B.; Trofimov, A. A unified approch for inversion problems in intensity-modulated radiation therapy. Phys. Med. Biol. 2006, 51, 2353–2365. [Google Scholar] [CrossRef] [Green Version]
  27. Attouch, H.; Redont, P.; Soubeyran, A. A new class of alternating proximal minimization algorithms with costs-to-move. SIAM J. Optim. 2007, 18, 1061–1081. [Google Scholar] [CrossRef]
  28. Byrne, C.; Moudafi, A. Extensions of the CQ algorithms for the split feasibility and split equality problems. Doc. Trav. 2012, 18, 1485–1496. [Google Scholar]
  29. Moudafi, A. Alternating CQ-algorithms for convex feasibility and split fixed-point problems. Doc. Trav. 2013, 15, 809–818. [Google Scholar]
  30. Che, H.; Chen, H. A relaxed self-adaptive projection algorithm for solving the multiple-sets split equality problem. J. Funct. Spaces 2020, 10, 1–12. [Google Scholar] [CrossRef]
  31. Yao, Y.; Shahzad, N. Strong convergence of a proximal point algorithm with general errors. Optim. Lett. 2012, 6, 621–628. [Google Scholar] [CrossRef]
  32. Xu, H.K. Iterative algorithms for nonlinear operators. J. Lond. Math. Soc. 2002, 66, 240–256. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

He, Y.-J.; Zhu, L.-J.; Tan, N.-N. An Improved Alternating CQ Algorithm for Solving Split Equality Problems. Mathematics 2021, 9, 3313. https://doi.org/10.3390/math9243313

AMA Style

He Y-J, Zhu L-J, Tan N-N. An Improved Alternating CQ Algorithm for Solving Split Equality Problems. Mathematics. 2021; 9(24):3313. https://doi.org/10.3390/math9243313

Chicago/Turabian Style

He, Yan-Juan, Li-Jun Zhu, and Nan-Nan Tan. 2021. "An Improved Alternating CQ Algorithm for Solving Split Equality Problems" Mathematics 9, no. 24: 3313. https://doi.org/10.3390/math9243313

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop