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Strong convergence of split equality variational inequality, variational inclusion, and multiple sets fixed point problems in Hilbert spaces with application
Journal of Inequalities and Applications volume 2024, Article number: 40 (2024)
Abstract
This paper introduces an innovative inertial simultaneous cyclic iterative algorithm designed to address a range of mathematical problems within the realm of split equality variational inequalities. Specifically, the algorithm accommodates finite families of split equality variational inequality problems, infinite families of split equality variational inclusion problems, and multiple-sets split equality fixed point problems involving demicontractive operators in infinite-dimensional Hilbert spaces. The algorithm integrates well-established methods, including the cyclic method, the inertial method, the viscosity approximation method, and the projection method. We establish the strong convergence of this proposed algorithm, demonstrating its applicability in various scenarios and unifying disparate findings from existing literature. Additionally, a numerical example is presented to validate the primary convergence theorem.
1 Introduction
The variational inequality problem (VIP) was developed by Stampacchia [1] in 1964, and it provides useful mathematical tools for investigating interesting issues like game theory, economic equilibrium mechanics, partial differential equations, and optimization theory; see [2–8]. Due to their active role and significance in nonlinear analysis, VIPs are currently growing in both theory and practice; for examples, see [8, 9]. The variational inequality problem (VIP), one of the significant problems, has drawn the interest of numerous mathematicians throughout the years. One can define VIP as finding \(\bar{r}^{*}\in Q\) such that
where L is a nonlinear mapping. The set of solutions of the VIP is represented by \(\operatorname{VIP}(Q, L)\). One of the most often used methods for studying VIPs is the projection method, which can be applied to demonstrate the equivalence between fixed points and VIPs.
In 1994, Censor and Elfving [10] introduced the split feasibility problem (SFP) for modeling inverse problems. SFP identifies a point in a closed convex subset of a Hilbert space such that the image of that point under a particular bounded linear operator belongs to a closed convex subset of a different Hilbert space. Currently, SFPs are implemented in a number of fields, including signal processing [11], computer tomography [12], image restoration [13, 14], and intensity modulated radiation treatment (IMRT) [12, 15]. Recently, numerous authors have presented various split-type problems; see [11, 12, 15] for significant developments in this direction or related topics. In 2005, Censor et al. [12] proposed multiple-sets split feasibility problems (MSSFPs), which have applications in the inverse problem of intensity-modulated radiation therapy. In 2009, Censor and Segal [16] proposed split common fixed point problems (SCFPPs) to generalize the split feasibility problem, the convex feasibility problem, and the multiple-sets split feasibility problem. For more related problems, see [17–23].
The split equality problem (SEP) was firstly proposed by Moudafi [24] in 2013. Partial and asymmetric relationships between the variables of two spaces are permitted by SEP. Moudafi [25] studied the split equality fixed point problem (SEFPP), which involves strongly nonexpansive mapping. Additionally, Moudafi and Al-Shemas [26] proved a weak convergence result and proposed the simultaneous iterative method. One can see the above-mentioned iterative approaches need the norm of the bounded linear operators \(\|A\|\) and \(\|B\|\). Calculating the norm of a bounded linear operator can be difficult in some situations. Lopez et al. [27] provided a valuable solution to this numerical challenge. In 2014, Zhao [28] improved the result of Moudafi [16, 24, 25]. By considering the step size that does not need previous knowledge of the operator norm and obtained the weak convergence result for quasi-nonexpansive mappings. A significant convergence result involving quasi-nonexpansive mappings for solving the split common fixed point problem was demonstrated by Shehu et al. [29] in 2017. The split equality variational inequality problem (SEVIP) has been extensively studied and used to solve many real-world issues, including modeling intensity-modulated radiation therapy treatment planning [30]. Mathematically, SEVIPs are very general since they include common solutions of the variational inequality problem [31], split equality zero point problem [32], split equality feasibility problem [33], and common zeros of mappings [34]. SEVIP is stated as a problem of finding \((\bar{r}^{*}, \bar{s}^{*})\in Q_{1}\times Q_{2}\) such that
where A, B are bounded linear operators, \(Q_{1}\), \(Q_{2}\) are nonempty closed convex subsets of \(H_{1}\) and \(H_{2}\), respectively, and \(L^{x}:H_{1}\to H_{1}\), \(L^{y}:H_{2}\to H_{2}\) are nonlinear operators.
In 2014, Wu et al. [35] proposed the multiple-sets split equality fixed point problem (MSEFPP) stated as a problem of finding \((\bar{r}^{*}, \bar{s}^{*})\in H_{1}\times H_{2}\) such that
where \(S_{k_{1}}:H_{1}\to H_{1}\), \(U_{k_{2}}:H_{2}\to H_{2}\) are nonlinear operators with \(1\le k_{1}\le N \), \(1\le k_{2}\le P\) and A, B are bounded linear operators. MSEFPP includes the multiple-sets split equality problem (MSEP), the split equality fixed point problem (SEFPP), the multiple-sets split feasibility problem (MSFP), the split equality problem (SEP), and many others; see [36]. MSEFPP had a significant impact on the growth of various fields including signal processing and image restoration.
Variational inclusion problems had a significant impact on the growth of many fields including mathematical programming, optimal control, mathematical economics, variational inequalities, game theory, complementarity problems, etc. In 2011, Moudafi [33] studied the split monotone variational inclusion problem (SMVIP). SMVIP is to find \(\bar{r}^{*} \in H\) such that
where A is a bounded linear operators, \(K:H_{1}\to H_{1}\) and \(T:H_{2}\to H_{2}\) are set-valued maximal monotone mappings. The general split equality variational inclusion problem (GSEMVIP) was studied by Chang et al. [37] in 2014, generalizing the SMVIP. GSEMVIP is stated as finding \((\bar{r}^{*},\bar{s}^{*})\in H_{1}\times H_{2}\) such that
The set of solutions to the GSEMVIP is represented by Ω. Chang et al. [37] proved a strong convergence theorem for GSEMVIP. In 2017, Latif and Eslamian [38] introduced a method to find the common solution of three split equality problems, namely variational inequality, equilibrium, and fixed point problems of nonexpansive semigroups. In 2019, Kazmi et al. [39] proposed an iterative method to find the common solution of three split equality problems, namely variational inclusion, generalized general variational-like inequality, and fixed point problems for quasi-nonexpansive mapping.
The inertial extrapolation methods were extensively used as an acceleration technique. The researchers used inertial extrapolation to build numerous iterative algorithms. The critical characteristic of inertial extrapolation is that the following iteration is determined using the results of the previous two iterations. The efficiency of its original (non-inertial) algorithms, like [40, 41], has been significantly enhanced by inertial approaches, as demonstrated by numerical studies. As a result, a lot of research is currently focused on exploiting inertial extrapolation to improve current methods (see [42, 43] and the references therein).
Suppose that \(H_{1}\), \(H_{2}\), and \(H_{3}\) are real Hilbert spaces. Assume that \(A:H_{1}\rightarrow H_{3}\) and \(B:H_{2}\rightarrow H_{3}\) are two bounded linear operators. Suppose that \(\{K_{i}\}_{i=1}^{\infty}:H_{1} \to 2^{H_{1}}\) and \(\{T_{i}\}_{i=1}^{\infty}:H_{2} \to 2^{H_{2}}\) are maximal monotone mappings. Let \(S_{k_{1}}:H_{1}\to H_{1}\) and \(U_{k_{2}}:H_{2}\to H_{2}\) be demicontractive operators such that \(I-S_{k_{1}}\) and \(I-U_{k_{2}}\) are demiclosed at zero for \(1\le {k_{1}} \le N\), \(1\le{k_{2}} \le P\) with constants \(0\le \kappa _{k_{1}}<1\) and \(0\le \phi _{k_{2}}<1\), respectively, where \(\kappa =\max {\kappa _{k_{1}} }\) and \(\phi =\max {\phi _{k_{2}}}\). Define \(S_{k_{1}}=S_{k_{1}(\mod N)+1}\) and \(U_{k_{2}}=U_{k_{2}(\mod P)+1}\). Let \(L_{j}^{x}:H_{1} \to H_{1}\), \(L_{j}^{y}:H_{2} \to H_{2}\) be \(c_{j}^{x}\), \(c_{j}^{y}\)-inverse strongly monotone mappings, respectively, \(\bar{c}^{x}=\min_{j=1,2,\ldots,M}\{c_{j}^{x}\}\) and \(\bar{c}^{y}=\min_{j=1,2,\ldots,M}\{c_{j}^{y}\}\). We represent the required solution set by Γ, where
2 Preliminaries
In this study, we take H to be a real Hilbert space with the inner product \(\langle .,. \rangle \) and the corresponding norm \(\|.\|\). For weak and strong convergence of \(\{x_{n}\}\) to \(x^{*}\), we use the notations \(x_{n} \rightharpoonup x^{*}\) and \(x_{n} \rightarrow x^{*}\). Assume that Q is a nonempty, closed, and convex subset of a Hilbert space H. The collection of all fixed points for mapping Ū is denoted by \(\operatorname{Fix}(\bar{U})\).
Definition 2.1
Suppose that \(\bar{U}: H\rightarrow H\) is a mapping. Then Ū is called
-
(i)
contraction if \(\lambda \in [0,1)\) such that
$$\Vert \bar{U}u-\bar{U}v \Vert \leq \lambda \Vert \bar{u}-\bar{v} \Vert \quad \text{for all } \bar{u},\bar{v} \in H; $$ -
(ii)
λ-demicontractive if there is \(\lambda \in [0,1)\) such that
$$\Vert \bar{U}\bar{u}-\bar{v} \Vert ^{2}\leq \Vert \bar{u}- \bar{v} \Vert ^{2}+ \lambda \Vert \bar{u}-\bar{U}\bar{u} \Vert ^{2}\quad \text{for all }\bar{u} \in H\text{ and } \bar{v} \in \operatorname{Fix}(\bar{U}); $$ -
(iii)
ω̄- inverse strongly monotone (ism) if there is \(\bar{\omega}>0\) such that
$$\langle \bar{U}\bar{u}-\bar{U}\bar{v},\bar{u}-\bar{v} \rangle \geq \bar{\omega} { \Vert \bar{U}\bar{u}-\bar{U}\bar{v} \Vert }^{2}\quad \text{for all } \bar{u}, \bar{v} \in H. $$
Definition 2.2
Suppose that \(\bar{U}:H \rightarrow 2^{H}\) is a mapping. Then Ū is called
-
(i)
monotone if
$$\langle \bar{r}-\bar{t}, \bar{u}-\bar{v} \rangle \ge 0\quad \text{for all } \bar{r},\bar{t} \in H, \bar{u} \in \bar{U}\bar{r}, \bar{v} \in \bar{U} \bar{t}; $$ -
(ii)
maximal monotone if it is monotone and the graph \(G(\bar{U})\) is not properly contained in any other monotone mapping, where
$$G(\bar{U})= \bigl\{ (\bar{x},\bar{y}) \in H \times H :\bar{y} \in \bar{U}( \bar{x})\bigr\} . $$
Lemma 2.3
[44] Let H be a real Hilbert space, then
-
(i)
\(2 \langle \bar{r},\bar{t} \rangle =\|\bar{r}\|^{2}+\| \bar{t}\|^{2}-\|\bar{r}-\bar{t}\|^{2}=\|\bar{r}+\bar{t}\|^{2}-\| \bar{r}\|^{2}-\|\bar{t}\|^{2}\) for all \(\bar{r},\bar{t} \in H\);
-
(ii)
\(\|\bar{r}+\bar{t}\|^{2} \leq \|\bar{r}\|^{2}+2 \langle \bar{t}, \bar{r}+\bar{t} \rangle \) for all \(\bar{r},\bar{t} \in H\);
-
(iii)
\(\|\alpha _{0} \bar{r}_{0}+\alpha _{1} \bar{r}_{1}+\alpha _{2} \bar{r}_{2}\|^{2}7= \alpha _{0}\|\bar{r}_{0}\|^{2}+\alpha _{1}\| \bar{r}_{1}\|^{2}+\alpha _{2} \|\bar{r}_{2}\|^{2}-\alpha _{0}\alpha _{1} \|\bar{r}_{0}-\bar{r}_{1}\|^{2} -\alpha _{0}\alpha _{2}\|\bar{r}_{0}-\bar{r}_{2}\|^{2}-\alpha _{2} \alpha _{1}\|\bar{r}_{1}-\bar{r}_{2}\|^{2}\) for \(\bar{r}_{i} \in H\), \(\alpha _{i} \in [0,1]\), \(i=0,1,2\), and \(\sum_{i=0}^{2}\alpha _{i}=1\).
Lemma 2.4
[45] The metric projection \(P_{Q}\) satisfies:
-
(i)
\(\bar{z}=P_{Q}\bar{u}\) iff \(\langle \bar{u}-\bar{z},\bar{z}-\bar{y} \rangle \leq 0 \) for all \(\bar{y},\bar{z}\in Q\) and \(\bar{u}\in H\);
-
(ii)
\(\|P_{Q}\bar{u}-\bar{z}\|^{2} \le \|\bar{u}-\bar{z}\|^{2}-\|P_{Q} \bar{u}-\bar{u}\|^{2} \) for all \(\bar{u}\in H\) and \(\bar{z} \in Q\).
Lemma 2.5
[46] Suppose that \(\bar{U}:H\rightarrow H\) is a κ-demicontractive operator and \(\bar{U}_{\lambda}:=(1-\lambda )I+\lambda \bar{U}\) for any \(\lambda \in (0,1-\kappa )\), where \(\kappa <1\), then for \(\bar{v}^{*} \in \operatorname{Fix}(\bar{U})\) and \(\bar{v}\in H\)
Lemma 2.6
[47] Suppose that \(\{s_{n} \}\) is a bounded sequence in a Hilbert space H, \(\lim_{n\rightarrow \infty}\|s_{n+1}-s_{n}\|=0\), and \(z^{*}\in \omega _{w}(s_{n})\). Then there is a subsequence \(\{s_{m_{k}}\}\) of \(\{s_{n}\}\) such that \(m_{k}(\mod N)+1=i\) and \(s_{m_{k}}\rightharpoonup z^{*}\), where \(i \in \{1,2,\ldots,N\}\).
Lemma 2.7
[48] Assume that \(\{s_{n}\}\) is a sequence of real numbers and there is a subsequence \(\{n_{k}\}\) of \(\{n\}\) satisfying \(s_{n_{k}}< s_{n_{k}+1} \) for all \(k \in \mathbb{N} \). Then there is a nondecreasing sequence \(\{m_{i}\} \subset \mathbb{N}\) such that \(m_{i}\rightarrow \infty \) and satisfies
where \(i\in \mathbb{N}\) and \(m_{i}=\max \{k\leq i:s_{k} < s_{k+1}\}\).
Lemma 2.8
[49] Assume that \(\{s_{n}\}\subset [0,\infty )\), \(\{t_{n}\} \subset (-\infty , \infty )\), and \(\{\omega _{n}\}\subset [0,1]\) such that
where \(\sum_{n=0}^{\infty}\omega _{n}=\infty \) and \(\limsup_{n\rightarrow \infty} t_{n}\leq 0\), then \(\lim_{n\rightarrow \infty}s_{n}=0\).
Lemma 2.9
[50] Assume that \(L : H \to 2^{H}\) is a maximal monotone mapping, \(\tau > 0\), and \(J^{L}_{\tau} \) is the resolvent mapping of L defined by \(J^{L}_{\tau}:= (I +{\tau} L)^{-1}\). Suppose that \(L^{-1}(0)\ne \phi \). Then \(\langle \bar{r}^{*}-J^{L}_{\tau}\bar{r}^{*}, J^{L}_{\tau} \bar{r}^{*}-q \rangle \ge 0\) for all \(\bar{r}^{*}\in H\), \(\tau >0\) and \(q\in L^{-1}(0)\).
Lemma 2.10
[51] Let \(L:Q\to H\) be a mapping and \(\bar{r}^{*}\in Q\). Then, for \(b>0\), \(\bar{r}^{*}\in \operatorname{VIP}(Q,L)\) iff \(\bar{r}^{*}= P_{Q}(I-bL) \bar{r}^{*} \), where \(P_{Q}\) is the metric projection of H onto Q.
Lemma 2.11
[52] Let \(L_{j} :Q\to H\) be a \(c_{j}\)-ism mapping with \(\bar{c}=\min_{j=1,2,\ldots,M}\{c_{j}\}\), where \(j=1,2,\ldots,M\) and \(\bigcap_{j=1}^{M} \operatorname{VIP}(Q,L_{j})\ne \phi \), then \(\bigcap_{j=1}^{M} \operatorname{VIP}(Q,L_{j}) =\operatorname{VIP}(Q, \sum_{j=1}^{M}c_{j}L_{j})\), where \(0< b_{j}<1\) for every \(j=1,2,\ldots,M\) and \(\sum_{j=1}^{M}c_{j}=1\).
Lemma 2.12
[53] Let \(\bar{U}:Q\rightarrow Q\) be a μ-demicontractive mapping and \(\operatorname{Fix}(\bar{U})\ne \phi \), then \(\operatorname{Fix}(\bar{U})\) is closed and convex.
Inspired and motivated by ongoing research in this direction, we study a new inertial simultaneous cyclic iterative algorithm to find the common solution of three split equality problems, namely the finite family of variational inequalities, infinite family of variational inclusion, and multiple-sets fixed point problems for demicontractive operators. The proposed algorithm does not require prior knowledge of the operator norm, and we demonstrate our algorithm’s strong convergence under some mild circumstances. We also discuss how our findings can be used to solve the problem of intensity-modulated radiation therapy (IMRT). In addition, we provide a numerical example to illustrate the numerical behavior of the suggested method and to compare it with different methods.
3 Main results
Let \(H_{1}\), \(H_{2}\), \(H_{3}\) be Hilbert spaces, \(Q_{1}\) and \(Q_{2}\) be closed convex subsets of \(H_{1}\) and \(H_{2}\), respectively. Assume that \(\psi _{1}:H_{1} \to H_{1}\), \(\psi _{2}:H_{2} \to H_{2}\) are \(\lambda _{1}\), \(\lambda _{2}\) contractions mappings with \(\lambda =\max \{\lambda _{1}, \lambda _{2}\}\) and \(A : H_{1} \to H_{3}\) and \(B : H_{2} \to H_{3}\) are two (nonzero) bounded linear operators. Let \(A^{*}\) and \(B^{*}\) denote the adjoint of A and B respectively. Let \(L_{j}^{x}:H_{1} \to H_{1}\), \(L_{j}^{y}:H_{2} \to H_{2}\) be \(c_{j}^{x}\), \(c_{j}^{y}\)-inverse strongly monotone mappings, respectively, \(\bar{c}^{x}=\min_{j=1,2,\ldots,M}\{c_{j}^{x}\}\) and \(\bar{c}^{y}=\min_{j=1,2,\ldots,M}\{c_{j}^{y}\}\). Suppose that \(\{K_{i}\}_{i=1}^{\infty}:H_{1} \to 2^{H_{1}}\) and \(\{T_{i}\}_{i=1}^{\infty}:H_{2} \to 2^{H_{2}}\) are maximal monotone mappings. Let \(S_{k_{1}}:H_{1}\to H_{1}\) and \(U_{k_{2}}:H_{2}\to H_{2}\) be demicontractive operators such that \(I-S_{k_{1}}\) and \(I-U_{k_{2}}\) are demiclosed at zero for \(1\le {k_{1}} \le N\), \(1\le{k_{2}} \le P\) with constants \(0\le \kappa _{k_{1}}<1\) and \(0\le \phi _{k_{2}}<1\) respectively, where \(\kappa =\max {\kappa _{k_{1}} }\) and \(\phi =\max {\phi _{k_{2}}}\). Define \(S_{k_{1}}=S_{k_{1}(\mod N)+1}\) and \(U_{k_{2}}=U_{k_{2}(\mod P)+1}\).
Algorithm 3.1
Consider \(0< a<\nu ^{x}_{n}\le 2\bar{c}^{x}\), \(0< b<\nu ^{y}_{n}\le 2\bar{c}^{y}\) for some \(a,b\in \mathbb{R} \), \(b_{j}^{x}, b_{j}^{y}\in (0,1)\) for \(j=1,2,\ldots,M\), \(\{\delta _{n}\}, \{\mu _{n}\}, \{\eta _{n,i}\}, \{\sigma _{n}\} \subset [\bar{d},\bar{e}]\subset (0,1)\), \(a_{n} \in (\delta ,1-\phi -\delta )\), \(\beta \in (0,1)\), \(\sum_{n=1}^{\infty}\tau _{n}<\infty \) and \(\{\kappa _{n}\}\subset [\kappa , \bar{\kappa}] \), where \(0<\kappa \le \bar{\kappa}\), \(\delta >0\), d̄ and \(\bar{e} \in \mathbb{R}\). Choose \(x_{0}, x_{1} \in Q\) and \(\gamma _{n}\) such that \(0 \le \gamma _{n}\le \bar{\gamma _{n}}\), where
Compute
where the stepsize \(\rho _{n}\) is given as
Remark 3.2
Assume that the solution set Γ is nonempty, then \(\{\rho _{n}\}\) in Algorithm 3.1 is well defined; see [54].
Lemma 3.3
Let the solution set Ω be nonempty, \(\{x_{n}\}\) and \(\{y_{n}\}\) be bounded sequences. Suppose that \(\|l_{n}-J_{\theta _{i}}^{(K_{i},T_{i})}(I-\rho _{{n}}(H^{*}H)l_{n}) \|\to 0\), \(\|h_{n}-w_{n}\|\to 0\), \(\|v_{n}-t_{n}\|\to 0\), \(\|x_{n}-w_{n}\|\to 0\), and \(\|y_{n}-t_{n}\|\to 0\). Then \(l^{*}=(x^{*},y^{*})\) is a solution of Problem (1.5) iff \(l^{*}= J_{\theta _{i}}^{(K_{i},T_{i})}(I-\rho _{n}(H^{*}H))l^{*}\) for every \(\theta _{i}>0\) and \(\rho _{n}>0\), where \(i\ge 1\), \(n\in \mathbb{N}\),
Proof
Now, we have to show that \(l^{*}=(x^{*}, y^{*})\in \Omega \). For this we have to show for \(i\ge 1\), \(l^{*}=J_{\theta _{i}}^{(K_{i},T_{i})}(I-\rho _{n}(H^{*}H))l^{*}\). Suppose that \(l^{*}\ne J_{\theta _{i}}^{(K_{i},T_{i})}(I-\rho _{n}(H^{*}H))l^{*}\). As \(\{x_{n}\}\) and \(\{y_{n}\}\) are bounded, take subsequences \(\{x_{n_{q}}\}\) of \(\{x_{n}\}\) and \(\{y_{n_{q}}\}\) of \(\{y_{n}\}\) such that \(x_{n_{q}} \rightharpoonup x^{*} \in H_{1} \) and \(y_{n_{q}} \rightharpoonup y^{*} \in H_{2} \). Additionally, from given conditions, there are subsequences \(\{h_{n_{q}}\}\) \(\{w_{n_{q}}\}\), \(\{v_{n_{q}}\}\), and \(\{t_{n_{q}}\}\) respectively such that \(h_{n_{q}}\rightharpoonup x^{*} \), \(w_{n_{q}}\rightharpoonup x^{*} \), \(v_{n_{q}}\rightharpoonup y^{*} \), and \(t_{n_{q}}\rightharpoonup y^{*} \). Define \(l_{n}=(h_{n}, v_{n})\). Using the given condition, we have
Now, using Opial’s condition and equation (3.3), we get
which is a contradiction. Hence \(l^{*}= J_{\theta _{i}}^{(K_{i},T_{i})}(I-\rho _{n}(H^{*}H))l^{*}\) for \(i\ge 1\).
Conversely, assume that \(l^{*}= J_{\theta _{i}}^{(K_{i},T_{i})}(I-\rho _{n}(H^{*}H))l^{*}\). Subsequently,
As the solution set is nonempty, \(K_{i}^{-1}(0)\) and \(T_{i}^{-1}(0)\) are nonempty. Using Lemma 2.9 and equation (3.4), we have
which implies
Similarly,
Adding equations (3.5) and (3.6), we have
On simplifying
As the solution set is nonempty, let \(l'=(x',y')\in \Omega \) for each \(i\ge 1\). Therefore, \(x' \in K_{i}^{-1}(0)\) and \(y'\in T_{i}^{-1}(0)\) and \(Ax'=By'\). Taking \(x_{1}=x'\) and \(y_{1}=y'\) in equation (3.7), we have
which implies \(A x^{*}=By^{*}\), and using \(A x^{*}=By^{*}\) in equation (3.4), we have
which implies \(0\in K_{i}(x^{*})\) and \(0\in T_{i}(y^{*})\) for all \(i\ge 1\). Hence, \(l^{*}\) is the solution of Problem (1.5). □
Theorem 3.4
Let the sequence \(\{(x_{n}, y_{n})\}\) be generated by the iterative Algorithm 3.1and a solution set Γ be nonempty. Suppose that the following conditions are satisfied:
-
(i)
\(\sum_{j=1}^{M}b_{j}^{x}=\sum_{j=1}^{M}b_{j}^{y}=1\), \(\sum_{n=1}^{\infty}\nu ^{x}_{n}<\infty \), \(\sum_{n=1}^{\infty}\nu ^{y}_{n}< \infty \),
$$0< a< \nu ^{x}_{n}\le 2\bar{c}^{x},\qquad 0< b< \nu ^{y}_{n}\le 2\bar{c}^{y}\quad \textit{for some }a,b \in \mathbb{R} ; $$ -
(ii)
\(\tau _{n}=o(\sigma _{n})\), \(\lim_{n\to \infty}\frac{\tau _{n}}{\sigma _{n}}=0\), \(\lim_{n\to \infty}\sigma _{n}=0\), \(\sum_{n=1}^{\infty}\sigma _{n}= \infty \);
-
(iii)
\(\delta _{n}+\mu _{n}+\sum_{i=1}^{\infty}\eta _{n,i}=1\) for \(n\ge 0\),
then the sequence generated by the iterative Algorithm 3.1converges strongly to \((\bar{r}^{*}, \bar{s}^{*})\in \Gamma \).
Proof
Firstly, we will show that the sequence \(\{(x_{n}, y_{n})\}\) is bounded. Take \((\bar{r}^{*}, \bar{s}^{*})\in \Gamma \). Let
then using Lemma 2.3, we estimate
and
As \(A\bar{r}^{*}=B\bar{s}^{*}\) and adding equations (3.10) and (3.11), we have
Let \(z_{n}^{1}=P_{Q_{1}}(I-\nu _{n}^{x}\sum_{j=1}^{M} b_{j}^{x}L_{j}^{x})w_{n}\) and \(z_{n}^{2}=P_{Q_{2}}(I-\nu _{n}^{y}\sum_{j=1}^{M} b_{j}^{y}L_{j}^{y})t_{n}\).
As \(\bar{r}^{*} \in \operatorname{VIP}(L_{j}^{x},Q_{1})\). Also, from the definition of inverse strongly monotone and from condition (i), we have
Similarly,
Adding equations (3.14) and (3.16), we have
As \(\bar{r}^{*} \in \operatorname{VIP}(L_{j}^{x},Q_{1})\), we have
Using \(I-\nu _{n}^{x}\sum_{j=1}^{M} b_{j}^{x}L_{j}^{x}\) is nonexpansive and on rearranging the terms, we have
Similarly,
Adding equations (3.21) and (3.22), we have
Using equation (3.2), we get
Similarly,
As \(\lim_{n\rightarrow \infty}\frac{\tau _{n}}{\sigma _{n}}=0\), there is \(M_{1}>0\) such that \(\tau _{n}\le \sigma _{n} M_{1}\). Using equations (3.24) and (3.25) and using Minkowski’s inequality, we get
Now, using Lemma 2.5, we have
and
Using Lemma 2.3, condition (iii), and equation (3.27), we have
Similarly,
Adding equations (3.30), (3.31) and using equation (3.13), we have
Using equations (3.2), (3.18), and (3.33), we estimate
As \(\lambda =\max \{\lambda _{1}, \lambda _{2}\}\), using equations (3.2), (3.19), (3.26), and Minkowski’s inequality, we have
Using equations (3.23), (3.33), and (3.34), we estimate
From Lemma 2.3 and equation (3.28), we have
Similarly,
Using equation (3.13) and adding equations (3.39) and (3.41), we get
Using equation (3.12) and adding equations (3.40) and (3.42), we get
From equations (3.37) and (3.44), we have
Hence \(\{x_{n}\}\) and \(\{y_{n}\}\) are bounded. Subsequently, \(\{w_{n}\}\), \(\{t_{n}\}\), \(\{h_{n}\}\), and \(\{v_{n}\}\) are also bounded.
Similarly,
Adding equations (3.47) and (3.48), we have
From equation (3.2), we have
Similarly,
Adding equations (3.50) and (3.51) and using equations (3.18) and (3.49), we get
where
Hence,
which gives
where \(\bar{p}_{n}=\|x_{n}-\bar{r}^{*}\|^{2}+\|y_{n}-\bar{s}^{*}\|^{2}\), \(d_{n}=2\sigma _{n}(1-\lambda )\) and
Case 1. Assume that there exists \(N\in \mathbb{N}\) such that \(\{\bar{p}_{n}\}\) is decreasing for \(n\geq N\). As \(\{\bar{p}_{n}\}\) is bounded and monotonic and subsequently convergent, as \(\lim_{n\rightarrow \infty}\sigma _{n}=0\) and \(\tau _{n}=o(\sigma _{n})\), using equations (3.35), (3.43), (3.49), we have
which implies
Taking limit \(n \to \infty \) in equation (3.58) and using condition (ii), we get
From equations (3.32), (3.35), and (3.49), we get
which implies
Taking limit \(n \to \infty \) in equation (3.60) and using condition (ii), we get
From equations (3.35), (3.45), and (3.49), we have
Taking limit \(n \to \infty \) in equation (3.63) and using condition (ii), we get
As A and B are bounded linear operators and hence
From equations (3.34), (3.35), and (3.49), we get
Taking limit \(n \to \infty \) in equation (3.66) and using conditions \((i)-(ii) \), we get
From equations (3.35), (3.38), and (3.49), we get
Taking limit \(n \to \infty \) in equation (3.68) and using equation (3.67) and using condition (ii), we get
From equation (3.2), we have
Similarly,
Taking limit \(n \to \infty \) in equations (3.70) and (3.71), using equation (3.69) and \(\lim_{n\rightarrow \infty}\sigma _{n}=0\), we get
From equation (3.2), we have
Similarly,
Taking limit \(n \to \infty \) in equation (3.73) and (3.74), then using equations (3.59) and (3.62), we get
Also,
Similarly,
Since \(\tau _{n}=o(\sigma _{n})\) and \(\lim_{n\rightarrow \infty}\sigma _{n}=0\), we have
Using equations (3.72), (3.75), and (3.78), we get
Moreover,
By equations (3.65), (3.72), and (3.78), we get
As A and B are bounded linear operators, therefore, \(Ax_{n_{q}}\rightharpoonup Ax^{*}\) and \(By_{n_{q}}\rightharpoonup By^{*}\). Clearly, it follows from weak semicontinuity of norm that
This implies \(Ax^{*}=By^{*}\). Subsequently, \((x^{*},y^{*})\in \Gamma \). Fix an index \(k_{1} \in \{1,2,\ldots, N\}\). As the pool of index is finite and \(\lim_{n\rightarrow \infty} \|x_{n}-x_{n-1}\|=0\), from Lemma 2.6, one can find a subsequence \(\{x_{m_{q}}\}\) of \(\{x_{n}\}\) such that \(x_{m_{q}}\rightharpoonup x^{*}\) and \(m_{q}(\mod N)+1=k_{1}\). Using equations (3.72) and (3.78), we get \(h_{m_{q}}\rightharpoonup x^{*}\). Additionally,
and \(I-S_{k_{1}}\) is demiclosed at zero for each \(k_{1}\in \{1,2,\ldots, N\}\), which implies \(x^{*}\in \bigcap_{k_{1}=1}^{N} \operatorname{Fix}(S_{k_{1}})\). Similarly,
and \(I-U_{k_{2}}\) is demiclosed at zero for each \(k_{2}\in \{1,2,\ldots, P\}\), which implies \(y^{*}\in \bigcap_{k_{2}=1}^{P} \operatorname{Fix}(U_{k_{2}})\). As \(\{x_{n}\}\) is bounded, take a subsequence \(\{x_{n_{q}}\}\) of \(\{x_{n}\}\) such that \(x_{n_{q}} \rightharpoonup x^{*} \in H_{1} \). Additionally, from the boundedness of \(\{h_{n}\}\), \(\{w_{n}\}\) and from equations (3.72) and (3.78), there are subsequences \(\{h_{n_{q}}\}\), \(\{w_{n_{q}}\}\) respectively such that \(h_{n_{q}}\rightharpoonup x^{*} \) and \(w_{n_{q}}\rightharpoonup x^{*} \). Suppose that \(x^{*}\notin \bigcap_{j=1}^{M} \operatorname{VIP}(Q_{1}, L_{j}^{x})\). Then, from Lemmas (2.10) and (2.11), we have \(x^{*}\notin \operatorname{Fix}(P_{Q_{1}}(I-\nu _{x}\sum_{j=1}^{M} b_{j}^{x}L_{j}^{x}))\). Using Opial’s condition and equation (3.69), we estimate
which is a contradiction. Thus \(x^{*}\in \operatorname{Fix}(P_{Q_{1}}(I-\nu _{x}\sum_{j=1}^{M} b_{j}^{x}L_{j}^{x}))\). Similarly, \(\{y_{n}\}\) is bounded, take a subsequence \(\{y_{n_{q}}\}\) of \(\{y_{n}\}\) such that \(y_{n_{q}} \rightharpoonup y^{*} \in H_{2} \). Additionally, from the boundedness of \(\{v_{n}\}\), \(\{t_{n}\}\) and from equations (3.72) and (3.78), there are subsequences \(\{v_{n_{q}}\}\) \(\{t_{n_{q}}\}\) respectively such that \(v_{n_{q}}\rightharpoonup y^{*} \) and \(t_{n_{q}}\rightharpoonup y^{*} \). Suppose that \(y^{*}\notin \bigcap_{j=1}^{M} \operatorname{VIP}(Q_{2}, L_{j}^{y})\). Then, from Lemmas (2.10) and (2.11), we have \(y^{*}\notin \operatorname{Fix}(P_{Q_{2}}(I-\nu _{y}\sum_{j=1}^{M} b_{j}^{y}L_{j}^{y}))\). Using Opial’s condition and equation (3.69), we estimate
which is a contradiction. Thus \(y^{*}\in \operatorname{Fix}(P_{Q_{2}}(I-\nu _{y}\sum_{j=1}^{M} b_{j}^{y}L_{j}^{y}))\). Using equation (3.59), we get
Now, using Lemma 3.3, \(l^{*}\in \Omega \). As \(w_{n_{q}}\rightharpoonup x^{*} \) and \(t_{n_{q}}\rightharpoonup y^{*} \), from equation (3.69), we estimate \(z_{n_{q}}^{1}\rightharpoonup x^{*} \) and \(z_{n_{q}}^{2}\rightharpoonup y^{*} \). Using Lemma 2.4, we get
As \(\lim_{n\rightarrow \infty}\sigma _{n}=0\) and \(\lim_{n\rightarrow \infty}\frac{\tau _{n}}{\sigma _{n}}=0\), using equation (3.88), we get \(\limsup_{n\to \infty} \bar{s}_{n}\le 0 \), and using Lemma 2.8, we have
which implies \(\lim_{n\rightarrow \infty}\|x_{n}-\bar{r}^{*}\|^{2}=\lim_{n \rightarrow \infty}\|y_{n}-\bar{s}^{*}\|^{2}=0\). Thus \(x_{n}\to \bar{r}^{*}\) and \(y_{n}\to \bar{s}^{*}\).
Case 2. Suppose that there is a subsequence \(\{\bar{p}_{n_{q}}\}\) of \(\{\bar{p}_{n}\}\) such that
Hence, by Lemma 2.7, there is a nondecreasing sequence of natural numbers \(\{n_{l}\}\subset \mathbb{N}\) such that \(n_{l}\rightarrow \infty \) as \(l\rightarrow \infty \), we have
and
This with equation (3.63) gives
Taking limit \(l \to \infty \), using \(\lim_{l\to \infty}\rho _{n_{l}}=0\) and \(\epsilon _{n_{l}}=o(\rho _{n_{l}})\), we get
Similarly,
Taking limit \(l\to \infty \), using \(\lim_{l\to \infty}\rho _{n_{l}}=0\), condition (i), and \(\epsilon _{n_{l}}=o(\rho _{n_{l}})\), we get
Similarly, we can prove
Using the same justification as in Case 1, we can prove
Again, following the same steps as in Case 1, we obtain
Additionally, from equation (3.56), we obtain
which implies
As \(d_{n_{l}}>0\), we have \(\bar{p}_{{n_{l}}}\le \bar{s}_{n_{l}}\). Subsequently,
As \(\lim_{l\rightarrow \infty}\sigma _{n_{l}}=0\), \(\lim_{l\rightarrow \infty}\frac{\epsilon _{n_{l}}}{\sigma _{n_{l}}}= 0\) and from equation (3.101), we obtain \(\bar{p}_{{n_{l}}}\to 0\) as \(l\to \infty \). Also using equation (3.102), we get \(\bar{p}_{n_{l}+1}\rightarrow 0\) as \(l \rightarrow \infty \). Subsequently, from equation (3.90), we get \(\bar{p}_{n_{l}+1}\ge \bar{p}_{l} \). This implies \(\lim_{l\rightarrow \infty}\bar{p}_{l}=0\) i.e. \(\|x_{l}-x^{*}\|^{2}+\|y_{l}-y^{*}\|^{2}\rightarrow 0\) as \(l \rightarrow \infty \), which implies \(x_{l}\rightarrow \bar{r}^{*}\) and \(y_{l}\rightarrow \bar{s}^{*}\). □
Remark 3.5
-
(i)
Izuchukwu et al. [55] established a strong convergence theorem for finding a common solution to two types of split equality problems, namely fixed point problem and finite families of variational inequalities problems. In this paper we proved a strong convergence theorem for finding a common solution to three types of split equality problems, namely infinite families of variational inclusion problems, finite families of variational inequalities, and a multiple-sets fixed point problem. Hence, our result is more desirable than Izuchukwu et al. [55] results.
-
(ii)
Theorem 3.4 generalizes the findings of Kazmi et al. [56] from the common solution of a multiple-sets split equality fixed point problem and a split equality variational inequality problem to a infinite family of split equality variational inclusion problems, a multiple-sets split equality fixed point problem, and a finite family of split equality variational inequality problems.
-
(iii)
Theorem 3.4 improves and generalizes the result of Guo et al. [57] from the common solution of a split equality variational inclusion problem and a finite family of fixed point problems to an infinite family of split equality variational inclusion problems, a finite family of split equality variational inequality problems, and a multiple-sets split equality fixed point problem. Further, our result generalize the Guo et al. [57] result from nonexpansive mapping to more generalized demicontractive mappings.
4 Applications
Intensity-modulated radiation therapy
Intensity-modulated radiation therapy (IMRT) is a cutting-edge radiotherapy technology that treats cancer while minimizing the amount of normal tissue exposed to radiation in the treatment area. IMRT has recently garnered much attention, as evidenced by [15, 58]. We often utilize an optimization technique to minimize the objective function of radiation beam weights, and radiations with varying intensities are transmitted into the body of patients while building IMRT.
The primary goal of IMRT is to deliver a sufficient dosage to the areas that require radiation therapy, known as planned target volumes (PTV), while limiting the amount given to other areas, known as organs at risk (OAR). To achieve a clinically appropriate dose distribution, we primarily evaluate the radiation dosage absorbed by irradiated tissue based on the distribution of beamlet intensities and the radiation intensity given by all beamlets when planning IMRT. Furthermore, we consider the irradiated object’s physical and biological features. While the restrictions on the deliverable radiation intensities of the beamlets are represented in the intensity space, the space whose elements are the deliverable radiation intensities of the beamlets, and the limits on the dose received by each voxel of the body are represented in the dose space. The intensity space and the dosage space are Euclidean spaces for vectors.
We employ volumetric modulated arc therapy (VMAT) and study two external sources/treatment equipment, the Varian and the Elekta. Here, we compare one dose space and two intensity spaces. We split the entire patient volume into J-voxels \(j = 1,2,\ldots,J\), beam into K-beamlets, \(k = 1,2,\ldots,K\). Assume that \(a_{jk}\ge 0\) is the dose absorbed at jth as a result of unit intensity from kth beamlet, \(x_{k}\ge 0\) is the intensity of k beamlet, and \(c_{j}\) denotes the dose absorbed by jth voxel given as
We represent A as a dose influence matrix with nonnegative entries \(a_{jk}\). In the dose space, we will set a lower bound on the dose delivered to PTV and an upper bound on the dose delivered to OAR. Suppose that \(A_{s}\) denotes anatomical structures that are \(Q+R\) in number, where \(A_{s}\) are PTVs for \(s = 1,2,\ldots,Q\) and \(A_{s}\) are OARs for \(s= Q+1,Q+2,\ldots,R\). Let \(l_{s}\) and \(u_{s}\) be lower and upper bound doses for jth voxel, and some upper bound constraints are given as
the lower bound constraint
the equivalent uniform dose (EUD) constraint \(U_{s}:R^{J} \to R\) given as
Assume that A and B are influenced matrices, \(X_{+}\) and \(Y_{+}\) are nonnegative constraints, \(X_{k_{1}}\), \({k_{1}} = 1,2,\ldots, N\), and \(Y_{k_{2}}, {k_{2}} = 1,2,\ldots, P\), are the beamlet intensity vectors that satisfy the nth constraint for Varian (V) and Elekta (E) machine respectively. Consider the multiple-sets split equality problem of finding
Thus, the main objective of IMRT is to find an intensity r̄ that is closest to the entire radiation intensity space in the Varian machine and an intensity s̄ that is closest to the entire radiation intensity space in the Elekta machine, these both machines transmit an equal dose to the tumor. As we know that \(\bar{r^{*}}\in \operatorname{Fix}(P_{Q})\) if and only if \(x \in Q\), where \(P_{Q}\) is the metric projection defined on a closed convex subset Q of Hilbert space H.
Take \(S_{k_{1}}=P_{X_{+}\cap X_{j}}\) (\({k_{1}}=1,2,\ldots,N\)) and \(U_{k_{2}}=P_{Y_{+}\cap Y_{k}}\) (\({k_{2}}=1,2,\ldots,P\)) in Algorithm 3.1. For the multiple-sets split equality problem, we obtain the following cyclic iterative algorithm.
Algorithm 4.1
Consider \(0< a<\nu ^{x}_{n}\le 2\bar{c}^{x}\), \(0< b<\nu ^{y}_{n}\le 2\bar{c}^{y}\) for some \(a,b\in \mathbb{R} \), \(b_{j}^{x}, b_{j}^{y}\in (0,1)\) for \(j=1,2,\ldots,M\), \(\{\delta _{n}\}, \{\mu _{n}\}, \{\eta _{n,i}\}, \{\sigma _{n}\} \subset [\bar{d},\bar{e}]\subset (0,1)\), \(a_{n} \in (\delta ,1-\phi -\delta )\), \(\beta \in (0,1)\), \(\sum_{n=1}^{\infty}\tau _{n}<\infty \), and \(\{\kappa _{n}\}\subset [\kappa , \bar{\kappa}] \), where \(0<\kappa \le \bar{\kappa}\), \(\delta >0\), d̄, and \(\bar{e} \in \mathbb{R}\). Choose \(x_{0}, x_{1} \in Q\) and \(\gamma _{n}\) such that \(0 \le \gamma _{n}\le \bar{\gamma _{n}}\), where
Compute
where the stepsize \(\rho _{n}\) is given as
As we know, metric projection is firmly nonexpansive, and thus 0-demicontractive. As a result, using Algorithm 4.1 and the proof described in Theorem 3.4, we can obtain the strong convergence result for approximating the solution of the multiple-sets split equality problem (4.1).
It is not always possible to find an intensity that meets all of the constraints. In this case, we find a solution that is as close to all of the constraints as possible. To do so, we consider the following proximity function:
The multiple-sets split equality problem (4.1), on the other hand, can be written as the minimization problem
5 Numerical example
In this section, we give a numerical example to compare the convergence of the algorithms given in [32, 37] to Algorithms 3.1.
Example 5.1
Let \(H_{1}=H_{2}=H_{3}=\mathbb{R}^{4}\) be the set of real numbers. Assume that
Let \(A, B:\mathbb{R}^{4}\rightarrow \mathbb{R}^{4}\) be two bounded linear operators created from a normal distribution with unit variance and mean zero. Consider \(\psi _{1}:\mathbb{R}^{4}\rightarrow \mathbb{R}^{4}\) and \(\psi _{2}:\mathbb{R}^{4}\rightarrow \mathbb{R}^{4}\) as two contraction mappings such that \(\psi _{1}(x)=\frac{x}{24}\) and \(\psi _{2}(x)=\frac{x}{24}\) for all \(x\in \mathbb{R}^{4}\). Further, we take two demicontractive mappings \(S_{k_{1}}\) and \(U_{k_{1}}:\mathbb{R}^{4}\rightarrow \mathbb{R}^{4}\) such that \(S_{k_{1}}(x)=\frac{-3{k_{1}}}{{k_{1}}+1}x\), \(U_{k_{2}}(x)= \frac{-2{k_{2}}}{{k_{2}}+1}\), where \(k_{1}=k_{2}=1\), \(x=(x_{1}, x_{2}, x_{3}, x_{4})^{T}\). Take \(i=1\), and \({K_{1}}:H_{1} \to 2^{H_{1}}\) and \({T_{1}}:H_{2} \to 2^{H_{2}}\) are defined as \(K_{1}(x)=A^{*}A(x)\) and \(T_{1}(x)=B^{*}B(x)\). Take \(J=20\). Let \(L_{j}^{x}\) and \(L_{j}^{y}\) be defined by \(L_{j}^{y}(\bar{t})=\frac{\bar{t}+2}{5j}\) and \(L_{j}^{x}(\bar{t})=\frac{\bar{t}-1}{3j}\) with \(b_{j}^{x}=\frac{2}{3^{j}}+\frac{1}{N3^{N}}\), \(b_{j}^{y}=\frac{4}{5^{j}}+\frac{1}{N5^{N}}\) and \(\nu _{j}^{x}=\frac{1}{7j^{3}}\), \(\nu _{j}^{y}=\frac{1}{5j^{3}}\). Obviously, the solution set Γ is nonempty as \(0\in \Gamma \). Let \(\delta _{n}=\frac{1}{n+1}\), \(\mu _{n}=\frac{1+n}{3n}\), \(\eta _{n,1}= \frac{2n^{2}-2n-1}{3n (n+1)}\), \(\sigma _{n}=\frac{1}{2n+1}\), \(a_{n}=\frac{1}{n+1}\), and the step size \(\tau _{n}=\frac{1}{n^{5}}\). Choose \(\beta =0.5\) and initial values \(x=(0.1,0.1, 0.1, 0.1)^{T}\), \(y=(0.2, 0.2, 0.2, 0.2)^{T}\). We set \(E_{n} =\|x_{n}-x_{n-1}\|+\|y_{n}-y_{n-1}\|< 10^{-4}\) as a stopping criterion and plot the graphs of errors \(E_{n}\) over the number of iterations n. Furthermore, we provide numerical data for various values of \(\eta _{n}\) and the following values of \(x_{0}\) and \(x_{1}\).
Case 1: \(x_{0}=(0.01,0.01, 0.01, 0.01)\), \(x_{1}=(0.02,0.02, 0.02, 0.02)\);
Case 2: \(x_{0}=(0.1,0.1, 0.1, 0.1)\), \(x_{1}=(0.2,0.2, 0.2, 0.2)\);
Case 3: \(x_{0}=(1, 1, 1, 1)\), \(x_{1}=(2, 2, 2, 2)\).
Tables 1–3 and Figs. 1–3 present the numerical results.
Numerical analysis of Alg 3.1 for different cases
Numerical analysis of Alg 3.1 for different values of \(\rho _{1}\)
6 Conclusion
In this paper, we have proposed a new inertial simultaneous cyclic iterative algorithm with a method for a finite family of split equality variational inequality problems, an infinite family of split equality variational inclusion problems, and a multiple-sets split equality fixed point problem for demicontractive operators in infinite dimensional Hilbert spaces. The proposed algorithm includes several well-known methods such as the cyclic method, the inertial method, the viscosity approximation method, and the projection method. We prove strong convergence of the proposed algorithm. This result extends and unifies various known results in the literature. Finally, we give a numerical example to justify the main convergence theorem.
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References
Stampacchia, G.: Formes bilineaires coercivites sur les ensembles convexes. C. R. Acad. Sci. Paris 258, 4413–4416 (1964)
Balooee, J., Chang, S.S., Yao, J.C.: Generalized set-valued nonlinear variational like inequalities and fixed point problems: existence and approximation solvability results. J. Optim. Theory Appl. 197, 891–938 (2023)
Dong, Q.L., Liu, L., Yao, Y.: Self-adaptive projection and contraction methods with alternated inertial terms for solving the split feasibility problem. J. Nonlinear Convex Anal. 23(3), 591–605 (2022)
Dong, Q.L., Peng, Y., Yao, Y.: Alternated inertial projection methods for the split equality problem. J. Nonlinear Convex Anal. 22, 53–67 (2021)
Godwin, E.C., Alakoya, T.O., Mewomo, O.T., Yao, J.C.: Relaxed inertial Tseng extragradient method for variational inequality and fixed point problems. Appl. Anal. (2023). https://doi.org/10.1080/00036811.2022.2107913
Yao, Y., Shehu, Y., Li, X., Dong, Q.L.: A method with inertial extrapolation step for split monotone inclusion problems. Optimization 70, 741–761 (2021)
Lions, J.L., Stampacchia, G.: Variational inequalities. Commun. Pure Appl. Math. 20, 493–519 (1967)
Lin, T.C.: Approximation theorems and fixed point theorems in cones. Proc. Am. Math. Soc. 102(2), 502–506 (1988)
Yao, Y., Chen, R., Xu, H.K.: Schemes for finding minimum-norm solutions of variational inequalities. Nonlinear Anal. 72, 3447–3456 (2010)
Censor, Y., Elfving, T.: A multiprojection algorithm using Bregman projections in a product space. Numer. Algorithms 8, 221–239 (1994)
Byrne, C.: A unified treatment of some iterative algorithms in signal processing and image reconstruction. Inverse Probl. 20, 103–120 (2004)
Censor, Y., Elfving, T., Kopf, N., Bortfeld, T.: The multiple-sets split feasibility problem and its applications for inverse problems. Inverse Probl. 21, 2071–2084 (2005)
He, H., Ling, C., Xu, H.K.: An implementable splitting algorithm for the \(\ell _{1} \)-norm regularized split feasibility problem. J. Sci. Comput. 67, 281–298 (2016)
Lorenz, D.A., Shöpfer, F., Wenger, S.: The linearized Bregman method via split feasibility problems: analysis and generalizations. SIAM J. Imaging Sci. 7, 1237–1262 (2014)
Censor, Y., Bortfeld, T., Martin, B., Trofimov, A.: A unified approach for inversion problems in intensity-modulated radiation therapy. Phys. Med. Biol. 2006(51), 2353–2365 (2006)
Censor, Y., Segal, A.: The split common fixed point problem for directed operators. J. Convex Anal. 16, 587–600 (2009)
Batra, C., Gupta, N., Chugh, R., Kumar, R.: Generalized viscosity extragradient algorithm for pseudomonotone equilibrium and fixed point problems for finite family of demicontractive operators. J. Appl. Math. Comput. 1–28 (2022)
Chugh, R., Batra, C., Biban, G., Ahuja, A.: New four-step iteration process for approximating fixed point of contraction mappings. Math. Eng. Sci. 13(4) (2022)
Chugh, R., Kumar, R., Batra, C.: Variational inequality problem with application to convex minimization problem. Math. Eng. Sci. Aerosp. 14(1) (2023)
Nandal, A., Chugh, R.: On zeros of accretive operators with application to the convex feasibility problem. UPB Sci. Bull., Ser. A, Appl. Math. Phys. 81, 95–106 (2019)
Hussain, N., Nandal, A., Kumar, V., Chugh, R.: Multistep generalized viscosity iterative algorithm for solving convex feasibility problems in Banach spaces. J. Nonlinear Convex Anal. 21, 587–603 (2020)
Nandal, A., Chugh, R., Kumari, S.: Convergence analysis of algorithms for variational inequalities involving strictly pseudocontractive operators. Poincare J. Anal. Appl. 2019, 123–136 (2019)
Gupta, N., Postolache, M., Nandal, A., Chugh, R.: A cyclic iterative algorithm for multiple-sets split common fixed point problem of demicontractive mappings without prior knowledge of operator norm. Mathematics 9, 372 (2021)
Moudafi, A.: A relaxed alternating CQ-algorithm for convex feasibility problems. Nonlinear Anal., Theory Methods Appl. 79, 117–121 (2013)
Moudafi, A.: Alternating CQ-algorithm for convex feasibility and split fixed-point problems. J. Nonlinear Convex Anal. 154, 809–818 (2014)
Moudafi, A., Al-Shemas, E.: Simultaneous iterative methods for split equality problem. Trans. Math. Program. Appl. 1, 1–11 (2013)
López, G., MartÃn-Márquez, V., Wang, F., Xu, H.K.: Solving the split feasibility problem without prior knowledge of matrix norms. Inverse Probl. 28, 085004 (2012)
Zhao, J.: Solving split equality fixed-point problem of quasi-nonexpansive mappings without prior knowledge of operators norms. Optimization 64, 2619–2630 (2015)
Shehu, Y., Ogbuisi, F.U., Iyiola, O.S.: Strong convergence theorem for solving split equality fixed point problem which does not involve the prior knowledge of operator norms. Bull. Iran. Math. Soc. 43, 349–371 (2017)
Byrne, C.: Iterative oblique projection onto convex sets and the split feasibility problem. Inverse Probl. 18, 441–453 (2002)
Censor, Y., Gibali, A., Reich, S., Sabach, S.: Common solutions to variational inequalities. Set-Valued Var. Anal. 20, 229–247 (2012)
Eslamian, M., Fakhri, A.: Split equality monotone variational inclusions and fixed point problem of set-valued operator. Acta Univ. Sapientiae Math. 9, 94–121 (2017)
Moudafi, A.: Split monotone variational inclusions. J. Optim. Theory Appl. 150, 275–283 (2011)
Chang, S.S., Yao, J.C., Wen, C.F., Yang, L., Qin, L.J.: Common zero for a finite family of monotone mappings in Hadamard spaces with applications. Mediterr. J. Math. 15, 160 (2018)
Wu, Y., Chen, V., Shi, L.Y.: Split equality problem and multiple-sets split equality problem for quasinonexpansive multivalued mappings. J. Inequal. Appl. 428 (2014)
Zhao, J., Zong, H.: Solving the multiple-set split equality common fixed-point problem of firmly quasi-nonexpansive operators. J. Inequal. Appl. 2018(1), 83 (2018)
Chang, S.S., Wang, L., Tang, Y.K., Wang, G.: Moudafi’s open question and simultaneous iterative algorithm for general split equality variational inclusion problems and general split equality optimization problems. Fixed Point Theory Appl. 2014(1), 215 (2014)
Latif, A., Eslamian, M.: Split equality problem with equilibrium problem, variational inequality problem, and fixed point problem of nonexpansive semigroups. J. Nonlinear Sci. Appl. 10, 3217–3230 (2017)
Kazmi, K.R., Ali, R., Furkan, M.: Common solution to a split equality monotone variational inclusion problem, a split equality generalized general variational-like inequality problem and a split equality fixed point problem. Fixed Point Theory 20(1), 211–232 (2019)
Alvarez, F., Attouch, H.: An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping. Set-Valued Anal. 9, 3–11 (2001)
Dong, Q.L., Yuan, H.B., Cho, Y.J., Rassias, T.M.: Modified inertial Mann algorithm and inertial CQ-algorithm for nonexpansive mappings. Optim. Lett. 12(1), 87–102 (2018)
Dang, Y., Sun, J., Xu, H.: Inertial accelerated algorithms for solving a split feasibility problem. J. Ind. Manag. Optim. 13, 1383–1394 (2017)
Hanjing, A., Suantai, S.: Hybrid inertial accelerated algorithms for split fixed point problems of demicontractive mappings and equilibrium problems. Numer. Algorithms 85, 1051–1073 (2020)
Wang, Y., Kim, T.H., Fang, X.: Weak and strong convergence theorems for the multiple-set split equality common fixed-point problems of demicontractive mappings. J. Funct. Spaces 2017 (2017)
Dinh, B.V.: Projection algorithms for solving nonmonotone equilibrium problems in Hilbert space. J. Comput. Appl. 302, 106–117 (2016)
Cui, H., Wang, F.: Iterative methods for the split common fixed point problem in Hilbert spaces. Fixed Point Theory Appl. 2014, 78 (2014)
Wang, J., Wang, Y.: Strong convergence of a cyclic iterative algorithm for split common fixed-point problems of demicontractive mappings. J. Nonlinear Var. Anal. 2, 295–303 (2018)
Maingé, P.E.: Strong convergence of projected subgradient methods for nonsmooth and nonstrictly convex minimization. Set-Valued Anal. 16, 899–912 (2008)
Xu, H.K.: Iterative algorithms for nonlinear operators. J. Lond. Math. Soc. 66, 240–256 (2002)
Chuang, C.S.: Strong convergence theorems for the split variational inclusion problem in Hilbert spaces. Fixed Point Theory Appl. 350 (2013)
Takahashi, W.: Introduction to Nonlinear and Convex Analysis. Yokohama Publishers, Yokohama (2009)
Sriprad, W., Srisawat, S.: An iterative method for solving split monotone variational inclusion problems and finite family of variational inequality problems in Hilbert spaces. Int. J. Math. Math. Sci. 2021 (2021)
Marino, G., Xu, H.K.: Weak and strong convergence theorems for strict pseudo-contractions in Hilbert spaces. J. Math. Anal. Appl. 329, 336–346 (2007)
Zhao, J., He, S.: Solving the general split common fixed-point problem of quasi-nonexpansive mappings without prior knowledge of operator norms. Filomat 31, 559–573 (2017)
Izuchukwu, C., Okeke, C.C., Mewomo, O.T.: Systems of variational inequalities and multiple-set split equality fixed-point problems for countable families of multivalued type-one mappings of the demicontractive type. Ukr. Math. J. 71(11), 1692–1718 (2020)
Kazmi, K.R., Furkan, M., Ali, R.: Simultaneous extragradient iterative method to a split equality variational inequality problem and a multiple-sets split equality fixed point problem for multi-valued demicontractive mappings. Adv. Fixed Point Theory 9(2), 111–134 (2019)
Guo, H., He, H., Chen, R.: Strong convergence theorems for the split equality variational inclusion problem and fixed point problem in Hilbert spaces. Fixed Point Theory Appl. 2015(1), 223 (2015)
Yao, Y., Chen, R., Marino, G., Liou, Y.C.: Applications of fixed-point and optimization methods to the multiple set split feasibility problem. J. Appl. Math. 2012 (2012)
Acknowledgements
The authors extend their appreciation to the Arab Open University, Saudi Arabia, for funding this work through AOU research fund No. (AOURG-2023-009).
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C.B., R.C., R.K., K.S., S.A. and N.M. wrote the main manuscript text and prepared Figs. 1-3. All authors reviewed the manuscript.
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Batra, C., Chugh, R., Kumar, R. et al. Strong convergence of split equality variational inequality, variational inclusion, and multiple sets fixed point problems in Hilbert spaces with application. J Inequal Appl 2024, 40 (2024). https://doi.org/10.1186/s13660-024-03118-0
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DOI: https://doi.org/10.1186/s13660-024-03118-0