
This article proposes an iteration algorithm with double inertial extrapolation steps for approximating a common solution of split equilibrium problem, fixed point problem and variational inequity problem in the framework of Hilbert spaces. Unlike several existing methods, our algorithm is designed such that its implementation does not require the knowledge of the norm of the bounded linear operator and the value of the Lipschitz constant. The proposed algorithm does not depend on any line search rule. The method uses a self-adaptive step size which is allowed to increase from iteration to iteration. Furthermore, using some mild assumptions, we establish a strong convergence theorem for the proposed algorithm. Lastly, we present a numerical experiment to show the efficiency and the applicability of our proposed iterative method in comparison with some well-known methods in the literature. Our results unify, extend and generalize so many results in the literature from the setting of the solution set of one problem to the more general setting common solution set of three problems.
Citation: James Abah Ugboh, Joseph Oboyi, Hossam A. Nabwey, Christiana Friday Igiri, Francis Akutsah, Ojen Kumar Narain. Double inertial extrapolations method for solving split generalized equilibrium, fixed point and variational inequity problems[J]. AIMS Mathematics, 2024, 9(4): 10416-10445. doi: 10.3934/math.2024509
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This article proposes an iteration algorithm with double inertial extrapolation steps for approximating a common solution of split equilibrium problem, fixed point problem and variational inequity problem in the framework of Hilbert spaces. Unlike several existing methods, our algorithm is designed such that its implementation does not require the knowledge of the norm of the bounded linear operator and the value of the Lipschitz constant. The proposed algorithm does not depend on any line search rule. The method uses a self-adaptive step size which is allowed to increase from iteration to iteration. Furthermore, using some mild assumptions, we establish a strong convergence theorem for the proposed algorithm. Lastly, we present a numerical experiment to show the efficiency and the applicability of our proposed iterative method in comparison with some well-known methods in the literature. Our results unify, extend and generalize so many results in the literature from the setting of the solution set of one problem to the more general setting common solution set of three problems.
Hydraulic jumps are frequently observed in lab studies, river flows, and coastal environments. The shallow water equations, commonly known as Saint-Venant equations, have important applications in oceanography and hydraulics. These equations are used to represent fluid flow when the depth of the fluid is minor in comparison to the horizontal scale of the flow field fluctuations [1]. They have been used to simulate flow at the atmospheric and ocean scales, and they have been used to forecast tsunamis, storm surges, and flow around constructions, among other things [2,3]. Shallow water is a thin layer of constant density fluid in hydrostatic equilibrium that is bordered on the bottom by a rigid surface and on the top by a free surface [4].
Nonlinear waves have recently acquired importance due to their capacity to highlight several complicated phenomena in nonlinear research with spirited applications [5,6,7,8]. Teshukov in [9] developed the equation system that describes multi-dimensional shear shallow water (SSW) flows. Some novel solitary wave solutions for the ill-posed Boussinesq dynamic wave model under shallow water beneath gravity were constructed [10]. The effects of vertical shear, which are disregarded in the traditional shallow water model, are included in this system to approximate shallow water flows. It is a non-linear hyperbolic partial differential equations (PDE) system with non-conservative products. Shocks, rarefactions, shear, and contact waves are all allowed in this model. The SSW model can capture the oscillatory character of turbulent hydraulic leaps, which corrects the conventional non-linear shallow water equations' inability to represent such occurrences [11]. Analytical and numerical techniques for solving such problems have recently advanced; for details, see [12,13,14,15,16,17] and the references therein.
The SSW model consists of six non-conservative hyperbolic equations, which makes its numerical solution challenging since the concept of a weak solution necessitates selecting a path that is often unknown. The physical regularization mechanism determines the suitable path, and even if one knows the correct path, it is challenging to construct a numerical scheme that converges to the weak solution since the solution is susceptible to numerical viscosity [18].
In the present work, we constructed the generalized Rusanov (G. Rusanov) scheme to solve the SSW model in 1D of space. This technique is divided into steps for predictors and correctors [19,20,21,22,23]. The first one includes a numerical diffusion control parameter. In the second stage, the balance conservation equation is retrieved. Riemann solutions were employed to determine the numerical flow in the majority of the typical schemes. Unlike previous schemes, the interesting feature of the G. Rusanov scheme is that it can evaluate the numerical flow even when the Riemann solution is not present, which is a very fascinating advantage. In actuality, this approach may be applied as a box solver for a variety of non-conservative law models.
The remainder of the article is structured as follows. Section 2 offers the non-conservative shear shallow water model. Section 3 presents the structure of the G. Rusanov scheme to solve the 1D SSW model. Section 4 shows that the G. Rusanov satisfies the C-property. Section 5 provides several test cases to show the validation of the G. Rusanov scheme versus Rusanov, Lax-Friedrichs, and reference solutions. Section 6 summarizes the work and offers some conclusions.
The one-dimensional SSW model without a source term is
∂W∂t+∂F(W)∂x+K(W)∂h∂x=S(W), | (2.1) |
W=(hhv1hv2E11E12E22),F(W)=(hv1R11+hv21+gh22R12+hv1v2(E11+R11)v1E12v1+12(R11v2+R12v1)E22v1+R12v2), |
K(W)=(000ghv112ghv20),S(W)=(0−gh∂B∂x−Cf|v|v1−Cf|v|v2−ghv1∂B∂x+12D11−Cf|v|v21−12ghv2∂B∂x+12D12−Cf|v|v1v212D22−Cf|v|v22), |
while R11 and R12 are defined by the following tensor Rij=hpij, and also, E11, E12 and E22 are defined by the following tensor Eij=12Rij+12hvivj with i≥1,j≤2. We write the previous system 2.1 in nonconservative form with non-dissipative (Cf=0,D11=D12=D22=0), see [24], which can be written as follows
∂W∂t+(∇F(W)+C(W))∂W∂x=S1(W). | (2.2) |
Also, we can write the previous system as the following
∂W∂t+A(W)∂W∂x=S1(W) | (2.3) |
with
A(W)=[010000gh00200000020−3E11v1h+2v31+ghv23E11h−3v2103v100−2E12v1h−E11v2h+2v21v2+ghv222E12h−2v1v2E11h−v21v22v10−E22v1h−2E12v2h+v22v1E22h+v222E12h−2v2v102v2v1] |
and
S1(W)=(0−gh∂B∂x0−ghv1∂B∂x−12ghv2∂B∂x0). |
System (2.3) is a hyperbolic system and has the following eigenvalues
λ1=λ2=v1,λ3=v1−√2E11h−v21,λ4=v1+√2E11h−v21, |
λ5=v1−√gh+3(v21−2E11h),λ6=v1+√gh+3(v21−2E11h). |
In order deduce the G. Rusanov scheme, we rewrite the system (2.1) as follows
∂W∂t+∂F(W)∂x=−K(W)∂h∂x+S1(W)=Q(W). | (3.1) |
Integrating Eq (3.1) over the domain [tn,tn+1]×[xi−12,xi+12] gives the finite-volume scheme
Wn+1i=Wni−ΔtΔx(F(Wni+1/2)−F(Wni−1/2))+ΔtQin | (3.2) |
in the interval [xi−1/2,xi+1/2] at time tn. Wni represents the average value of the solution W as follows
Wni=1Δx∫xi+1/2xi−1/2W(tn,x)dx, |
and F(Wni±1/2) represent the numerical flux at time tn and space x=xi±1/2. It is necessary to solve the Riemann problem at xi+1/2 interfaces due to the organization of the numerical fluxes. Assume that for the first scenario given below, there is a self-similar Riemann problem solution associated with Eq (3.1)
W(x,0)={WL,ifx<0,WR,ifx>0 | (3.3) |
is supplied by
W(t,x)=Rs(xt,WL,WR). | (3.4) |
The difficulties with discretization of the source term in (3.2) could arise from singular values of the Riemann solution at the interfaces. In order to overcome these challenges and reconstruct a Wni+1/2 approximation, we created a finite-volume scheme in [19,20,21,22,23,25,26,27] for numerical solutions of conservation laws including source terms and without source terms. The principal objective here is building the intermediate states Wni±1/2 to be utilized in the corrector stage (3.2). The following is obtained by integrating Eq (3.1) through a control volume [tn,tn+θni+1/2]×[x−,x+], that includes the point (tn,xi+1/2), with the objective to accomplish this:
∫x+x−W(tn+θni+1/2,x)dx=Δx−Wni+Δx+Wni+1−θni+1/2(F(Wni+1)−F(Wni))+θni+1/2(Δx−−Δx+)Qni+12 | (3.5) |
with Wni±1/2 denoting the approximation of Riemann solution Rs across the control volume [x−,x+] at time tn+θni+1/2. While calculating distances Δx− and Δx+ as
Δx−=|x−−xi+1/2|,Δx+=|x+−xi+1/2|, |
Qni+12 closely resembles the average source term Q and is given by the following
Qni+12=1θni+12(Δx−+Δx+)∫tn+θni+12tn∫x+x−Q(W)dtdx. | (3.6) |
Selecting x−=xi, x+=xi+1 causes the Eq (3.5) to be reduced to the intermediate state, which given as follows
Wni+1/2=12(Wni+Wni+1)−θni+1/2Δx(F(Wni+1)−F(Wni))+θni+12Qni+12, | (3.7) |
where the approximate average value of the solution W in the control domain [tn,tn+θni+1/2]×[xi,xi+1] is expressed by Wni+1/2 as the following
Wni+1/2=1Δx∫xi+1xiW(x,tn+θni+1/2)dx | (3.8) |
by selecting θni+1/2 as follows
θni+1/2=αni+1/2ˉθi+1/2,ˉθi+1/2=Δx2Sni+1/2. | (3.9) |
This choice is contingent upon the results of the stability analysis[19]. The following represents the local Rusanov velocity
Sni+1/2=maxk=1,...,K(max(∣λnk,i∣,∣λnk,i+1∣)), | (3.10) |
where αni+1/2 is a positive parameter, and λnk,i represents the kth eigenvalues in (2.3) evaluated at the solution state Wni. Here, in our case, k=6 for the shear shallow water model. One can use the Lax-Wendroff technique again for αni+1/2=ΔtΔxSni+1/2. Choosing the slope αni+1/2=˜αni+1/2, the proposed scheme became a first-order scheme, where
˜αni+1/2=Sni+1/2sni+1/2, | (3.11) |
and
sni+1/2=mink=1,...,K(max(∣λnk,i∣,∣λnk,i+1∣)). | (3.12) |
In this case, the control parameter can be written as follows:
αni+1/2=˜αni+1/2+σni+1/2Φ(rmi+1/2), | (3.13) |
where ˜αni+1/2 is given by (3.11), and Φi+1/2=Φ(ri+1/2) is a function that limits the slope. While for
ri+1/2=Wi+1−q−Wi−qWi+1−Wi,q=sgn[F′(Xn+1,Wni+1/2)] |
and
σni+1/2=ΔtΔxSni+1/2−Sni+1/2sni+1/2. |
One may use any slope limiter function, including minmod, superbee, and Van leer [28] and [29]. At the end, the G. Rusanov scheme for Eq (2.3) can be written as follows
{Wni+12=12(Wni+Wni+1)−αni+122Snj+12[F(Wni+1)−F(Wni)]+αni+122Snj+12Qni+12,Wn+1i=Wni−rn[F(Wni+12)−F(Wni−12)]+ΔtnQin. | (3.14) |
With a definition of the C-property [30], the source term in (2.1) is discretized in the G. Rusanov method in a way that is well-balanced with the discretization of the flux gradients. According to [30,31], if the following formulas hold, a numerical scheme is said to achieve the C-property for the system (3.1).
(hp11)n= constant,hn+B= constantandvn1=vn2=0. | (4.1) |
When we set v1=v2=0 in the stationary flow at rest, we get system (3.1), which can be expressed as follows.
∂∂t(h0012hp1112hp1212hp22)+∂∂x(0hp11+12gh2hp12000)=(0−gh∂B∂x0000)=Q(x,t). | (4.2) |
We can express the predictor stage (3.14) as follows after applying the G. Rusanov scheme to the previous system
Wni+12=(12(hni+hni+1)−αni+124Sni+12(hni+hni+1)[(hni+1+Bi+1)−(hni+Bi)]−αni+122Sni+12((hp11)ni+1−(hp11)ni−αni+122Sni+12((hp12)ni+1−(hp12)ni)14((hp11)ni+1+(hp11)ni)14((hp12)ni+1+(hp12)ni)14((hp22)ni+1+(hp22)ni)). | (4.3) |
Also, we can write the previous equations as follows
Wni+12=(hni+120−αni+122Sni+12((hp12)ni+1−(hp12)ni)12(hp11)ni+1212(hp12)ni+1212(hp22)ni+12), | (4.4) |
and the stage of the corrector updates the solution to take on the desired form.
((h)n+1i(hv1)n+1i(hv2)n+1i(E11)n+1i(E12)n+1i(E22)n+1i)=((h)ni(hv1)ni(hv2)ni(E11)ni(E12)ni(E22)ni)−ΔtnΔx(0g2((hni+12)2−(hni−12)2)(hp12)ni+12−(hp12)ni−12000) | (4.5) |
+(0ΔtnQni0000). |
The solution is stationary when Wn+1i=Wni and this leads us to rewrite the previous system (4.5) as follows
Δtn2Δxg((hni+12)2−(hni−12)2)−ΔtnQni. |
This lead to the following
Qni=−g8Δx(hni+1+2hni+hni−1)(Bi+1−Bi−1). | (4.6) |
Afterward, when the source term in the corrector stage is discretized in the earlier equations, this leads to the G. Rusanov satisfying the C-property.
In order to simulate the SSW system numerically, we provide five test cases without a source term using the G. Rusanov, Rusanov, and Lax-Friedrichs schemes to illustrate the effectiveness and precision of the suggested G. Rusanov scheme. The computational domain for all cases is L=[0,1] divided into 300 gridpoints, and the final time is t=0.5s, except for test case 2, where the final time is t=10s. We evaluate the three schemes with the reference solution on the extremely fine mesh of 30000 cells that was produced by the traditional Rusanov scheme. Also, we provide one test case with a source term using the G. Rusanov scheme in the domain [0,1] divided into 200 gridpoints and final time t=0.5. We select the stability condition [19] in the following sense
Δt=CFLΔxmaxi(|αni+12Sni+12|), | (5.1) |
where a constant CFL=0.5.
This test case was studied in [24] and [32], and the initial condition is given by
(h,v1,v2,p11,p22,p12)={(0.01,0.1,0.2,0.04,0.04,1×10−8)ifx≤L2,(0.02,0.1,−0.2,0.04,0.04,1×10−8)ifx>L2. | (5.2) |
The solution include five waves, which are represented by 1− shock and 6− rarefaction. We compare the numerical results with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells. Figures 1–3 show the numerical results and variation of parameter of control. We note that all waves are captured by this scheme and the numerical solution agrees with the reference solution. Table 1 shows the CPU time of computation for the three schemes by using a Dell i5 laptop, CPU 2.5 GHZ.
Scheme | G. Rusanov | Rusanov | Lax-Friedrichs |
CPU time | 0.068005 | 0.056805 | 0.0496803 |
This test case (the shear waves problem) was studied in [24] and [32], and the initial condition is given by
(h,v1,v2,p11,p22,p12)={(0.01,0,0.2,1×10−4,1×10−4,0)ifx≤L2,(0.01,0,−0.2,1×10−4,1×10−4,0)ifx>L2. | (5.3) |
The solution is represented by two shear waves with only transverse velocity discontinuities, the p12 and p22 elements of a stress tensor. We compare the numerical results by G. Rusanov, Rusanov, and Lax-Friedrichs with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells and a final time t=10s, see Figures 4 and 5. We observe that Figure 5 displays a spurious rise in the central of the domain of computation, which has also been seen in the literature using different methods, see [24,32].
This test case was studied in [24] with the following initial condition
(h,v1,v2,p11,p22,p12)={(0.02,0,0,4×10−2,4×10−2,0)ifx≤L2,(0.01,0,0,4×10−2,4×10−2,0)ifx>L2. | (5.4) |
The solution for h, v1, and p11 consists of two shear rarefaction waves, one moving to the right and the other moving to the left, and a shock wave to the right. See Figures 6 and 7 which show the behavior of the height of the water, velocity, pressure, and variation of the parameter of control.
We take the same example, but with another variant of the modified test case, where p12=1×10−8 is set to a small non-zero value. We notice that the behavior of h, v1, and p11 is the same, but there is a change in the behavior of p12 such that it displays all of the five waves (four rarefaction and one shock) of the shear shallow water model, see Figures 8 and 9.
This test case was studied in [24] with the following initial condition
(h,v1,v2,p11,p22,p12)={(0.02,0,0,1×10−4,1×10−4,0)ifx≤L2,(0.03,−0.221698,0.0166167,1×10−4,1×10−4,0)ifx>L2. | (5.5) |
The solution for this test case consists of a single shock wave. We compare the numerical results with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells and a final time t=0.5s. Figures 10 and 11 show the numerical results, and we note that all waves are captured by this scheme and the numerical solution agrees with the reference solution, but the solution of p11 does not agree with the exact solution [24]. There is an oscillation with Lax-Friedrichs scheme in the water height h and the pressure p11.
This test case (the shear waves problem) was studied in [24] and [32] with the following initial condition
(h,v1,v2,p11,p22,p12)={(0.02,0,0,1×10−4,1×10−4,0)ifx≤L2,(0.01,0,0,1×10−4,1×10−4,0)ifx>L2. | (5.6) |
The solution of this test case has a single shock wave and a single rarefaction wave, separated by a contact discontinuity. We compare the numerical results with the reference solution, which is calculated with the Rusanov scheme with very fine mesh of 30000 cells and a final time t=0.5s. Figures 12 and 13 show the numerical results, and we note that all waves are captured by this scheme and the numerical solution agrees with the reference solution, but the solution of p11 does not agree with the reference solution [24].
In this test case, we take the previous test case 3 and we added the discontinuous fond topography with the following initial condition
(h,v1,v2,p11,p22,p12,B)={(0.02,0,0,4×10−2,4×10−2,1×10−8,0)ifx≤L2,(0.01,0,0,4×10−2,4×10−2,1×10−8,0.01)ifx>L2. | (5.7) |
We simulate this test case with the G. Rusanov scheme with 200 gridpoints on the domain L=[0,1] at the final time t=0.5s, and we note that the behavior of the solution for h consists of one shear rarefaction wave moving to the left, a contact discontinuity after that two shear rarefaction waves, one moving to the right and the other moving to the left (see the left side of Figure 14. The right side of the Figure 14 shows the behavior of the velocity v1, which it consists of a rarefaction wave, a contact discontinuity after that, and two shear rarefactions. The left side of Figure 14 displays the behavior of the velocity v1. It is composed of two shear rarefactions followed by a contact discontinuity and three rarefaction waves moving to the right. The the right side of Figure 14 displays the behavior of the velocity v2. It is composed of two shear rarefactions moving to the left followed by a contact discontinuity after that, and two rarefaction waves moving to the right.
In summary, we have reported five numerical examples to solve the SSW model. Namely, we implemented the G. Rusanov, Rusanov, and Lax-Friedrichs schemes. We also compared the numerical solutions with the reference solution obtained by the classical Rusanov scheme on the very fine mesh of 30000 gridpoints. The three schemes were capable of capturing shock waves and rarefaction. Also, we have given the last numerical test with a source term. Finally, we discovered that the G. Rusanov scheme was more precise than the Rusanov and Lax-Friedrichs schemes.
The current study is concerned with the SSW model, which is a higher order variant of the traditional shallow water model since it adds vertical shear effects. The model features a non-conservative structure, which makes numerical solutions problematic. The 1D SSW model was solved using the G. Rusanov technique. We clarify that this scheme satisfied the C-property. Several numerical examples were given to solve the SSW model using the G. Rusanov, Rusanov, Lax-Friedrichs, and reference solution techniques. The simulations given verified the G. Rusanov technique's high resolution and validated its capabilities and efficacy in dealing with such models. This approach may be expanded in a two-dimensional space.
H. S. Alayachi: Conceptualization, Data curation, Formal analysis, Writing - original draft; Mahmoud A. E. Abdelrahman: Conceptualization, Data curation, Formal analysis, Writing - original draft; K. Mohamed: Conceptualization, Software, Formal analysis, Writing - original draft.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 445-9-753.
The authors declare that they have no competing interests.
[1] |
M. Aphane, L. Jolaoso, K. Aremu, O. Oyewole, An inertial-viscosity algorithm for solving split generalized equilibrium problem and a system of demimetric mappings in Hilbert spaces, Rend. Circ. Mat. Palermo, II. Ser, 72 (2023), 1599–1628. https://doi.org/10.1007/s12215-022-00761-8 doi: 10.1007/s12215-022-00761-8
![]() |
[2] | E. Blum, W. Oettli, From optimization and variational inequalities to equilibrium problems, Math. Student, 63 (1994), 123–145. |
[3] | C. Byrne, Y. Censor, A. Gilbali, S. Reich, The split common null point problem, J. Nonlinear Convex A., 13 (2012), 759–775. |
[4] |
Y. Censor, A. Gibali, S. Reich, The subgradient extragradient method for solving variational inequalities Hilbert space, J. Optim. Theory Appl., 148 (2011), 318–335. http://dx.doi.org/10.1007/s10957-010-9757-3 doi: 10.1007/s10957-010-9757-3
![]() |
[5] |
Y. Censor, T. Bortfeld, B. Martin, A. Trofimov, A unified approach for inversion problems in intensity radiation therapy, Phys. Med. Biol., 51 (2006), 2353. http://dx.doi.org/10.1088/0031-9155/51/10/001 doi: 10.1088/0031-9155/51/10/001
![]() |
[6] |
Y. Censor, A. Gibali, S. Reich, Strong convergence of subgradient extragradient methods for the variational inequality problem in Hilbert space, Optim. Method. Softw., 26 (2011), 827–845. http://dx.doi.org/10.1080/10556788.2010.551536 doi: 10.1080/10556788.2010.551536
![]() |
[7] |
Y. Censor, A. Gibali, S. Reich, Extensions of Korpelevich's extragradient method for the variational inequality problem in Euclidean space, Optimization, 61 (2011), 1119–1132. http://dx.doi.org/10.1080/02331934.2010.539689 doi: 10.1080/02331934.2010.539689
![]() |
[8] |
B. Djafari-Rouhani, K. Kazmi, M. Farid, Common solution to systems of variational inequalities and fixed point problems, Fixed Point Theory, 18 (2017), 167–190. http://dx.doi.org/10.24193/FPT-RO.2017.1.14 doi: 10.24193/FPT-RO.2017.1.14
![]() |
[9] | G. Ficher, Sul pproblem elastostatico di signorini con ambigue condizioni al contorno, Atti Accad. Naz. Lincei, VIII. Ser., Rend., Cl. Sci. Fis. Mat. Nat, 34 (1963), 138–142. |
[10] |
M. Farid, K. Kazmi, A new mapping for finding a common solution of split generalized equilibrium problem, variational inequality problem and fixed point problem, Korean J. Math., 27 (2019), 297–327. http://dx.doi.org/10.11568/kjm.2019.27.2.297 doi: 10.11568/kjm.2019.27.2.297
![]() |
[11] |
B. He, A class of projection and contraction methods for monotone variational inequalities, Appl. Math. Optim., 35 (1997), 69–76. http://dx.doi.org/10.1007/BF02683320 doi: 10.1007/BF02683320
![]() |
[12] |
S. He, H. Xu, Uniqueness of supporting hyperplanes and an alternative to solutions of variational inequalities, J. Glob. Optim., 57 (2013), 1375–1384. http://dx.doi.org/10.1007/s10898-012-9995-z doi: 10.1007/s10898-012-9995-z
![]() |
[13] |
H. Iiduka, Acceleration method for convex optimization over the fixed point set of a nonexpansive mapping, Math. Program., 149 (2015), 131–165. http://dx.doi.org/10.1007/s10107-013-0741-1 doi: 10.1007/s10107-013-0741-1
![]() |
[14] |
K. Kazmi, S. Rizvi, Iterative approximation of a common solution of a split equilibrium problem, a variational inequality problem and a fixed point problem, Journal of the Egyptian Mathematical Society, 21 (2013), 44–51. http://dx.doi.org/10.1016/j.joems.2012.10.009 doi: 10.1016/j.joems.2012.10.009
![]() |
[15] |
K. Kazmi, S. Rizvi, Iterative approximation of a common solution of a split generalized equilibrium problem and a fixed point problem for nonexpansive semigroup, Math. Sci., 7 (2013), 1. http://dx.doi.org/10.1186/2251-7456-7-1 doi: 10.1186/2251-7456-7-1
![]() |
[16] | G. Korpelevich, An extragradient method for finding saddle points and for other problems, Ekon. Mat. Metody., 12 (1976), 747–756. |
[17] |
M. Lukumon, A. Mebawondu, A. Ofem, C. Agbonkhese, F. Akutsah, O. Narain, An efficient iterative method for solving quasimonotone bilevel split variational inequality problem, Adv. Fixed Point Theory, 13 (2023), 26. http://dx.doi.org/10.28919/afpt/8269 doi: 10.28919/afpt/8269
![]() |
[18] |
P. Maingé, A hybrid extragradient-viscosity method for monotone operators and fixed point problems, SIAM J. Control Optim., 47 (2008), 1499–1515. http://dx.doi.org/10.1137/060675319 doi: 10.1137/060675319
![]() |
[19] |
A. Moudafi, Split monotone variational inclusions, J. Optim. Theory Appl., 150 (2011), 275–283. http://dx.doi.org/10.1007/s10957-011-9814-6 doi: 10.1007/s10957-011-9814-6
![]() |
[20] |
A. Ofem, A. Mebawondu, G. Ugwunnadi, H. Isik, O. Narain, A modified subgradient extragradient algorithm-type for solving quasimonotone variational inequality problems with applications, J. Inequal. Appl., 2023 (2023), 73. http://dx.doi.org/10.1186/s13660-023-02981-7 doi: 10.1186/s13660-023-02981-7
![]() |
[21] | A. Ofem, A. Mebawondu, G. Ugwunnadi, P. Cholamjiak, O. Narain, Relaxed Tseng splitting method with double inertial steps for solving monotone inclusions and fixed point problems, Numer. Algor., in press. http://dx.doi.org/10.1007/s11075-023-01674-y |
[22] |
A. Ofem, A. Mebawondu, C. Agbonkhese, G. Ugwunnadi, O. Narain, Alternated inertial relaxed Tseng method for solving fixed point and quasi-monotone variational inequality problems, Nonlinear Functional Analysis and Applications, 29 (2024), 131–164. http://dx.doi.org/10.22771/nfaa.2024.29.01.10 doi: 10.22771/nfaa.2024.29.01.10
![]() |
[23] |
S. Saejung, P. Yotkaew, Approximation of zeros of inverse strongly monotone operators in Banach spaces, Nonlinear Anal.-Theor., 75 (2012), 742–750. http://dx.doi.org/10.1016/j.na.2011.09.005 doi: 10.1016/j.na.2011.09.005
![]() |
[24] |
M. Safari, F. Moradlou, A. Khalilzadah, Hybrid proximal point algorithm for solving split equilibrium problems and its applications, Hacet. J. Math. Stat., 51 (2022), 932–957. http://dx.doi.org/10.15672/hujms.1023754 doi: 10.15672/hujms.1023754
![]() |
[25] |
Y. Shehu, O. Iyiola, Projection methods with alternating inertial steps for variational inequalities: weak and linear convergence, Appl. Numer. Math., 157 (2020), 315–337. http://dx.doi.org/10.1016/j.apnum.2020.06.009 doi: 10.1016/j.apnum.2020.06.009
![]() |
[26] |
Y. Shehu, O. Iyiola, F. Ogbuisi, Iterative method with inertial terms for nonexpansive mappings, Applications to compressed sensing, Numer. Algor., 83 (2020), 1321–1347. http://dx.doi.org/10.1007/s11075-019-00727-5 doi: 10.1007/s11075-019-00727-5
![]() |
[27] |
Y. Shehu, P. Vuong, A. Zemkoho, An inertial extrapolation method for convex simple bilevel optimization, Optim. Method. Softw., 36 (2021), 1–19. http://dx.doi.org/10.1080/10556788.2019.1619729 doi: 10.1080/10556788.2019.1619729
![]() |
[28] | G. Stampacchia, Formes bilineaires coercitives sur les ensembles convexes, C. R. Math. Acad. Sci., 258 (1964), 4413. |
[29] | W. Takahashi, Nonlinear functional analysis, Yokohama: Yokohama Publishers, 2000. |
[30] | W. Takahashi, The split common fixed point problem and the shrinking projection method in Banach spaces, J. Convex Anal., 24 (2017), 1015–1028. |
[31] |
W. Takahashi, H. Xu, J. Yao, Iterative methods for generalized split feasibility problems in Hilbert spaces, Set-Valued Var. Anal., 23 (2015), 205–221. http://dx.doi.org/10.1007/s11228-014-0285-4 doi: 10.1007/s11228-014-0285-4
![]() |
[32] |
W. Takahashi, C. Wen, J. Yao, The shrinking projection method for a finite family of demimetric mappings with variational inequality problems in a Hilbert space, Fixed Point Theor., 19 (2018), 407–419. http://dx.doi.org/10.24193/fpt-ro.2018.1.32 doi: 10.24193/fpt-ro.2018.1.32
![]() |
[33] |
D. Thong, L. Liu, Q. Dong, L. Long, P. Tuan, Fast relaxed inertial Tseng's method-based algorithm for solving variational inequality and fixed point problems in Hilbert spaces, J. Comput. Appl. Math., 418 (2023), 114739. http://dx.doi.org/10.1016/j.cam.2022.114739 doi: 10.1016/j.cam.2022.114739
![]() |
[34] |
P. Tseng, A modified forward-backward splitting method for maximal monotone mappings, SIAM J. Control Optim., 38 (2000), 431–446. http://dx.doi.org/10.1137/S0363012998338806 doi: 10.1137/S0363012998338806
![]() |
[35] |
F. Wang, H. Xu, Cyclic algorithms for split feasibility problems in Hilbert spaces, Nonlinear Anal.-Theor., 74 (2011), 4105–4111. http://dx.doi.org/10.1016/j.na.2011.03.044 doi: 10.1016/j.na.2011.03.044
![]() |
[36] | Z. Xie, G. Cai, B. Tan, Inertial subgradient extragradient method for solving pseudomonotone equilibrium problems and fixed point problems in Hilbert spaces, Optimization, in press. http://dx.doi.org/10.1080/02331934.2022.2157677 |
[37] |
J. Yang, H. Liu, The subgradient extragradient method extended to pseudomonotone equilibrium problems and fixed point problems in Hilbert space, Optim. Lett., 14 (2020), 1803–1816. http://dx.doi.org/10.1007/s11590-019-01474-1 doi: 10.1007/s11590-019-01474-1
![]() |
1. | Georges Chamoun, Finite Volume Analysis of the Two Competing-species Chemotaxis Models with General Diffusive Functions, 2025, 22, 2224-2902, 232, 10.37394/23208.2025.22.24 |
Scheme | G. Rusanov | Rusanov | Lax-Friedrichs |
CPU time | 0.068005 | 0.056805 | 0.0496803 |