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An empirical assessment of Tukey combined extended exponentially weighted moving average control chart

  • Statistical process control (SPC) is a quality control method that enables the monitoring of processes using statistical methodologies. Nonparametric control charts, including the Tukey control chart (TCC), are a robust and effective instrument to assess a method since the actual distribution of the quality characteristic in question is indeterminate. The extended exponentially weighted moving average (EEWMA) control chart was employed to monitor the mean process because of its rapid detection of shifts. To maximize the benefits of both control charts, we developed a method known as EEWMA-TCC, which combines EEWMA with TCC. The efficacy of the proposed chart was evaluated under symmetrical distribution using various individual and aggregate performance metrics based on average run length (ARL) and percentage reduction in ARL (PDARL). Our findings indicated that the suggested chart outperforms control charts, including the TCC chart, the EWMA chart, the EEWMA chart, and the EWMA-TCC (mixed exponentially weighted moving average-Tukey) chart, in the quick identification of shifts. An application of the proposed designs in the crucial dimension of machined part data is demonstrated. The results indicated that they were consistent with the research findings. On the other hand, nonparametric control charts provide an alternate way to track the mean process.

    Citation: Khanittha Talordphop, Yupaporn Areepong, Saowanit Sukparungsee. An empirical assessment of Tukey combined extended exponentially weighted moving average control chart[J]. AIMS Mathematics, 2025, 10(2): 3945-3960. doi: 10.3934/math.2025184

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  • Statistical process control (SPC) is a quality control method that enables the monitoring of processes using statistical methodologies. Nonparametric control charts, including the Tukey control chart (TCC), are a robust and effective instrument to assess a method since the actual distribution of the quality characteristic in question is indeterminate. The extended exponentially weighted moving average (EEWMA) control chart was employed to monitor the mean process because of its rapid detection of shifts. To maximize the benefits of both control charts, we developed a method known as EEWMA-TCC, which combines EEWMA with TCC. The efficacy of the proposed chart was evaluated under symmetrical distribution using various individual and aggregate performance metrics based on average run length (ARL) and percentage reduction in ARL (PDARL). Our findings indicated that the suggested chart outperforms control charts, including the TCC chart, the EWMA chart, the EEWMA chart, and the EWMA-TCC (mixed exponentially weighted moving average-Tukey) chart, in the quick identification of shifts. An application of the proposed designs in the crucial dimension of machined part data is demonstrated. The results indicated that they were consistent with the research findings. On the other hand, nonparametric control charts provide an alternate way to track the mean process.



    Mathematical modelling provides a systematic formalism for the understanding of the corresponding real-world problem. Moreover, adequate mathematical tools for the analysis of the translated real-world problem are at our disposal. Fixed point theory (FPT), an important branch of nonlinear functional analysis, is prominent for modelling a variety of real-world problems. It is worth mentioning that the real-world phenomenon can be translated into well known existential as well as computational FPP.

    The EP theory provides an other systematic formalism for modelling the real-world problems with possible applications in optimization theory, variational inequality theory and game theory [7,10,13,17,18,19,21,25,28,31,32]. In 1994, Blum and Oettli [13] proposed the (monotone-) EP in Hilbert spaces. Since then various classical iterative algorithms are employed to compute the optimal solution of the (monotone-) EP and the FPP. It is remarked that the convergence characteristic and the speed of convergence are the principal attributes of an iterative algorithm. All the classical iterative algorithms from FPT or EP theory have a common shortcoming that the convergence characteristic occurs with respect to the weak topology. In order to enforce the strong convergence characteristic, one has to assume stronger assumptions on the domain and/or constraints. Moreover, strong convergence characteristic of an iterative algorithm is often more desirable than weak convergence characteristic in an infinite dimensional framework.

    The efficiency of an iterative algorithm can be improved by employing the inertial extrapolation technique [29]. This technique has successfully been combined with the different classical iterative algorithms; see e.g., [2,3,4,5,6,8,9,14,15,16,23,27]. On the other hand, the parallel architecture of the algorithm helps to reduce the computational cost.

    In 2006, Tada and Takahashi [33] suggested a hybrid framework for the analysis of monotone EP and FPP in Hilbert spaces. On the other hand, the iterative algorithm proposed in [33] fails for the case of pseudomonotone EP. In order to address this issue, Anh [1] suggested a hybrid extragradient method, based on the seminal work of Korpelevich [24], to address the pseudomonotone EP together with the FPP. Inspired by the work of Anh [1], Hieu et al. [21] suggested a parallel hybrid extragradient framework to address the pseudomonotone EP together with the FPP associated with nonexpansive operators.

    Inspired and motivated by the ongoing research, it is natural to study the pseudomonotone EP together with the FPP associated with the class of an η-demimetric operators. We therefore, suggest some variants of the classical Mann iterative algorithm [26] and the Halpern iterative algorithm [20] in Hilbert spaces. We formulate these variants endowed with the inertial extrapolation technique and parallel hybrid architecture for speedy strong convergence results in Hilbert spaces.

    The rest of the paper is organized as follows. We present some relevant preliminary concepts and useful results regarding the pseudomonotone EP and FPP in Section 2. Section 3 comprises strong convergence results of the proposed variants of the parallel hybrid extragradient algorithm as well as Halpern iterative algorithm under suitable set of constraints. In Section 4, we provide detailed numerical results for the demonstration of the main results in Section 3 as well as the viability of the proposed variants with respect to various real-world applications.

    Throughout this section, the triplet (H,<,>,) denotes the real Hilbert space, the inner product and the induced norm, respectively. The symbolic representation of the weak and strong convergence characteristic are and , respectively. Recall that a Hilbert space satisfies the Opial's condition, i.e., for a sequence (pk)H with pkν then the inequality lim infkpkν<lim infkpkμ holds for all μH with νμ. Moreover, H satisfies the the Kadec-Klee property, i.e., if pkν and pkν as k, then pkν0 as k.

    For a nonempty closed and convex subset KH, the metric projection operator ΠHK:HK is defined as ΠHK(μ)=argminνKμν. If T:HH is an operator then Fix(T)={νH|ν=Tν} represents the set of fixed points of the operator T. Recall that the operator T is called η-demimetric (see [35]) where η(,1), if Fix(T) and

    μν,μTμ12(1η)μTμ2,μHandνFix(T).

    The above definition is equivalently represented as

    Tμν2μν2+ημTμ2,μHandνFix(T),

    Recall also that a bifunction g:K×KR{+} is coined as (ⅰ) monotone if g(μ,ν)+g(ν,μ)0, for all μ,νK; and (ⅱ) strongly pseudomonotone if g(μ,ν)0g(ν,μ)αμν2,for all μ,νK, where α>0. It is worth mentioning that the monotonicity of a bifunction implies the pseudo-monotonicity, but the converse is not true. Recall the EP associated with the bifunction g is to find μK such that g(μ,ν)0 for all νK. The set of solutions of the equilibrium problem is denoted by EP(g).

    Assumption 2.1. [12,13] Let g:K×KR{+} bea bifunction satisfying the following assumptions:

    (A1) g is pseudomonotone, i.e., g(μ,ν)0g(μ,ν)0, for allμ,νK;

    (A2) g is Lipschitz-type continuous, i.e., there exist two nonnegativeconstants d1,d2 such that

    g(μ,ν)+g(ν,ξ)g(μ,ξ)d1μν2d2νξ2, for allμ,ν,ξK;

    (A3) g is weakly continuous on K×K imply that, if μ,νK and (pk), (qk) are two sequences in K such that pkμ and qkν respectively, then f(pk,qk)f(μ,ν);

    (A4) For each fixed μK, g(μ,) is convex and subdifferentiable on K.

    In view of the Assumption 2.1, EP(g) associated with the bifunction g is weakly closed and convex.

    Let gi:K×KR{+} be a finite family of bifunctions satisfying Assumption 2.1. Then for all i{1,2,,M}, we can compute the same Lipschitz coefficients (d1,d2) for the family of bifunctions gi by employing the condition (A2) as

    gi(μ,ξ)gi(μ,ν)gi(ν,ξ)d1,iμν2+d2,iνξ2d1μν2+d2νξ2,

    where d1=max1iM{d1,i} and d2=max1iM{d2,i}. Therefore, gi(μ,ν)+gi(ν,ξ)gi(μ,ξ)d1μν2d2νξ2. In addition, we assume Tj:HH to be a finite family of η-demimetric operators such that Γ:=(Mi=1EP(gi))(Nj=1Fix(Tj)). Then we are interested in the following problem:

    ˆpΓ. (2.1)

    Lemma 2.2. [11] Let μ,νH and βR then

    (1) μ+ν2μ2+2ν,μ+ν;

    (2) μν2=μ2ν22μν,ν;

    (3) βμ+(1β)ν2=βμ2+(1β)ν2β(1β)μν2.

    Lemma 2.3. [35] Let T:KH be an η-demimetric operator defined on a nonempty, closed and convex subset K of a Hilbert space H with η(,1). Then Fix(T) is closed and convex.

    Lemma 2.4. [36] Let T:KH be an η-demimetric operator defined on a nonempty, closed and convex subset K of a Hilbert space H with η(,1). Then the operator L=(1γ)Id+γT is quasi-nonexpansive provided that Fix(T) and 0<γ<1η.

    Lemma 2.5. [11] Let T:KK be a nonexpansive operator defined on a nonempty closed convex subset K of a real Hilbert spaceH and let (pk) be a sequence in K. If pkx and if (IdT)pk0, then xFix(T).

    Lemma 2.6. [37] Let h:KR be aconvex and subdifferentiable function on nonempty closed and convex subset K of a real Hilbert space H. Then, p solves the min{h(q):qK}, if and only if 0h(p)+NK(p), where h() denotes the subdifferential of h and NK(ˉp) is the normal cone of K at ˉp.

    Our main iterative algorithm of this section has the following architecture:

    Theorem 3.1. Let the following conditions:

    (C1) k=1ξkpkpk1<;

    (C2) 0<aγkmin{1η1,,1ηN},

    hold. Then Algorithm 1 solves the problem 2.1.

    Algorithm 1 Parallel Hybrid Inertial Extragradient Algorithm (Alg.1)
    Initialization: Choose arbitrarily, p0,p1H, KH and C1=H. Set k1, {α1,,αN}(0,1) such that Nj=1αj=1, 0<μ<min(12d1,12d2), ξk[0,1) and γk(0,).
    Iterative Steps: Given pkH, calculate ek, ˉvk and wk as follows:
      Step 1. Compute
        {ek=pk+ξk(pkpk1);ui,k=argmin{μgi(ek,ν)+12ekν2:νK},i=1,2,,M;vi,k=argmin{μgi(ui,k,ν)+12ekν2:νK},i=1,2,,M;ik=argmax{vi,kpk:i=1,2,,M},ˉvk=vik,k;wk=Nj=1αj((1γk)Id+γkTj)ˉvk;
      If wk=ˉvk=ek=pk then terminate and pk solves the problem 2.1. Else
      Step 2. Compute
        Ck+1={zCk:wkz2pkz2+ξ2kpkpk12+2ξkpkz,pkpk1},pk+1=ΠHCk+1p1,k1.
      Set k=:k+1 and return to Step 1.

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    The following result is crucial for the strong convergence result of the Algorithm 1.

    Lemma 3.2. [1,30] Suppose that νEP(gi), and pk, ek, ui,k, vi,k, i{1,2,,M} are defined in Step 1 of the Algorithm 1. Then we have

    vi,kν2ekν2(12μd1)ui,kek2(12μd2)ui,kvi,k2.

    Proof of Theorem 3.1.

    Step 1. The Algorithm 1 is stable.

    Observe the following representation of the set Ck+1:

    Ck+1={zCk:wkpk,z12(wk2pk2+ξ2kpkpk12+2ξkpkz,pkpk1)}.

    This infers that Ck+1 is closed and convex for all k1. It is well-known that EP(gi) and Fix(Tj) (from the Assumption 2.1 and Lemma 2.3, respectively) are closed and convex. Hence Γ is nonempty, closed and convex. For any pΓ, it follows from Algorithm 1 that

    ekp2=pkp+ξk(pkpk1)2pkp2+ξ2kpkpk12+2ξkpkp,pkpk1. (3.1)

    From (3.1) and recalling Lemma 2.4, we obtain

    wkp=Nj=1αj((1γk)Id+γkTj)ˉvkpNj=1αj((1γk)Id+γkTj)ˉvkpNj=1αjˉvkp=ˉvkp.

    Now recalling Lemma 3.2, the above estimate implies that

    wkp2ˉvkp2pkp2+ξ2kpkpk12+2ξkpkp,pkpk1. (3.2)

    The above estimate (3.2) infers that ΓCk+1. It is now clear from these facts that the Algorithm 1 is well-defined.

    Step 2. The limit limkpkp1 exists.

    From pk+1=ΠHCk+1p1, we have pk+1p1,pk+1ν0 for each νCk+1. In particular, we have pk+1p1,pk+1p0 for each pΓ. This proves that the sequence (pkp1) is bounded. However, from pk=ΠH1Ckp1 and pk+1=ΠH1Ck+1p1Ck+1, we have that

    pkp1pk+1p1.

    This infers that (pkp1) is nondecreasing and hence

    limkpkp1exists. (3.3)

    Step 3. ~pΓ.

    Compute

    pk+1pk2=pk+1p1+p1pk2=pk+1p12+pkp122pkp1,pk+1p1=pk+1p12+pkp122pkp1,pk+1pk+pkp1=pk+1p12pkp122pkp1,pk+1pkpk+1p12pkp12.

    Utilizing (3.3), the above estimate infers that

    limkpk+1pk=0. (3.4)

    Recalling the definition of (ek) and the condition (C1), we have

    limkekpk=limkξkpkpk1=0. (3.5)

    Recalling (3.4) and (3.5), the following relation

    ekpk+1ekpk+pkpk+1,

    infers that

    limkekpk+1=0. (3.6)

    Note that pk+1Ck+1, therefore the following relation

    wkpk+1pkpk+1+2ξkpkpk1+2ξkpkpk+1,pkpk1,

    infers, on employing (3.4) and the condition (C1), that

    limkwkpk+1=0. (3.7)

    Again, recalling (3.4) and (3.7), the following relation

    wkpkwkpk+1+pk+1pk

    infers that

    limkwkpk=0. (3.8)

    In view of the condition (C2), observe the variant of (3.2)

    (12μd1)uik,kek2(12μd2)uik,kvik,k2(pkp+wkp)pkwk+ξ2kpkpk12+2ξkpkppkpk1.

    Recalling (3.8) and condition (C1), we get

    (12μd1)limkuik,kek2(12μd2)limkuik,kvik,k2=0. (3.9)

    The above estimate (3.9) implies that

    limkuik,kek2=limkuik,kvik,k2=0. (3.10)

    Reasoning as above, recalling (3.5), (3.8) and (3.10), we have

    ˉvkekˉvkuik,k+uik,kek0;

    ˉvkpkˉvkek+ekpk0;

    wkekwkpk+pkek0;

    wkˉvkwkek+ekˉvk0.

    In view of the estimate limkwkˉvk=0, we have

    limkTjˉvkˉvk=0,j={1,2,,N}. (3.11)

    Next, we show that ~pMi=1EP(gi).

    Observe that

    ui,k=argmin{μgi(ek,ν)+12ekν2:νK}.

    Recalling Lemma 2.6, we get

    02{μgi(ek,ν)+12ekν2}(ui,k)+NK(ui,k).

    This implies the existence of ˜x2gi(ek,ui,k) and ~xNK(ui,k) such that

    μ˜x+ekui,k+~x. (3.12)

    Since ~xNK(ui,k) and ~x,νui,k0 for all νK. Therefore recalling (3.12), we have

    μ˜x,νui,kui,kek,νui,k,νK. (3.13)

    Since ˜x2gi(ek,ui,k),

    gi(ek,ν)gi(ek,ui,k)p,νui,k,νK. (3.14)

    Therefore recalling (3.13) and (3.14), we obtain

    μ(gi(ek,ν)gi(ek,ui,k))ui,kek,νui,k,νK. (3.15)

    Observe from the fact that (pk) is bounded then pkt~pH as t for a subsequence (pkt) of (pk). This also infers that ˉwkt~p, ˉvkt~p and bkt~p as t. Since ek~p and ekui,k0 as k, this implies ui,k~p. Recalling the assumption (A3) and (3.15), we deduce that gi(~p,ν)0 for all νK and i{1,2,,M}. Therefore, ~pMi=1EP(gi). Moreover, recall that ˉvkt~p as t and (3.11) we have ~pNj=1Fix(Tj). Hence ~pΓ.

    Step 4. pkp=ΠHΓp1.

    Since p=ΠHΓp1 and ~pΓ, therefore we have pk+1=ΠHCk+1p1 and pΓCk+1. This implies that

    pk+1p1pp1.

    By recalling the weak lower semicontinuity of the norm, we have

    p1pp1~plim inftp1pktlim suptp1pktp1p.

    Recalling the uniqueness of the metric projection operator yields that ~p=p=ΠHΓp1. Also limtpktp1=pp1=~pp1. Moreover, recalling the Kadec-Klee property of H with the fact that pktp1~pp1, we have pktp1~pp1 and hence pkt~p. This completes the proof.

    Corollary 3.3. Let KH be a nonempty closed and convex subset of a real Hilbert space H. For all i{1,2,,M}, let gi:K×KR{+} be a finite family of bifunctions satisfying Assumption 2.1. Assume that Γ:=Mi=1EP(gi), such that

    {ek=pk+ξk(pkpk1);ui,k=argmin{μgi(ek,ν)+12ekν2:νK},i=1,2,,M;vi,k=argmin{μgi(ui,k,ν)+12ekν2:νK},i=1,2,,M;ik=argmax{vi,kpk:i=1,2,,M},ˉvk=vik,k;Ck+1={zCk:ˉvkz2pkz2+ξ2kpkpk12+2ξkpkz,pkpk1};pk+1=ΠHCk+1p1,k1. (3.16)

    Assume that the condition (C1) holds, then the sequence (pk) generated by (3.16) strongly converges to a point in Γ.

    We now propose an other variant of the hybrid iterative algorithm embedded with the Halpern iterative algorithm [20].

    Remark 3.4. Note for the Algorithm 2 that the claim pk is a common solution of the EP and FPP provided that pk+1=pk, in general is not true. So intrinsically a stopping criterion is implemented for k>kmax for some chosen sufficiently large number kmax.

    Algorithm 2 Parallel Hybrid Inertial Halpern-Extragradient Algorithm (Alg.2)
    Initialization: Choose arbitrarily q,p0,p1H, KH and C1=H. Set k1, {α1,,αN},βk(0,1) such that Nj=1αj=1, 0<μ<min(12d1,12d2), ξk[0,1) and γk(0,).
    Iterative Steps: Given pkH, calculate ek, ˉvk and wk as follows:
      Step 1. Compute
        {ek=pk+ξk(pkpk1);ui,k=argmin{μgi(ek,ν)+12ekν2:νK},i=1,2,,M;vi,k=argmin{μgi(ui,k,ν)+12ekν2:νK},i=1,2,,M;ik=argmax{vi,kpk:i=1,2,,M},ˉvk=vik,k;wk=Nj=1αj((1γk)Id+γkTj)ˉvk;tl,k=βkq+(1βk)wk;lk=argmax{tj,kpk:j=1,2,,P},ˉtk=tlk,k.
        If ˉtk=wk=ˉvk=ek=pk then terminate and pk solves the problem 2.1. Else
      Step 2. Compute
        Ck+1={zCk:ˉtkz2βkqz2+(1βk)(pkz2+ξ2kpkpk12+2ξkpkz,pkpk1)};pk+1=ΠHCk+1  p1,k1.
      Set k=:k+1 and go back to Step 1.

     | Show Table
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    Theorem 3.5. Let Γ and the following conditions:

    (C1) k=1ξkpkpk1<;

    (C2) 0<aγkmin{1η1,,1ηN} and limkβk=0,

    hold. Then the Algorithm 2 solves the problem 2.1.

    Proof. Observe that the set Ck+1 can be expressed in the following form:

    Ck+1={zCk:ˉtkz2βkqz2+(1βk)(pkz2+ξ2kpkpk12+2ξkpkz,pkpk1)}.

    Recalling the proof of Theorem 3.1, we deduce that the sets Γand Ck+1 are closed and convex satisfying ΓCk+1 for all k0. Further, (pk) is bounded and

    limkpk+1pk=0. (3.17)

    Since pk+1=ΠHCk+1(q)Ck+1, we have

    ˉtkpk+12βkqpk+12+(1βk)(pkpk+12+ξ2kpkpk12+2ξkpkpk+1,pkpk1).

    Recalling the estimate (3.17) and the conditions (C1) and (C2), we obtain

    limkˉtkpk+1=0.

    Reasoning as above, we get

    limkˉtkpk=0.

    The rest of the proof of Theorem 3.5 follows from the proof of Theorem 3.1 and is therefore omitted.

    The following remark elaborate how to align condition (C1) in a computer-assisted iterative algorithm.

    Remark 3.6. We remark here that the condition (C1) can easily be aligned in a computer-assisted iterative algorithm since the value of pkpk1 is quantified before choosing ξk such that 0ξk^ξk with

    ^ξk={min{σkpkpk1,ξ}ifpkpk1;ξ                       otherwise.

    Here {σk} denotes a sequence of positives k=1σk< and ξ[0,1).

    As a direct application of Theorem 3.1, we have the following variant of the problem 2.1, namely the generalized split variational inequality problem associated with a finite family of single-valued monotone and hemicontinuous operators Aj:KH defined on a nonempty closed convex subset K of a real Hilbert space H for each j{1,2,,N}. The set VI(K,A) represents all the solutions of the following variational inequality problem Aμ,νμ0νC.

    Theorem 3.7. Assume that Γ=Mi=1VI(C,Ai)Nj=1Fix(Tj) and the conditions (C1)–(C4) hold. Then the sequence (pk)

    {ek=pk+ξk(pkpk1);ui,k=ΠK(ekμAi(ek)),i=1,2,,M;vi,k=ΠK(ekμAi(ui,k)),i=1,2,,M;ik=argmax{vi,kpk:i=1,2,,M},ˉvk=vik,k;wk=Nj=1αj((1γk)Id+γkTj)ˉvk;Ck+1={zCk:wkz2pkz2+ξ2kpkpk12+2ξkpkz,pkpk1},pk+1=ΠHCk+1p1,k1, (3.18)

    generated by (3.18) solves the problem 2.1.

    Proof. Observe that, if we set gi(ˉμ,ˉν)=Ai(ˉμ),ˉνˉμ for all ˉμ,ˉνK, then each Ai being L-Lipschitz continuous infers that gi is Lipschitz-type continuous with d1=d2=L2. Moreover, the pseudo-monotonicity of Ai ensures the pseudo-monotonicity of gi. Recalling the assumptions (A3)–(A4) and the Algorithm 1, note that

    ui,k=argmin{μAi(pk),νpk+12pkν2:νK};vi,k=argmin{μAi(ui,k),νui,k+12pkν2:νK},

    can be transformed into

    ui,k=argmin{12ν(pkμAi(pk)2:νK}=ΠK(pkμAi(pk));vi,k=argmin{12ν(pkμAi(ui,k)2:νK}=ΠK(pkμAi(ui,k)).

    Hence recalling gi(ˉμ,ˉν)=Ai(ˉμ),ˉνˉμ for all ˉμ,ˉνK and for all i{1,2,,M} in Theorem 3.1, we have the desired result.

    This section provides the effective viability of the algorithm via a suitable numerical experiment.

    Example 4.1. Let H=R be the set of all real numbers with the inner product defined by p,q=pq, for all p,qR and the induced usual norm ||. For each i={1,2,,M}, let the family of pseudomonotone bifunctions gi(p,q):K×KR on K=[0,1]H, is defined by gi(p,q)=Si(p)(qp), where

    Si(p)={0,0pλi;sin(pλi)+exp(pλi)1,λip1.

    where 0<λ1<λ2<...<λM<1. Note that EP(gi)=[0,λi] if and only if 0pλi and q[0,1]. Consequently, Mi=1EP(gi)=[0,λ1]. For each j{1,2,,N}, let the family of operators Tj:RR be defined by

    Tj(p)={3pj,p[0,);p,p(,0).

    Clearly, Tj defines a finite family of η-demimetric operators with Nj=1Fix(Tj)={0}. Hence Γ=(Mi=1EP(gi))(Nj=1Fix(Tj))=0. In order to compute the numerical values of the Algorithm 1, we choose ξ=0.5, αk=1100k+1, μ=17, λi=i(M+1), M=2×105 and N=3×105. Since

    {min{1k2pkpk1  ,0.5}    if   pkpk1;0.5                            otherwise,

    Observe that the expression

    ui,k=argmin{μSi(ek)(νek)+12(ypk)2,ν[0,1]},

    in the Algorithm 1 is equivalent to the following relation ui,k=ekμSi(ek),for alli{1,2,,M}. Similarly vi,k=ekμSi(ui,k),for alli{1,2,,M}. Hence, we can compute the intermediate approximation ˉvk which is farthest from ek among vi,k, for all i{1,2,,M}. Generally, at the kth step if Ek=pkpk1=0 then pkΓ implies that pk is the required solution of the problem. The terminating criteria is set as Ek<106. The values of the Algorithm 1 and its variant are listed in the following table (see Table 1):

    Table 1.  Numerical values of Algorithm 1.
    No. of Iter. CPU-Time (Sec)
    N0. Alg.1, ξk=0 Alg.1, ξk0 Alg.1, ξk=0 Alg.1, ξk0
    Choice 1. p0=(5), p1=(2) 87 75 0.088153 0.073646
    Choice 2. p0=(4.3), p1=(1.7) 88 79 0.072250 0.068662
    Choice 3. p0=(7), p1=(3) 99 92 0.062979 0.051163

     | Show Table
    DownLoad: CSV

    The values of the non-inertial and non-parallel variant of the Algorithm 1 referred as Alg.1 are listed in the following table (see Table 2):

    Table 2.  Numerical values of Algorithm Alg.1.
    No. of Choices No. of Iter. CPU-Time (Sec)
    Choice 1. p0=(5), p1=(2) 111 0.091439
    Choice 2. p0=(4.3), p1=(1.7) 106 0.089872
    Choice 3. p0=(7), p1=(3) 104 0.081547

     | Show Table
    DownLoad: CSV

    The error plotting Ek against the Algorithm 1 and its variants for each choices in Tables 1 and 2 are illustrated in Figure 1.

    Figure 1.  Comparison between Algorithm 1 and its variants in view of Example 4.1.

    Example 4.2. Let H=Rn with the induced norm p=ni=1|pi|2 and the inner product p,q=ni=1piqi, for all p=(p1,p2,,pn)Rn and q=(q1,q2,,qn)Rn. The set K is given by K={pRn+:|pk|1}, where k={1,2,,n}. Consider the following problem:

    find\;{p_ * } \in \Gamma : = \bigcap\limits_{i = 1}^M E P({g_i}) \cap \bigcap\limits_{j = 1}^N F ix({T_j}),

    where g_{i}:K \times K \rightarrow \mathbb{R} is defined by:

    \begin{equation*} g_{i}(p, q) = \mathop \sum \limits_{k = 1}^n S_{i, k}(q^{2}_{k}-p^{2}_{k}), \; \forall \; \; i \in \{1, 2, \cdots, M\}, \end{equation*}

    where S_{i, k} \in (0, 1) is randomly generated for all i = \{1, 2, \cdots, M\} and k = \{1, 2, \cdots, n\} . For each j \in \{1, 2, \cdots, N\} , let the family of operators T_{j}: \mathcal{H} \rightarrow \mathcal{H} be defined by

    \begin{equation*} T_{j}(p) = \left\{ \begin{array}{ll} & { -\frac{4p}{j} , \; \; \; \; \; \; \; \; \; \; p \in [0, \infty) ;} \\ & { \; \; \; \; \; \; \; p , \; \; \; \; \; \; \; \; \; \; p \in (-\infty, 0) .} \end{array} \right. \end{equation*}

    for all p \in \mathcal{H} . It is easy to observe that \Gamma = \bigcap^{M}_{i = 1}EP(g_{i}) \cap \bigcap^{N}_{j = 1}Fix(T_{j}) = 0 . The values of the Algorithm 1 and its non-inertial variant are listed in the following table (see Table 3):

    Table 3.  Numerical values of Algorithm 1.
    No. of Iter. CPU-Time (Sec)
    N0. Alg.1, \xi_{k}=0 Alg.1, \xi_{k} \neq 0 Alg.1, \xi_{k} = 0 Alg.1, \xi_{k} \neq 0
    Choice 1. p_{0}=(5) , p_{1}=(2) , n=5 46 35 0.061975 0.054920
    Choice 2. p_{0}=(1) , p_{1}=(1.5) , n=10 38 27 0.056624 0.040587
    Choice 3. p_{0}=(-8) , p_{1}=(3) , n=30 50 37 0.055844 0.041246

     | Show Table
    DownLoad: CSV

    The values of the non-inertial and non-parallel variant of the Algorithm 1 referred as Alg. 1^{\ast} are listed in the following table (see Table 4):

    Table 4.  Numerical values of Algorithm Alg.1 ^{\ast} .
    No. of Choices No. of Iter. CPU-Time (Sec)
    Choice 1. p_{0}=(5) , p_{1}=(2) , n=5 81 0.072992
    Choice 2. p_{0}=(1) , p_{1}=(1.5) , n=10 75 0.065654
    Choice 3. p_{0}=(-8) , p_{1}=(3) , n=30 79 0.068238

     | Show Table
    DownLoad: CSV

    The error plotting E_{k}\leq 10^{-6} against the Algorithm 1 and its variants for each choices in Tables 3 and 4 are illustrated in Figure 2.

    Figure 2.  Comparison between Algorithm 1 and its variants in view of Example 4.2.

    Example 4.3. Let L^{2}([0, 1]) = \mathcal{H} with induced norm \Vert p \Vert = (\int^{1}_{0}\vert p(s)\vert^{2} ds)^{\frac{1}{2}} and the inner product \langle p, q \rangle = \int^{1}_{0}p(s)q(s)ds , for all p, q \in L^{2}([0, 1]) and s \in [0, 1] . The feasible set K is given by: K = \{p \in L^{2}([0, 1]): \Vert p \Vert \leq 1\} . Consider the following problem:

    find\;\bar p \in \Gamma : = \bigcap\limits_{i = 1}^M E P({g_i}) \cap \bigcap\limits_{j = 1}^N F ix({T_j}),

    where g_{i}(p, q) is defined as \langle S_{i}p, q-p\rangle with the operator S_{i}:L^{2}([0, 1]) \rightarrow L^{2}([0, 1]) given by

    \begin{equation*} S_{i}(p(s)) = \max\Big\{0, \frac{p(s)}{i}\Big\}, \; \forall\; i \in \{1, 2, \cdots, M\}, \; s \in [0, 1]. \end{equation*}

    Since each g_{i} is monotone and hence pseudomonotone on C . For each j \in \{1, 2, \cdots, N\} , let the family of operators T_{j}: \mathcal{H} \rightarrow \mathcal{H} be defined by

    \begin{equation*} T_{j}(p) = \Pi_{C}(p) = \left\{ \begin{array}{ll} & { \frac{p}{\Vert p\Vert} , \; \; \; \; \; \; \; \; \; \; \Vert p \Vert > 1 ;} \\ & { \; \; \; \; \; \; \; p , \; \; \; \; \; \; \; \; \; \; \Vert p\Vert \leq 1 .} \end{array} \right. \end{equation*}

    Then T_{j} is a finite family of \eta -demimetric operators. It is easy to observe that \Gamma = \bigcap^{M}_{i = 1}EP(g_{i}) \cap \bigcap^{N}_{j = 1}Fix(T_{j}) = 0 . Choose M = 50 and N = 100 . The values of the Algorithm 1 and its non-inertial variant have been computed for different choices of p_{0} and p_{1} in the following table (see Table 5):

    Table 5.  Numerical values of Algorithm 1.
    No. of Iter. CPU-Time (Sec)
    N0. Alg.1, \xi_{k}=0 Alg.1, \xi_{k} \neq 0 Alg.1, \xi_{k} = 0 Alg.1, \xi_{k} \neq 0
    Choice 1. p_{0}=exp(3s)\times\sin (s) , p_{1}=3s^{2}-s 10 5 1.698210 0.981216
    Choice 2. p_{0}=\frac{1}{1+s} , p_{1}=\frac{s^{2}}{10} 14 6 2.884623 1.717623
    Choice 3. p_{0}=\frac{\cos(3s)}{7} , p_{1}=s 16 5 2.014687 1.354564

     | Show Table
    DownLoad: CSV

    The values of the non-inertial and non-parallel variant of the Algorithm 1 referred as Alg. 1^{\ast} have been computed for different choices of p_{0} and p_{1} in the following table (see Table 6):

    Table 6.  Numerical values of Algorithm Alg.1 ^{\ast} .
    No. of Choices No. of Iter. CPU-Time (Sec)
    Choice 1. p_{0}=exp(3s)\times\sin (s) , p_{1}=3s^{2}-s 23 2.65176
    Choice 2. p_{0}=\frac{1}{1+s} , p_{1}=\frac{s^{2}}{10} 27 3.102587
    Choice 3. p_{0}=\frac{\cos(3s)}{7} , p_{1}=s 26 2.903349

     | Show Table
    DownLoad: CSV

    The error plotting E_{k} = < 10^{-4} against the Algorithm 1 and its variants for each choices in Tables 5 and 6 are illustrated in Figure 3.

    Figure 3.  Comparison between Algorithm 1 and its variants in view of Example 4.3.

    We can see from Tables 16 and Figures 13 that the Algorithm 1 out performs its variants with respect to the reduction in the error, time consumption and the number of iterations required for the convergence towards the common solution.

    In this paper, we have constructed some variants of the classical extragradient algorithm that are embedded with the inertial extrapolation and hybrid projection techniques. We have shown that the algorithm strongly converges towards the common solution of the problem 2.1. A useful instance of the main result, that is, Theorem 3.1, as well as an appropriate example for the viability of the algorithm, have also been incorporated. It is worth mentioning that the problem 2.1 is a natural mathematical model for various real-world problems. As a consequence, our theoretical framework constitutes an important topic of future research.

    The authors declare that they have no competing interests.

    The authors wish to thank the anonymous referees for their comments and suggestions.

    The author Yasir Arfat acknowledge the support via the Petchra Pra Jom Klao Ph.D. Research Scholarship from King Mongkut's University of Technology Thonburi, Thailand (Grant No.16/2562).

    The authors Y. Arfat, P. Kumam, W. Kumam and K. Sitthithakerngkiet acknowledge the financial support provided by the Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT. Moreover, this reserch was funded by King Mongkut's University of Technology North Bangkok, Contract No. KMUTNB-65-KNOW-28.



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