Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

A preconditioned new modulus-based matrix splitting method for solving linear complementarity problem of H+-matrices

  • Received: 21 June 2022 Revised: 08 September 2022 Accepted: 19 September 2022 Published: 24 October 2022
  • For solving the linear complementarity problem (LCP), we propose a preconditioned new modulus-based matrix splitting (PNMMS) iteration method by extending the state-of-the-art new modulus-based matrix splitting (NMMS) iteration method to a more general framework with a customized preconditioner. We devise a generalized preconditioner that is associated with both H+-matrix A and vector q of the LCP. The convergence analysis is conducted under some mild conditions. In particular, we provide a comparison theorem to theoretically show the PNMMS method accelerates the convergence rate. Numerical experiments further illustrate that the PNMMS method is efficient and has better performance for solving the large and sparse LCP.

    Citation: Dongmei Yu, Yifei Yuan, Yiming Zhang. A preconditioned new modulus-based matrix splitting method for solving linear complementarity problem of H+-matrices[J]. Electronic Research Archive, 2023, 31(1): 123-146. doi: 10.3934/era.2023007

    Related Papers:

    [1] Mustafa Mudhesh, Aftab Hussain, Muhammad Arshad, Hamed AL-Sulami, Amjad Ali . New techniques on fixed point theorems for symmetric contraction mappings with its application. AIMS Mathematics, 2023, 8(4): 9118-9145. doi: 10.3934/math.2023457
    [2] Pragati Gautam, Vishnu Narayan Mishra, Rifaqat Ali, Swapnil Verma . Interpolative Chatterjea and cyclic Chatterjea contraction on quasi-partial b-metric space. AIMS Mathematics, 2021, 6(2): 1727-1742. doi: 10.3934/math.2021103
    [3] Tahair Rasham, Muhammad Nazam, Hassen Aydi, Abdullah Shoaib, Choonkil Park, Jung Rye Lee . Hybrid pair of multivalued mappings in modular-like metric spaces and applications. AIMS Mathematics, 2022, 7(6): 10582-10595. doi: 10.3934/math.2022590
    [4] Muhammad Tariq, Mujahid Abbas, Aftab Hussain, Muhammad Arshad, Amjad Ali, Hamid Al-Sulami . Fixed points of non-linear set-valued (α,ϕM)-contraction mappings and related applications. AIMS Mathematics, 2022, 7(5): 8861-8878. doi: 10.3934/math.2022494
    [5] Haitham Qawaqneh, Mohd Salmi Md Noorani, Hassen Aydi . Some new characterizations and results for fuzzy contractions in fuzzy b-metric spaces and applications. AIMS Mathematics, 2023, 8(3): 6682-6696. doi: 10.3934/math.2023338
    [6] Afrah Ahmad Noman Abdou . Chatterjea type theorems for complex valued extended b-metric spaces with applications. AIMS Mathematics, 2023, 8(8): 19142-19160. doi: 10.3934/math.2023977
    [7] Monairah Alansari, Mohammed Shehu Shagari, Akbar Azam, Nawab Hussain . Admissible multivalued hybrid Z-contractions with applications. AIMS Mathematics, 2021, 6(1): 420-441. doi: 10.3934/math.2021026
    [8] Tahair Rasham, Abdullah Shoaib, Shaif Alshoraify, Choonkil Park, Jung Rye Lee . Study of multivalued fixed point problems for generalized contractions in double controlled dislocated quasi metric type spaces. AIMS Mathematics, 2022, 7(1): 1058-1073. doi: 10.3934/math.2022063
    [9] Muhammad Tariq, Muhammad Arshad, Mujahid Abbas, Eskandar Ameer, Saber Mansour, Hassen Aydi . A relation theoretic m-metric fixed point algorithm and related applications. AIMS Mathematics, 2023, 8(8): 19504-19525. doi: 10.3934/math.2023995
    [10] Hanadi Zahed, Zhenhua Ma, Jamshaid Ahmad . On fixed point results in F-metric spaces with applications. AIMS Mathematics, 2023, 8(7): 16887-16905. doi: 10.3934/math.2023863
  • For solving the linear complementarity problem (LCP), we propose a preconditioned new modulus-based matrix splitting (PNMMS) iteration method by extending the state-of-the-art new modulus-based matrix splitting (NMMS) iteration method to a more general framework with a customized preconditioner. We devise a generalized preconditioner that is associated with both H+-matrix A and vector q of the LCP. The convergence analysis is conducted under some mild conditions. In particular, we provide a comparison theorem to theoretically show the PNMMS method accelerates the convergence rate. Numerical experiments further illustrate that the PNMMS method is efficient and has better performance for solving the large and sparse LCP.



    Simon and Volkmann considered in [1] the following two equations which are connected with the absolute values of some additive function γ:GR, that is,

    η(x)+η(y)=max{η(xy),η(x+y)},x,yG, (1.1)
    |η(x)η(y)|=min{η(xy),η(x+y)},x,yG, (1.2)

    for a real function η:GR defined on an abelian group (G,+) and both functional equations are satisfied by η(x)=|γ(x)| where γ(x+y)=γ(x)+γ(y). Moreover, solution of the equation

    η(x)η(y)=max{η(x+y),η(xy)}, (1.3)

    with supposition about G to be an abelian group was presented in the following theorem as:

    Theorem 1. [1, Theorem 2] Let η:GR, where every element of an abelian group G is divisible by 2 and 3. Then, η fulfills Eq (1.3) if and only if η(x)=0 or η(x)=e|γ(x)|, xG, where γ:GR is an additive function.

    The solutions of Eqs (1.1) and (1.2) presented by Jarczyk et al. [2] and are demonstrated as:

    Theorem 2. Let η:GR, where η is defined on an abelian group G. Then η fulfills Eq (1.1) if and only if functional Eq (1.2) holds and also satisfies η(2x)=2η(x) for xG.

    Furthermore, the most comprehensive study of the equation

    η(x)+η(y)=max{η(xy1),η(xy)}x,yG, (1.4)

    on groups has been presented in [3,4]. Volkmann has given the solution of Eq (1.4) with supposition that η fulfills the renowned condition called Kannappan condition [5], that is defined as, η(xgy)=η(xyg) for x,y,gG. Following that, Toborg [3] gave the characterization of such mappings exhibited in Eq (1.4) without taking into account the Kannappan condition and abelian group G. Their key findings are as follows:

    Theorem 3 (For the special case, see [3,4] for the general case). Let η:GR, where η is acting on any group G. Then, η fulfills Eq (1.4) if and only if η(x)=|γ(x)| for every xG, where γ:GR is an additive function.

    We suggest the readers consult the articles [2,6] and related cited references to get some inclusive results and solutions about the functional Eq (1.4). In addition, some stability results of Eqs (1.2) and (1.4) can be found in [7] and [6] respectively.

    Recently, in [8], Eq (1.3) presented in a generalized form as

    max{η(xy1),η(xy)}=η(x)η(y),x,yG, (1.5)

    with the exception of additional suppositions that every element of the abelian group is divisible by 2 and 3. Their main result is demonstrated as:

    Theorem 4 (see [8]). Let η:GR, where G is any group. Then a mapping η:GR fulfills the Eq (1.5) if and only if η0 or there exists a normal subgroup Nη such that

    Nη={xGη(x)=1}

    and

    xyNηorxy1Nη,x,yG  and  x,yNη;

    or η(x)=e|γ(x)|, xG, where γ:GR is an additive function.

    The main objective of this research article is to determine the solution to the generalized minimum functional equation

    χ(x)χ(y)=min{χ(xy1),χ(xy)},x,yG. (1.6)

    With the exception of additional suppositions, we derive some results concerning Eq (1.6) that are appropriate for arbitrary group G rather than abelian group (G,+).

    Redheffer and Volkmann [9] determined the solution of the Pexider functional equation

    max{h(x+y),h(xy)}=f(x)+g(x),x,yG, (1.7)

    for three unknown functions h, f and g acting on abelian group (G,+), which is a generalization of Eq (1.1).

    We will also examine the general solutions of the generalized Pexider-type functional equation

    max{η(xy),η(xy1)}=χ(x)η(y)+ψ(x),x,yG, (1.8)

    where real functions η, χ, and ψ are defined on any group G. This Pexider functional Eq (1.8) is a common generalization of two previous Eqs (1.4) and (1.5). Readers can see renowned papers [10,11] and associated references cited therein to obtain comprehensive results and discussions concerning the Pexider version of some functional equations.

    In this research paper, our group G will in general (G,) not be abelian (G,+), therefore, the group operation will be described multiplicatively as xy for x,yG. Symbol e will be acknowledged as the neutral element.

    Definition 1. Assume that G is any group. A mapping η:GR fulfills the Kannappan condition [5] if

    η(xzg)=η(xgz)for  every  g,x,zG.

    Remark 1. For every abelian group G, a mapping η:GR fulfills the Kannappan condition but converse may not be true.

    Lemma 1. Suppose that η:GR, where G is an arbitrary group. Let η is a strictly positive solution of the functional Eq (1.6), then η(x)=e|β(x)|, xG, where β:GR is an additive function.

    Proof. By given assumption, η(x)>0 for every xG. Since η satisfies Eq (1.6), as a result, 1η also satisfies the functional Eq (1.3), then by well-known theorem from [6], we can get that η(x)=e|β(x)|, xG, where β:GR is an additive function.

    First, we are going to prove the following important lemma which will be utilized several times during computations especially to prove Theorem 5.

    Lemma 2. Let η:GR, where G is an arbitrary group and η is a non-zero solution of Eq (1.6), then the following results hold:

    (1) η(e)=1;

    (2) η(x1)=η(x);

    (3) η(x1yx)=η(y);

    (4) η is central.

    Proof. (1). Putting y=e in (1.6), we can obtain that η(x)η(e)=η(x). By given condition, η is non-zero, therefore, we obtain η(e)=1.

    (2). Using x=e in functional Eq (1.6), we can deduce

    η(e)η(y1)=min{η(e.y1),η(e.y)}η(y1)=min{η(y1),η(y)}, (2.1)

    replacing y1 with y in Eq (2.1) provides that

    η(y)=min{η(y),η(y1)}. (2.2)

    Eqs (2.1) and (2.2) give that η(y1)=η(y). Since y is arbitrary, therefore, we have η(x1)=η(x) for any xG.

    (3). From functional Eq (1.6), the proof of property (3) can be obtained from the following simple calculation:

    η(x)η(x1yx)=min{η(x(x1yx)),η(x(x1yx)1)}=min{η(yx),η(xx1y1x)}=min{η((yx)1),η(y1x)}=min{η(x1y1),η((y1x)1)}(by Lemma 2(2))=min{η(x1y1),η(x1y)}η(x)η(x1yx)=η(x1)η(y)η(x)η(x1yx)=η(x)η(y)η(x1yx)=η(y).

    (4). By Lemma 2(3) and replacing y with xy, we can see that η(x1(xy)x)=η(xy), which gives η(xy)=η(yx), therefore, η is central.

    In addition, we concentrate on the main theorem of Section 2 to describe the solutions η of Eq (1.6).

    Theorem 5. Let η:GR, where G is an arbitrary group. A mapping η is a solution of Eq (1.6) if and only if η0 or there exists a normal subgroup Hη of G defined as

    Hη={xGη(x)=1}

    and fulfills the condition that

    xy1HηxyHηfor  every  x,yGHη; (2.3)

    or there exists a normal subgroup Hη of G fulfills the condition that

    xy1HηxyHηfor  every  x,yGHη; (2.4)

    or η(x)=e|β(x)|, xG, where β:GR is some additive function.

    Proof. The 'if' part obviously demonstrates that every mapping η determined in the statement of the theorem is a solution of Eq (1.6). Conversely, suppose that a function η:GR is a solution of (1.6), then putting x=y=e in Eq (1.6), we get η(e)=η(e)η(e), which gives that either η(e)=1 or η(e)=0. First, let η(e)=0, and then put y=e in (1.6) to get η(x)=0 for every xG. Suppose that η(e)=1, then there are the following different cases.

    Suppose that there exists zG such that η(z)0. Putting x=y in (1.6), we have min{η(x2),η(e)}=η(x)2, which gives that η(x)21, so 1η(x)1 but η(z)0, therefore, 1η(z)0. Let 1<η(z)<0, then we can compute

    min{η(z2),η(e)}=η(z)2min{η(z2),1}=η(z)2<1,

    which implies that η(z2)=η(z)2. Moreover,

    η(z)min{η(z3),η(z)}=η(z2)η(z)=η(z)2η(z)=η(z)3η(z)>η(z),

    which gives a contradiction, consequently, either η(z)=0 or η(z)=1. Additionally, it is not possible that η(x)=0 and η(y)=1 for some x,yG. Since η(e)=1, therefore, either η(x){0,1} or η(x){1,1}. Moreover, define Hη={xGη(x)=1}.

    It is obvious that eHη for the reason that η(e)=1. Suppose that hHη; then from Lemma 2(2) we obtain η(h1)=η(h)=1; therefore, h1Hη. Let h1,h2Hη; then, η(h1)=η(h2)=1, and we can deduce from Eq (1.6) that

    η(h1h2)=η(h1h2)η(h2)=min{η(h1h22),η(h1)}η(h1)η(h1h2)η(h1)η(h2). (2.5)
    η(h1)η(h2)=min{η(h1h2),η(h1h12)}η(h1h2)η(h1)η(h2)η(h1h2). (2.6)

    By (2.5) and (2.6) we can get that η(h1h2)=η(h1)η(h2)=1, therefore, we have h1h2Hη. Consequently, Hη is a subgroup of G. Assume that hHη; then Lemma 2(3) yields that η(x1hx)=η(h) for any xG and hHη; accordingly, Hη is a normal subgroup of G.

    First, suppose that η(x){0,1} and x,yGHη; therefore, η(x)=η(y)=0, then, by functional Eq (1.6), we have min{η(xy),η(xy1)}=η(x)η(y)=0. In a consequence, we can determine that xy1HηxyHη for any x,yGHη.

    In addition, considering the second case, let η(x){1,1} and let x,yGHη; thus, η(x)1 and η(y)1; then η(x)=η(y)=1. Consequently, Eq (1.6) gives that min{η(xy),η(xy1)}=η(x)η(y)=1. In either case, we can conclude that η(xy)=1 and η(xy1)=1, which infers that xy1HηxyHη for all x,yGHη.

    Furthermore, let η(x)>0 for all xG, then from Lemma 1, we can conclude that η(x)=e|β(x)|, xG.

    Corollary 1. Let η:GR, where G is an arbitrary group. Assume that η is a non-zero solution of the functional Eq (1.6); then the commutator subgroup G is a normal subgroup of Hη.

    Proof. Since η is a non-zero, then by the main theorem, we can derive the following cases:

    Case 1. According to the main theorem, there exists a normal subgroup Hη defined as η(x)=1 for every xHη and also satisfies the condition (2.4); consequently, by Lemma 2, we can compute that

    (xy)1Hη(xy1)1Hηy1x1Hηyx1Hηx1y1Hηx1yHηxyx1y1Hηxy1x1yHη[x,y]Hη(xy1x1y)1Hη[x,y]Hηy1xyx1Hη[x,y]Hηxyx1y1Hη,

    which indicates that η([x,y])=1.

    Case 2. There exists a normal subgroup Hη which satisfies the condition (2.3), that is xy1HηxyHη for all x,yGHη; accordingly, applying Lemma 2 and Case 1, we can deduce that η([x,y])=1.

    Case 3. Assume that η(x)>0 for any xG; consequently by Theorem 5, we have η(x)=e|β(x)| for any xG, where β:GR is an additive function, thus, η([x,y])=1 for the reason that β([x,y])=0 for any x,yG.

    Hence, in either case, the required proof is completed.

    Corollary 2. Any solution η:GR of Eq (1.6) on any group G fulfills the Kannappan condition.

    Proof. The proof relies on the following cases:

    Case 1. Assume that η0 on group G, then it is obvious that η fulfills the Kannappan condition.

    Case 2. Let η(x)0 for all xG. Then from Theorem 5 and Corollary 1, there exists normal subgroup Hη such that GHη, consequently, η(xyg)=1 if and only if xygHη if and only if [y1,x1]xyg=xgyHη if and only if η(xgy)=1. It is sufficient to prove the Kannappan condition because η only takes the values 1, 0, and -1.

    Case 3. Suppose that η(x)>0, xG, then η(x)=e|β(x)|, therefore η(xyg)=η(xgy) for any x,g,yG because β is an additive function.

    Corollary 3. If η is a strictly positive solution of (1.6), then max{η(xy1),η(xy)}(0,1].

    Theorem 6. Let η:GR and η is a non-zero solution of (1.6), then:

    (1) Assume that gG and η(gx1)=η(gx) for some elements xG with the restriction that η(x2)1. Then η(g2)=1.

    (2) Suppose that Gη={gGη(g2)=1}, then Gη is a normal subgroup of G.

    (3) If η is strictly positive, then Gη=Hη.

    Proof. Assume that x,yG, then by Eq (1.6) and Corollary 2, we have

    η(gx)η(gx1)=min{η(gxgx1),η(gx(gx1)1)}=min{η(gxgx1),η(gxxg1)}=min{η(g2),η(gx2g1)}η(gx)η(gx1)=min{η(g2),η(x2)}. (2.7)

    (1). By given condition η(gx)=η(gx1) for some xG and by Eq (2.7), we can see that either η(x2)=1 or η(g2)=1, therefore, given condition η(x2)1 implies that η(g2)=1.

    (2). Since η(e)=1, therefore eGη. Let gGη; then η(g2)=1. Moreover, η(x1)=η(x) gives that η(g2)=η(g2)=1, therefore g1Gη. Let g,yGη; then, η(y2)=1 and η(g2)=1, therefore, a simple calculation yields

    η(y2g2)=η(y2g2)η(g2)=min{η(y2g4),η(y2)}η(y2)η(y2g2)η(y2)η(g2). (2.8)
    η(y2)η(g2)=min{η(y2g2),η(y2g2)}η(y2g2)η(y2)η(g2)η(y2g2), (2.9)

    So, inequalities (2.8) and (2.9) implies that η(y2g2)=η(y2)η(g2)=1, which yields that ygGη, consequently, Gη is a subgroup of G. Additionally, Lemma 2 provides that η(xg)=η(gx) for every xG and gGη. As a result, Gη is a normal subgroup of a group G.

    (3). As η(x)>0 for every xG, then the proof can be seen easily from Lemma 1 and Theorem 6 (2).

    Definition 2. Suppose that group G is abelian. Then a mapping η:GR is called a discrete norm if η(x)>γ, where γ>0 and xG{e}. Then (G,η,e) is said to be a discretely normed abelian group [12].

    Theorem 7. Assume that (G,η,e) is a discretely normed abelian group. A mapping η:GR is a solution of (1.6) if and only if η(x)=e|β(x)|, xG{e}, where β:GR is some additive function.

    Proof. Since (G,η,e) is a discretely normed, then there exists a mapping η:GR such that η(x)>γ, where γ>0 and xG{e}. Setting η(x)=logη(x), and using Lemma 1, we get

    min{η(xy1),η(xy)}=η(x)η(y)

    if and only if η(x)=e|β(x)|, xG{e}, where β:GR is some additive function.

    Corollary 4. For free abelian group G, a mapping η is a solution of Eq (1.6) if and only if η(x)=e|β(x)|, xG{e}, where β:GR is some additive function.

    Theorem 8. Let η:GR fulfills the Kannappan condition, where G is an arbitrary group. Then η,χ,ψ are solutions of the functional Eq (1.8) if and only if

    {η(x)=λ1,xG,λ1R,ψ  is  an  arbitrary  function,χ(x)=1λ11ψ(x);

    or

    {η(x)=ξ(x)+λ1,xG,λ1R,χ(x)=1,ψ(x)=ξ(x),

    where ξ:GR is a solution of Eq (1.4);

    or

    {η(x)=λ2ξ(x)+λ1,xG,λ1,λ2R,λ2>0,χ(x)=ξ(x),ψ(x)=λ1(1ξ(x)),

    where ξ:GR is a solution of Eq (1.5);

    or

    {η(x)=λ2ξ(x)+λ1,xG,λ1,λ2R,λ2<0,χ(x)=ξ(x),ψ(x)=λ1(1ξ(x)),

    where ξ:GR is a solution of Eq (1.6).

    Proof. The 'if' part of the theorem can easily be seen that every function η, χ, and ξ presented in the statement is a solution of Eq (1.8). Conversely, suppose that η,χ,ψ are solutions of Eq (1.8), then we have the following cases:

    (1). η is constant.

    Assuming that η(x)=λ1 for xG and λ1R, we may deduce from Eq (1.8) that χ(x)=1λ11ψ(x) when ψ is an arbitrary function, which is required result described in the statement.

    (2). η is not constant.

    Setting y=e in Eq (1.8) gives that

    max{η(x),η(x)}=χ(x)η(e)+ψ(x)η(x)=χ(x)η(e)+ψ(x)ψ(x)=η(x)χ(x)η(e). (3.1)

    Using Eq (3.1) in (1.8), we conclude that

    max{η(xy),η(xy1)}=χ(x)η(y)+η(x)χ(x)η(e). (3.2)

    Setting x=e, we can obtain

    max{η(y),η(y1)}=χ(e)η(y)+η(e)χ(e)η(e). (3.3)

    We are going to show that χ(e)=1, but on the contrary, assume that χ(e)1. Setting

    H:={yG:η(y1)η(y)},H:=GH.

    If yH then y1H. Also, if yH, then from Eq (3.3), we have

    η(y)=χ(e)η(y)+η(e)χ(e)η(e)(χ(e)1)(η(e)η(y))=0,

    which implies that η(y)=η(e) for all yH. Moreover, H because η is not constant. Assume that yH then η(y)<η(y1)=η(e), which implies that

    η(y)η(e)<0. (3.4)

    Writing y instead of y in Eq (3.3) and using (3.4) we can get that

    η(e)=η(y1)=χ(e)η(y)+η(e)χ(e)η(e),

    which implies that (η(y)η(e))χ(e)=0, so χ(e)=0. Setting x=y and y=y1 in (3.2) we have

    η(e)max{η(e),η(y2)}=χ(y)η(y1)+η(y)χ(y)η(e)=χ(y)η(e)+η(y)χ(y)η(e)η(e)η(y)<η(e),

    which is a contradiction, thus, we have χ(e)=1. Moreover, from Eq (3.3), we can see that

    max{η(y),η(y1)}=η(y), (3.5)

    writting y1 instead of y in (3.5) we have

    max{η(y1),η(y)}=η(y1), (3.6)

    from (3.5) and (3.6) we can get that η(y1)=η(y).

    Since η(x1)=η(x) for every xG, then from Eq (3.2) and Kannappan condition we have

    χ(x)η(y)+η(x)χ(x)η(e)=max{η(xy),η(xy1)}=max{η(y1x1),η(yx1)}=max{η(ey1x1),η(eyx1)}=max{η(x1y1),η(x1y)}χ(x)η(y)+η(x)χ(x)η(e)=χ(x1)η(y)+η(x1)χ(x1)η(e)(η(y)η(e))(χ(x)χ(x1))=0,

    which infers that χ(x1)χ(x)=0 because η is not constant. Moreover, when η is not constant then η(x1)=η(x) and χ(x1)=χ(x) for every xG, consequently, by Eq (3.1) we can get that ψ(x1)=ψ(x). Also, by Eq (3.2) and Kannappan condition we can see that

    χ(x)η(y)+η(x)χ(x)η(e)=max{η(xy),η(xy1)}=max{η(exy),η(yx1)}=max{η(yx),η(yx1)}χ(x)η(y)+η(x)χ(x)η(e)=χ(y)η(x)+η(y)χ(y)η(e)χ(x)(η(y)η(e))+η(x)=χ(y)(η(x)η(e))+η(y)χ(x)(η(y)η(e))(η(y)η(e))=χ(y)(η(x)η(e))(η(y)η(e))(η(y)η(e))(χ(x)1)=(η(x)η(e))(χ(y)1).

    Suppose that η(y)η(e) for yG, then we can obtain that

    χ(x)1=χ(y)1η(y)η(e)(η(x)η(e)).

    Moreover, assume that β:=χ(y)1η(y)η(e), then χ(x)1=β(η(x)η(e)), so we can write as χ1(x)=βη1(x), where

    χ1(x):=χ(x)1,xG, (3.7)
    η1(x):=η(x)η(e),xG. (3.8)

    Also η1(e)=0. By functional Eq (3.2) and definition of η1, we have

    max{η1(xy),η1(xy1)}=max{η(xy),η(xy1)}η(e)=χ(x)η(y)+η(x)χ(x)η(e)η(e)=χ(x)(η(y)η(e))+η(x)η(e)=χ(x)η1(y)+η1(x)=(βη1(x)+1)η1(y)+η1(x)max{η1(xy),η1(xy1)}=βη1(x)η1(y)+η1(x)+η1(y). (3.9)

    According to the different values of β, we can discuss the following three different cases.

    Case 1. β=0.

    By Eq (3.7), we see that χ(x)=1, xG. Furthermore, by functional Eq (3.9), we have

    max{η1(xy),η1(xy1)}=η1(x)+η1(y),

    for all x,yG and also η1 satisfies the functional Eq (1.4), then from well-known theorem of Toborg [3], there exists some additive function g:GR such that η1(x)=|g(x)| for all xG, then from Eqs (3.1), (3.7) and (3.8), we can deduce

    {η(x)=λ1+ξ(x),χ(x)=1,ψ(x)=ξ(x),

    where λ1=η(e) and ξ:GR is a solution of Eq (1.4) such that ξ(x)=|g(x)|.

    Case 2. β>0.

    Let η2:=βη1(x) for all xG, then multiplying functional Eq (3.9) by β, we conclude that

    max{βη1(xy),βη1(xy1)}=(βη1(x))(βη1(y))+βη1(x)+βη1(y)max{η2(xy),η2(xy1)}=η2(x)η2(y)+η2(x)+η2(y)=(η2(x)+1)(η2(y)+1)1max{η2(xy),η2(xy1)}+1=(η2(x)+1)(η2(y)+1),

    then by setting ξ(x):=η2(x)+1 for xG, we get

    max{ξ(xy),ξ(xy1)}=ξ(x)ξ(y),x,yG.

    It is clear that ξ:GR satisfies Eq (1.5), then from Eq (3.8) we get

    ξ(x)=η2(x)+1=βη1(x)+1=β(η(x)η(e))+1,

    which gives that η(x)=λ2ξ(x)+λ1 where λ2=β1,λ1=η(e)β1.

    Also, from Eqs (3.1), (3.7) and (3.8), we can see that

    {η(x)=λ2ξ(x)+λ1,xG,λ2>0,χ(x)=ξ(x),ψ(x)=λ1(1ξ(x)),

    where ξ:GR is a solution of (1.5).

    Case 3. β<0.

    Assume that η2:=βη1(x) for every xG, then multiplying functional Eq (3.9) by β, we have

    max{βη1(xy),βη1(xy1)}=(βη1(x))(βη1(y))βη1(x)βη1(y)max{η2(xy),η2(xy1)}=η2(x)η2(y)+η2(x)+η2(y)=(η2(x)1)(η2(y)1)+1max{η2(xy),η2(xy1)}1=(η2(x)1)(η2(y)1),

    for any x,yG, then by setting ξ1(x):=η2(x)1 for xG, we have

    max{ξ1(xy),ξ1(xy1)}=ξ1(x)ξ1(y),x,yG,

    then by setting ξ(x):=ξ1(x), xG, we can see that ξ:GR satisfies the Eq (1.6), then from Eqs (3.1), (3.7) and (3.8), we have

    {η(x)=λ2ξ(x)+λ1,xG,λ2<0,χ(x)=ξ(x),ψ(x)=λ1(1ξ(x)),

    which completes the proof.

    The authors are grateful to the referees and the editors for valuable comments and suggestions, which have improved the original manuscript greatly. This work was supported by the National Natural Science Foundation of P. R. China (Grant number 11971493 and 12071491).

    All authors declare no conflict of interest in this paper.



    [1] N. W. Kappel, L. T. Watson, Iterative algorithms for the linear complementarity problem, Int. J. Comput. Math., 19 (1986), 273–297. https://doi.org/10.1080/00207168608803522 doi: 10.1080/00207168608803522
    [2] K. G. Murty, F.-T. Yu, Linear Complementarity, Linear and Nonlinear Programming, Heldermann: Berlin, Germany, 1988.
    [3] Y. Li, P. Dai, Generalized AOR methods for linear complementarity problem, Appl. Math. Comput., 188 (2007), 7–18. https://doi.org/10.1016/j.amc.2006.09.067 doi: 10.1016/j.amc.2006.09.067
    [4] O. L. Mangasarian, Solution of symmetric linear complementarity problems by iterative methods, J. Optim. Theory Appl., 22 (1977), 465–485. https://doi.org/10.1007/bf01268170 doi: 10.1007/bf01268170
    [5] H. Saberi Najafi, S. A. Edalatpanah, On the convergence regions of generalized accelerated overrelaxation method for linear complementarity problems, J. Optim. Theory Appl., 156 (2013), 859–866. https://doi.org/10.1007/s10957-012-0135-1 doi: 10.1007/s10957-012-0135-1
    [6] Y.-F. Ke, C.-F. Ma, On SOR-like iteration methods for solving weakly nonlinear systems, Optim. Methods Softw., 37 (2020), 320–337. https://doi.org/10.1080/10556788.2020.1755861 doi: 10.1080/10556788.2020.1755861
    [7] Y.-F. Ke, The new iteration algorithm for absolute value equation, Appl. Math. Lett., 99 (2020), 105990. https://doi.org/10.1016/j.aml.2019.07.021 doi: 10.1016/j.aml.2019.07.021
    [8] Z.-Z. Bai, On the convergence of the multisplitting methods for the linear complementarity problem, SIAM J. Matrix Anal. Appl., 21 (1999), 67–78. https://doi.org/10.1137/s0895479897324032 doi: 10.1137/s0895479897324032
    [9] Z.-Z. Bai, Parallel chaotic multisplitting iterative methods for the large sparse linear complementarity problem, J. Comput. Math., 19 (2001), 281–292.
    [10] Z.-Z. Bai, L.-L. Zhang, Modulus-based synchronous multisplitting iteration methods for linear complementarity problems, Numer. Linear Algebra Appl., 20 (2012), 425–439. https://doi.org/10.1007/s11075-012-9566-x doi: 10.1007/s11075-012-9566-x
    [11] Z.-Z. Bai, L.-L. Zhang, Modulus-based synchronous two-stage multisplitting iteration methods for linear complementarity problems, Numer. Algorithms, 62 (2013), 59–77. https://doi.org/10.1007/s11075-012-9566-x doi: 10.1007/s11075-012-9566-x
    [12] Z.-Z. Bai, D. J. Evans, Matrix multisplitting methods with applications to linear complementarity problems: Parallel synchronous and chaotic methods, Calculateurs Paralleles Reseaux et systemes repartis, 13 (2001), 125–141.
    [13] Z.-Z. Bai, D. J. Evans, Matrix multisplitting methods with applications to linear complementarity problems: parallel asynchronous methods, Int. J. Comput. Math., 79 (2002), 205–232. https://doi.org/10.1080/00207160211927 doi: 10.1080/00207160211927
    [14] Z.-Z. Bai, Modulus-based matrix splitting iteration methods for linear complementarity problems, Numer. Linear Algebra Appl., 17 (2010), 917–933. https://doi.org/10.1002/nla.680 doi: 10.1002/nla.680
    [15] L.-L. Zhang, Z.-R. Ren, Improved convergence theorems of modulus-based matrix splitting iteration methods for linear complementarity problems, Appl. Math. Lett., 26 (2013), 638–642. https://doi.org/10.1016/j.aml.2013.01.001 doi: 10.1016/j.aml.2013.01.001
    [16] C. Kanzow, Inexact semismooth Newton methods for large-scale complementarity problems, Optim. Methods Softw., 19 (2004), 309–325. https://doi.org/10.1080/10556780310001636369 doi: 10.1080/10556780310001636369
    [17] Z. Sun, J.-P. Zeng, A monotone semismooth Newton type method for a class of complementarity problems, J. Comput. Appl. Math., 235 (2011), 1261–1274. https://doi.org/10.1016/j.cam.2010.08.012 doi: 10.1016/j.cam.2010.08.012
    [18] Z.-Z. Bai, L.-L. Zhang, Modulus-based multigrid methods for linear complementarity problems, Numer. Linear Algebra Appl., 24 (2017), e2105. https://doi.org/10.1002/nla.2105 doi: 10.1002/nla.2105
    [19] Z.-G. Huang, J.-J. Cui, Accelerated relaxation modulus-based matrix splitting iteration method for linear complementarity problems, Bull. Malays. Math. Sci. Soc., 44 (2021), 2175–2213. https://doi.org/10.1007/s40840-020-01049-9 doi: 10.1007/s40840-020-01049-9
    [20] D.-K. Li, L. Wang, Y.-Y. Liu, A relaxation general two-sweep modulus-based matrix splitting iteration method for solving linear complementarity problems, J. Comput. Appl. Math., 409 (2022), 114140. https://doi.org/10.1016/j.cam.2022.114140 doi: 10.1016/j.cam.2022.114140
    [21] X.-F. Peng, M. Wang, W. Li, A relaxation two-sweep modulus-based matrix splitting iteration method for linear complementarity problems, East Asian J. Appl. Math., 9 (2019), 102–121. https://doi.org/10.4208/eajam.020318.220618 doi: 10.4208/eajam.020318.220618
    [22] H. Ren, X. Wang, X.-B. Tang, T. Wang, The general two-sweep modulus-based matrix splitting iteration method for solving linear complementarity problems, Comput. Math. Appl., 77 (2018), 1–11. https://doi.org/10.1016/j.camwa.2018.10.040 doi: 10.1016/j.camwa.2018.10.040
    [23] S.-L. Wu, C.-X. Li, P. Agarwal, Relaxed modulus-based matrix splitting methods for the linear complementarity problem, Symmetry, 13 (2021), 503. https://doi.org/10.3390/sym13030503 doi: 10.3390/sym13030503
    [24] B.-H. Huang, C.-F. Ma, Accelerated modulus-based matrix splitting iteration method for a class of nonlinear complementarity problems, Comp. Appl. Math., 37 (2018), 3053–3076. https://doi.org/10.1007/s40314-017-0496-z doi: 10.1007/s40314-017-0496-z
    [25] S.-L. Xie, H.-R. Xu, J.-P. Zeng, Two-step modulus-based matrix splitting iteration method for a class of nonlinear complementarity problems, Linear Algebra Appl., 494 (2016), 1–10. https://doi.org/10.1016/j.laa.2016.01.002 doi: 10.1016/j.laa.2016.01.002
    [26] H. Zheng, S. Vong, L. Liu, The relaxation modulus-based matrix splitting iteration method for solving a class of nonlinear complementarity problems, Int. J. Comput. Math., 96 (2019), 1648–1667. https://doi.org/10.1080/00207160.2018.1504928 doi: 10.1080/00207160.2018.1504928
    [27] H. Zheng, S. Vong, A two-step modulus-based matrix splitting iteration method for horizontal linear complementarity problems, Numer. Algorithms, 86 (2021), 1791–1810. https://doi.org/10.1007/s11075-020-00954-1 doi: 10.1007/s11075-020-00954-1
    [28] Y. Cao, A. Wang, Two-step modulus-based matrix splitting iteration methods for implicit complementarity problems, Numer. Algorithms, 82 (2019), 1377–1394. https://doi.org/10.1007/s11075-019-00660-7 doi: 10.1007/s11075-019-00660-7
    [29] J. Lu, W. Xiang, A generalized two-step modulus-based matrix splitting iteration method for implicit complementarity problems of H+-matrices, Filomat, 33 (2019), 4875–4888. https://doi.org/10.2298/fil1915875j doi: 10.2298/fil1915875j
    [30] Y. Wang, J.-F. Yin, Q.-Y. Dou, R. Li, Two-step modulus-based matrix splitting iteration methods for a class of implicit complementarity problems, Numer. Math. Theor. Meth. Appl., 12 (2019), 867–883. https://doi.org/10.4208/nmtma.oa-2018-0034 doi: 10.4208/nmtma.oa-2018-0034
    [31] Y.-F. Ke, C.-F. Ma, H. Zhang, The modulus-based matrix splitting iteration methods for second-order cone linear complementarity problems, Numer. Algorithms, 79 (2018), 1283–1303. https://doi.org/10.1007/s11075-018-0484-4 doi: 10.1007/s11075-018-0484-4
    [32] Y.-F. Ke, C.-F. Ma, H. Zhang, The relaxation modulus-based matrix splitting iteration methods for circular cone nonlinear complementarity problems, J. Comput. Appl. Math., 37 (2018), 6795–6820. https://doi.org/10.1007/s40314-018-0687-2 doi: 10.1007/s40314-018-0687-2
    [33] Y.-F. Ke, Convergence analysis on matrix splitting iteration algorithm for semidefinite linear complementarity problems, Numer. Algorithms, 86 (2021), 257–279. https://doi.org/10.1007/s11075-020-00888-8 doi: 10.1007/s11075-020-00888-8
    [34] S.-L. Wu, C.-X. Li, A class of new modulus-based matrix splitting methods for linear complementarity problem, Optim. Lett., 16 (2022), 1427–1443. https://doi.org/10.1007/s11590-021-01781-6 doi: 10.1007/s11590-021-01781-6
    [35] W. Li, H. Zheng, A preconditioned modulus-based iteration method for solving linear complementarity problems of H-matrices, Linear Multilinear Algebra, 64 (2016), 1390–1403. https://doi.org/10.1080/03081087.2015.1087457 doi: 10.1080/03081087.2015.1087457
    [36] H. Zheng, J. Luo, A preconditioned two-step modulus-based matrix splitting iteration method for linear complementarity problems of H-matrices, Math. Numer. Sin., 40 (2018), 24–32. (in Chinese)
    [37] H. Ren, X. Wang, X.-B. Tang, T. Wang, A preconditioned general two-step modulus-based matrix splitting iteration method for linear complementarity problems of H+-matrices, Numer. Algorithms, 82 (2019), 969–986. https://doi.org/10.1007/s11075-018-0637-5 doi: 10.1007/s11075-018-0637-5
    [38] L.-L. Zhang, Two-step modulus-based matrix splitting iteration method for linear complementarity problems, Numer. Algorithms, 57 (2011), 83–99. https://doi.org/10.1007/s11075-010-9416-7 doi: 10.1007/s11075-010-9416-7
    [39] X.-P. Wu, X.-F. Peng, W. Li, A preconditioned general modulus-based matrix splitting iteration method for linear complementarity problems of H-matrices, Numer. Algorithms, 79 (2018), 1131–1146. https://doi.org/10.1007/s11075-018-0477-3 doi: 10.1007/s11075-018-0477-3
    [40] P.-F. Dai, J.-C. Li, J.-C. Bai, J.-M. Qiu, A preconditioned two-step modulus-based matrix splitting iteration method for linear complementarity problem, Appl. Math. Comput., 348 (2019), 542–551. https://doi.org/10.1016/j.amc.2018.12.012 doi: 10.1016/j.amc.2018.12.012
    [41] Z.-Z. Bai, J.-Y. Pan, Matrix Analysis and Computations, Philadelphia(PA): SIAM, 2021. https://doi.org/10.1137/1.9781611976632
    [42] R. S. Varga, Matrix Iterative Analysis, 2nd edition, Springer Berlin, Heidelberg, 1962. https://doi.org/10.1007/978-3-642-05156-2
    [43] A. Frommer, G. Mayer, Convergence of relaxed parallel multisplitting methods, Linear Algebra Appl., 119 (1989), 141–152. https://doi.org/10.1016/0024-3795(89)90074-8 doi: 10.1016/0024-3795(89)90074-8
    [44] A. Berman, R. J. Plemmons, Nonnegative Matrices in the Mathematical Sciences, Philadelphia(PA): SIAM, 1994. https://doi.org/10.1137/1.9781611971262
    [45] O. Axelsson, Iterative Solution Methods, Cambridge University Press, 1996.
    [46] M. Neumann, R. J. Plemmons, Convergence of parallel multisplitting iterative methods for M-matrices, Linear Algebra Appl., 88–89 (1987), 559–573. https://doi.org/10.1016/0024-3795(87)90125-x doi: 10.1016/0024-3795(87)90125-x
    [47] L. Wang, Y.-Z. Song, Preconditioned AOR iterative methods for M-matrices, J. Comput. Appl. Math., 226 (2009), 114–124. https://doi.org/10.1016/j.cam.2008.05.022 doi: 10.1016/j.cam.2008.05.022
    [48] P.-F. Dai, J.-C. Li, J.-C. Bai, A preconditioned accelerated modulus-based Gauss-Seidel iterative method for linear complementarity problems, Math. Numer. Sin., 41 (2019), 308–319. (in Chinese)
    [49] G. Isac, Complementarity Problems and Variational Inequalities, Springer, Boston, MA, 2006. https://doi.org/10.1007/0-387-32900-5-2
    [50] X.-J. Shi, L. Yang, Z.-H. Huang, A fixed point method for the linear complementarity problem arising from american option pricing, Acta Math. Appl. Sin. Engl. Ser., 32 (2016), 921–932. https://doi.org/10.1007/s10255-016-0613-6 doi: 10.1007/s10255-016-0613-6
  • This article has been cited by:

    1. Muhammad Tariq, Eskandar Ameer, Amjad Ali, Hasanen A. Hammad, Fahd Jarad, Applying fixed point techniques for obtaining a positive definite solution to nonlinear matrix equations, 2023, 8, 2473-6988, 3842, 10.3934/math.2023191
    2. Mustafa Mudhesh, Aftab Hussain, Muhammad Arshad, Hamed AL-Sulami, Amjad Ali, New techniques on fixed point theorems for symmetric contraction mappings with its application, 2023, 8, 2473-6988, 9118, 10.3934/math.2023457
    3. Mustafa Mudhesh, Muhammad Arshad, Aftab Hussain, Amer Hassan Albargi, Eskandar Ameer, Seppo Hassi, The Solvability of Fourth‐Order Differential Equations under Almost Hardy–Rogers (ψ, ϕ)‐Type Multivalued Contractions in M℘‐Metric Space, 2024, 2024, 0161-1712, 10.1155/2024/6620429
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1731) PDF downloads(67) Cited by(0)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog