Research article Special Issues

The dual fuzzy matrix equations: Extended solution, algebraic solution and solution

  • Received: 14 September 2022 Revised: 10 December 2022 Accepted: 25 December 2022 Published: 12 January 2023
  • MSC : 03E72, 08A72, 26E50

  • In this paper, we propose a direct method to solve the dual fuzzy matrix equation of the form A˜X+˜B=C˜X+˜D with A, C matrices of crisp coefficients and ˜B, ˜D fuzzy number matrices. Extended solution and algebraic solution of the dual fuzzy matrix equations are defined and the relationship between them is investigated. This article focuses on the algebraic solution and a necessary and sufficient condition for the unique algebraic solution existence is given. By algebraic methods we not need to transform a dual fuzzy matrix equation into two crisp matrix equations to solve. In addition, the general dual fuzzy matrix equations and dual fuzzy linear systems are investigated based on the generalized inverses of the matrices. Especially, the solution formula and calculation method of the dual fuzzy matrix equation with triangular fuzzy number matrices are given and discussed. The effectiveness of the proposed method is illustrated with examples.

    Citation: Zengtai Gong, Jun Wu, Kun Liu. The dual fuzzy matrix equations: Extended solution, algebraic solution and solution[J]. AIMS Mathematics, 2023, 8(3): 7310-7328. doi: 10.3934/math.2023368

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  • In this paper, we propose a direct method to solve the dual fuzzy matrix equation of the form A˜X+˜B=C˜X+˜D with A, C matrices of crisp coefficients and ˜B, ˜D fuzzy number matrices. Extended solution and algebraic solution of the dual fuzzy matrix equations are defined and the relationship between them is investigated. This article focuses on the algebraic solution and a necessary and sufficient condition for the unique algebraic solution existence is given. By algebraic methods we not need to transform a dual fuzzy matrix equation into two crisp matrix equations to solve. In addition, the general dual fuzzy matrix equations and dual fuzzy linear systems are investigated based on the generalized inverses of the matrices. Especially, the solution formula and calculation method of the dual fuzzy matrix equation with triangular fuzzy number matrices are given and discussed. The effectiveness of the proposed method is illustrated with examples.



    Throughout the paper, the following notations are used. The set of real numbers, complex numbers, elliptic numbers, elliptic biquaternions are denoted by R,C,Cp, HCp, respectively. The set of all matrices on R(CporHCp) are denoted by Mm×n(R)(Mm×n(Cp)orMm×n(HCp)). For convenience, the set of all square matrices on Cp(orHCp) are denoted by Ms(Cp)(orMs(HCp)).

    In 1843, Hamilton introduced the set of real quaternions [1], which can be represented as

    H={q=q0+q1i+q2j+q3k:q0,q1,q2,q3R}

    where the quaternionic units i, j and k satisfy the equalities:

    i2=j2=k2=1,ij=ji=k,jk=kj=i,ki=ik=j. (1.1)

    There are many applications of real quaternions in various areas of science. One of these applications is related to matrix theory. In the first half of the 20th century, the real quaternion matrices began to study [2]. It is well known that linear matrix equations with their applications have been one of the main topics in matrix theory. For real quaternion matrices, Tian discussed the linear equations in [3], and gave a general method to solve them. Also, the real quaternion matrix equation XAXF=C (Lyapunov equation) is studied by Song et al. [4]. On the other hand, in [5], Song and Chen studied the real quaternion matrix equation XFAX=C (Sylvester equation). In the process of preparing this paper, we are motivated by the aforementioned studies [3,4,5]. The studies [6,7,8,9] can be suggested as some different and qualified studies on quaternion matrix equations.

    After the discovery of real quaternion algebra, Hamilton also introduced the complex quaternion algebra [10]. The set of complex quaternions is defined by

    HC={Q=Q0+Q1i+Q2j+Q3k:Q0,Q1,Q2,Q3C}

    where i,j and k satisfy the same multiplication rules given in (1.1).

    As well as real quaternions, complex quaternions have many applications in many areas of science and they have an important role to explain mathematical and physical events. In these applications of real and complex quaternions, their complex matrix representations have an important place. There can be found some interesting studies on complex matrices in [11,12,13,14].

    Recently, the elliptic biquaternion algebra, which includes the complex quaternion algebra and real quaternion algebra as special cases, has been introduced. Various studies concerned with elliptic biquaternion algebra have been presented in the literature. We refer the readers to the papers [15,16,17,18,19].

    This article is organized as follows. In section 2, we review elliptic numbers, elliptic matrices, elliptic biquaternions and elliptic biquaternion matrices. In Section 3, real representations of elliptic biquaternion matrices are obtained. In section 4, in view of these real representations, we develop a general method to study the solutions of linear matrix equations over the elliptic biquaternion algebra HCp. In section 5, we investigate the solutions of the elliptic biquaternion matrix equations XAXB=C and AXXB=C by means of this method. In section 6, we provide numerical algorithms for finding the solutions of problems which are discussed in the section 4 and section 5.

    The set of elliptic numbers is represented as

    Cp={x+Iy:x,yR,I2=p<0,pR}.

    In this number system, addition and multiplication of any elliptic numbers ω=x1+Iy1,ς=x2+Iy2Cp are defined as ω+ς=(x1+Iy1)+(x2+Iy2)=(x1+x2)+I(y1+y2) and ως=(x1+Iy1)(x2+Iy2)=(x1x2+py1y2)+I(x1y2+x2y1), respectively. As it is well known in the literature, Cp is a field under these two operations, [20]. The set of matrices, which includes m×n matrices with elliptic number entries, are investigated in [21]. In the set of m×n elliptic matrices Mm×n(Cp), ordinary matrix addition and multiplication are defined. Also, the scalar multiplication is defined as λA=λ[aij]=[λaij]Mm×n(Cp) where λCp and A=[aij]Mm×n(Cp), [21].

    The elliptic biquaternion algebra is a four dimensional vector space over the elliptic number field Cp. It is expressed by

    HCp={Q=A0+A1i+A2j+A3k:A0,A1,A2,A3Cp}

    where i,j and k are the quaternionic units which satisfy (1.1). Let Q=A0+A1i+A2j+A3k, R=B0+B1i+B2j+B3kHCp and λCp be given. Then, the operations of multiplication and addition are expressed as

    QR=[(A0B0)(A1B1)(A2B2)(A3B3)]+[(A0B1)+(A1B0)+(A2B3)(A3B2)]i+[(A0B2)(A1B3)+(A2B0)+(A3B1)]j+[(A0B3)+(A1B2)(A2B1)+(A3B0)]kQ+R=(A0+B0)+(A1+B1)i+(A2+B2)j+(A3+B3)k

    while the operation of scalar multiplication is expressed as [15]

    λQ=(λA0)+(λA1)i+(λA2)j+(λA3)k.

    The set of all m×n type matrices with elliptic biquaternion entries is denoted by Mm×n(HCp). In this set, the ordinary matrix addition and multiplication are defined. Also, the scalar multiplication is defined as

    QA=Q[aij]=[Qaij]Mm×n(HCp)

    where A=[aij]Mm×n(HCp) and QHCp. There is a faithful relation between elliptic matrices and elliptic biquaternion matrices. Where A0,A1,A2,A3Mm×n(Cp), every elliptic biquaternion matrix A=A0+A1i+A2j+A3kMm×n(HCp) has a 2m×2n elliptic representation

    ξ(A)=[A0+1|p|IA1A21|p|IA3A21|p|IA3A01|p|IA1] (2.1)

    which is determined by means of the following linear isomorphism [22]

    ξ:Mm×n(HCp)M2m×2n(Cp)A=A0+A1i+A2j+A3kξ(A)=[A0+1|p|IA1A21|p|IA3A21|p|IA3A01|p|IA1] (2.2)

    We aim to obtain the real representations of elliptic biquaternion matrices. In the next section, the representation ξ(A) will be written in a somewhat different form which is suitable for the purpose of us.

    In this section, firstly, we get the real representations of elliptic matrices. Afterwards, we obtain the real representations of elliptic biquaternion matrices which will be useful for investigating the solutions of linear matrix equations over the elliptic biquaternion algebra HCp in the next section.

    By means of the study [23] which was presented by Yaglom in 1968, we know that in the case I2=qrI(r24q<0), the transformation that is obtained by making the generalized complex number c1+Id1 correspond to the ordinary complex number c+id, where c=c1r2d1 and d=d124qr2, is an isomorphism. As it is well known, I2=p,p<0 for elliptic numbers. By taking into consideration this case, the restriction of this isomorphism is obtained as follows:

    ε:CpCc1+Id1c1+id1|p| (3.1)

    On the other hand, we know that an ordinary complex number z=a+ib has a faithful real matrix representation

    α(z)=[abba]

    which is determined by means of the following linear isomorphism

    α:CMΩ2(R)z=a+ibα(z)=[abba] (3.2)

    where MΩ2(R)={[xyyx]:x,yR}, [24].

    Now, we take into consideration the compound function δp=αε which is given as

    δp:CpMΩ2(R)ω=x+Iyδp(ω)=[xy|p|y|p|x]. (3.3)

    Since the functions ε and α are linear isomorphisms, there is no doubt that δp is a linear isomorphism. Thus, we have a faithful real matrix representation of an elliptic number ω=x+IyCp as

    δp(ω)=[xy|p|y|p|x].

    As a natural consequence of the linear isomorphism δp, the following function

    γp:Mm×n(Cp)MΩ2m×2n(R)A=A1+IA2γp(A)=[A1|p|A2|p|A2A1]

    that is expected to be a linear isomorphism can be immediately defined where MΩ2m×2n(R)={[G|p|H|p|HG]:G,HMm×n(R)}. One can see easily that this function is bijection and satisfies the following equalities

    γp(A+B)=γp(A)+γp(B),γp(AC)=γp(A)γp(C)

    for any elliptic matrices A,B,C of appropriate sizes. Thus, γp is a linear isomorphism as anticipated and we have a faithful real matrix representation of an elliptic matrix A=A1+IA2Mm×n(Cp) as

    γp(A)=[A1|p|A2|p|A2A1]. (3.4)

    On the other hand, the elliptic matrix representation of an elliptic biquaternion matrix A=A0+A1i+A2j+A3kMm×n(HCp) given in (2.1) can be written in a somewhat different form as

    ξ(A)=[A#0|p|A1A#2+|p|A3A#2+|p|A3A#0+|p|A1]+I[A0+1|p|A#1A21|p|A#3A21|p|A#3A01|p|A#1] (3.5)

    where Ai=Ai#+IAiMm×n(Cp), Ai#,AiMm×n(R), 0i3. Then, applying (3.4) to (3.5), we get 4m×4n real matrix representation of the elliptic matrix ξ(A) (in other words the real representation of the elliptic biquaternion matrix A) as follows:

    γp(ξ(A))=[A#0|p|A1A#2+|p|A3A#1|p|A0A#3+|p|A2A#2+|p|A3A#0+|p|A1A#3|p|A2A#1|p|A0A#1+|p|A0A#3|p|A2A#0|p|A1A#2+|p|A3A#3+|p|A2A#1+|p|A0A#2+|p|A3A#0+|p|A1]. (3.6)

    For convenience, let us denote γp(ξ(A)) by (A)γp where A is any elliptic biquaternion matrix. One can immediately see that the unit matrix In satisfies the equation

    (In)γp=I4n. (3.7)

    Some properties which are satisfied by the real representation are given below.

    Proposition 3.1. Let A,BMm×n(HCp), CMn×l(HCp), DMs(HCp) be arbitrary elliptic biquaternion matrices. In that case

    1. A=B(A)γp=(B)γp,

    2. (A+B)γp=(A)γp+(B)γp,(AC)γp=(A)γp(C)γp,

    3. If D is invertible, then (D)γp is invertible and (D1)γp=((D)γp)1,

    4. (A)γp=S4m1(A)γpS4n where S4t=[00It0000ItIt0000It00],t=m,n.

    Proof. Since γpε is a linear isomorphism, 1 and 2 are obvious. Also, the proof of 4 can be easily completed by direct calculation. Now we will prove 3.

    3. From the inverse property, we can write

    DD1=D1D=Is.

    Then, we get the equalities

    (D)γp(D1)γp=(DD1)γp=(Is)γp=I4s

    and

    (D1)γp(D)γp=(D1D)γp=(Is)γp=I4s

    by means of (3.7) and first two properties in this proposition. It means that (D1)γp=((D)γp)1.

    In this section, we give a general method on the solutions of linear matrix equations over the elliptic biquaternion algebra HCp with the aid of the real representations. To do so, we take into consideration the elliptic biquaternion matrix equation:

    A1XB1+...+AkXBk=C (4.1)

    where A1,...,AkMm×n(HCp),B1,...,BkMu×v(HCp),CMm×v(HCp) and XMn×u(HCp).

    Let us define the real representation of the elliptic biquaternion matrix equation (4.1) as in the following:

    (A1)γpY(B1)γp+...+(Ak)γpY(Bk)γp=(C)γp. (4.2)

    Thanks to the first two properties given in Proposition 3.1, the elliptic biquaternion matrix equation (4.1) is equivalent to the following real matrix equation

    (A1)γp(X)γp(B1)γp+...+(Ak)γp(X)γp(Bk)γp=(C)γp. (4.3)

    Hence, we have the following proposition.

    Proposition 4.1. The elliptic biquaternion matrix equation (4.1) has an elliptic biquaternion matrix solution XMn×u(HCp) if and only if the real matrix equation (4.2) has a real matrix solution Y=(X)γpM4n×4u(R).

    Theorem 4.2. Let A1,A2,...,AkMm×n(HCp),B1,B2,...,BkMu×v(HCp) and CMm×v(HCp) be given. In this case the elliptic biquaternion matrix equation (4.1) has a solution XMn×u(HCp) if and only if its real representation equation (4.2) has a solution YM4n×4u(R). In that case, if the block matrix

    Y=[Yij]4i,j=1,YijMn×u(R)

    is a solution of (4.2), then n×u elliptic biquaternion matrix

    X=(X#0+IX0)+(X#1+IX1)i+(X#2+IX2)j+(X#3+IX3)k (4.4)

    is a solution of (4.1) where

    X#0=14(Y11+Y22+Y33+Y44),X0=14|p|(Y31Y13+Y42Y24),X#1=14(Y24Y42+Y31Y13),X1=14|p|(Y44Y33+Y22Y11),X#2=14(Y21Y12+Y43Y34),X2=14|p|(Y14Y32+Y41Y23),X#3=14(Y14Y32+Y23Y41),X3=14|p|(Y34+Y12+Y43+Y21). (4.5)

    Proof. In view of the Proposition 4.1, the proof remains to show that if the block real matrix

    Y=[Yij]4i,j=1,YijMn×u(R) (4.6)

    is a solution of (4.2), in that case the elliptic biquaternion matrix, which is given in (4.4), is a solution of (4.1). When Y is a solution of (4.2), in view of the fourth property in Proposition 3.1, we have the equation

    (A1)γp(S4n1YS4u)(B1)γp+...+(Ak)γp(S4n1YS4u)(Bk)γp=(C)γp. (4.7)

    This last equation shows that S4n1YS4u is also a solution of (4.2). Then, according to the matrix theory, the following matrix

    Y=12(Y+S4n1YS4u) (4.8)

    satisfies the real matrix equation (4.2), that is, Y is another solution of (4.2). If (4.6) is substituted in (4.8), after some calculations, the equality

    Y=[JKNOLMPRNOJKPRLM] (4.9)

    is obtained where

    J=12(Y11+Y33),L=12(Y21+Y43),N=12(Y31Y13),P=12(Y41Y23)
    K=12(Y12+Y34),M=12(Y22+Y44),O=12(Y32Y14),R=12(Y42Y24).

    By taking into consideration (4.9) and (3.6), we construct the elliptic biquaternion matrix

    X=(X#0+IX0)+(X#1+IX1)i+(X#2+IX2)j+(X#3+IX3)k

    where X#i,Xi,0i3 are as in (4.5).

    Obviously, (X)γp=Y. Then, according to Proposition 4.1, the elliptic biquaternion matrix X is a solution of the equation (4.1).

    In this section, we investigate the solutions of the elliptic biquaternion matrix equations XAXB=C and AXXB=C by means of Theorem 4.2.

    For k=2, the special case of (4.1) is given by

    A1XB1+A2XB2=C (5.1)

    where A1,A2Mm×n(HCp),B1,B2Mu×v(HCp),CMm×v(HCp) and XMn×u(HCp). If B1=Iu,A2=In,m=n,u=v are taken in (5.1) and also some notation changes are made as follows: A1=A,B2=B,

    AXXB=C

    is obtained where AMn(HCp),BMu(HCp) and CMn×u(HCp). It is not difficult to see that the real representation of the last equation is

    (A)γpYY(B)γp=(C)γp.

    In view of the above derivation and Theorem 4.2, we have the following corollary:

    Corollary 5.1. Let AMn(HCp),BMu(HCp) and CMn×u(HCp). In this case the elliptic biquaternion matrix equation

    AXXB=C (5.2)

    has a solution XMn×u(HCp) if and only if the real matrix equation

    (A)γpYY(B)γp=(C)γp (5.3)

    has a solution YM4n×4u(R), in which case, if

    Y=[Yij]4i,j=1,YijMn×u(R)

    is a solution of (5.3), then n×u elliptic biquaternion matrix

    X=(X#0+IX0)+(X#1+IX1)i+(X#2+IX2)j+(X#3+IX3)k

    is a solution of (5.2) where X#i,Xi,0i3 are calculated as in (4.5).

    Similarly above, if A1=In,B1=Iu,m=n,u=v are taken in (5.1) and also some notation changes are made as follows: A2=A,B2=B,

    XAXB=C

    is obtained where AMn(HCp),BMu(HCp) and CMn×u(HCp). It is easy to see that the real representation of the last equation is

    Y(A)γpY(B)γp=(C)γp.

    Considering the above derivation and Theorem 4.2, we have the following corollary:

    Corollary 5.2. Let AMn(HCp),BMu(HCp) and CMn×u(HCp). In this case the elliptic biquaternion matrix equation

    XAXB=C (5.4)

    has a solution XMn×u(HCp) if and only if the real matrix equation

    Y(A)γpY(B)γp=(C)γp (5.5)

    has a solution YM4n×4u(R), in which case, if

    Y=[Yij]4i,j=1,YijMn×u(R)

    is a solution of (5.5), then n×u elliptic biquaternion matrix

    X=(X#0+IX0)+(X#1+IX1)i+(X#2+IX2)j+(X#3+IX3)k

    is a solution of (5.4) where X#i,Xi,0i3 are calculated as in (4.5).

    Based on the discussions in Section 4 and Section 5, in this section we provide numerical algorithms for finding the solutions of problems which are related to Corollary 5.1, Corollary 5.2 and Theorem 4.2.

    Note that all computations in the rest of the paper are performed on an Intel i7-3630QM@2.40 Ghz/16GB computer using MATLAB R2016a software. Another thing that can be of importance is that we use the standard MATLAB package procedures.

    Firstly, we give an example related to Corollary 5.1.

    Example 6.1. Solve elliptic biquaternion matrix equation

    [(1+I)i(2I)j00]XX[1i0k]=[(8+2I)(1+I)i+(18+2I)j2Ik(193I)(2+I)i2Ij+(253I)k0(2+I)i+Ij]

    over the elliptic biquaternion algebra HC9 with the aid of its real representation.

    By taking into consideration the Corollary 5.1, real representation of given matrix equation can be written as in the following:

    [3000100600000000003006100000000010063000000000000610003000000000]YY[1000010000000001001000010000010001001000000100000001001001000000]
    =[52212951161903000203249111651631000300035116195221290203030063177249111603020003].

    If we solve this equation, we have

    Y=[3062100303020003623003100203030010033062000303020310623003000203].

    It means that

    Y11=Y33=[3003],Y12=Y34=[6202],Y14=Y41=[0303],Y13=Y42=[1000],
    Y22=Y44=[3003],Y24=Y31=[1000],Y32=Y23=[0303],Y21=Y43=[6202].

    Consequently, it is concluded that

    X=[(1+I)i+2Ik(2+I)j0Ii+(2+I)j]M2(HC9).

    by means of the equations (4.4) and (4.5).

    We can generate an algorithm for problems related to Corollary 5.1 as in the following:

    Algorithm 1

    1. Input A,B,C(AMn(HCp),BMu(HCp) and CMn×u(HCp)).

    2. Form (A)γp,(B)γp,(C)γp.

    3. Compute Y=[Yij]4i,j=1 satisfying (5.3) (YijMn×u(R)).

    4. Calculate X#i,Xi according to (4.5)(0i3).

    5. Output X=(X#0+IX0)+(X#1+IX1)i+(X#2+IX2)j+(X#3+IX3)k.

    Now, we give an example related to Corollary 5.2.

    Example 6.2. Solve elliptic biquaternion matrix equation

    X[(1I)i000]X[100k]
    =[(1I)+(1+I)i+(I)j+(1+2I)k(12I)i+(1+I)j+(1+I)k0(I)i+5j]

    over the elliptic biquaternion algebra HC2 by using its real representation.

    If the Corollary 5.2 is taken into consideration, the real representation equation

    Y[1.4142010100000000000101.4142000100000000010001.4142010000000000010101.4142000000000]Y[1000000000000001001000000000010000001000000100000000001001000000]
    =[0.41422.82842.82840.41420.414210.41422.414201.41420500002.82842.41422.41422.82842.41420.41422.414210501.414200000.414210.41422.41420.41422.82842.82840.4142000001.4142052.41420.41422.414212.82842.41422.41422.828400000501.4142]

    can be written. By solving this equation, we have

    Y=[1.4142001.4142101.4142101.414205000001.41421.414201.41421100501.41420000101.414211.4142001.4142000001.4142051.414211001.41421.4142000000501.4142].

    It means that

    Y11=Y33=[1.4142001.4142],Y12=Y34=[01.414205],Y14=[1.4142100],
    Y13=Y42=[1000],Y21=Y43=[01.414205],Y23=[1.4142100],Y41=[1.4142100],
    Y22=Y44=[1.4142001.4142],Y24=Y31=[1000],Y32=[1.4142100].

    Consequently, we obtain

    X=[(1+0.9995I)i+(0.9995I)j(1+0.9995I)k0(0.9995I)i+5j]M2(HC2)

    by means of the equations (4.4) and (4.5).

    We can generate an algorithm for problems related to Corollary 5.2 as follows:

    Algorithm 2

    1. Input A,B,C(AMn(HCp),BMu(HCp) and CMn×u(HCp)).

    2. Form (A)γp,(B)γp,(C)γp.

    3. Compute Y=[Yij]4i,j=1 satisfying (5.5) (YijMn×u(R)).

    4. Calculate X#i,Xi according to (4.5)(0i3).

    5. Output X=(X#0+IX0)+(X#1+IX1)i+(X#2+IX2)j+(X#3+IX3)k.

    As a result of using the standard MATLAB package procedures, when our calculations include the rational numbers, root numbers, exponential expressions, logarithmic expressions etc., our method gives an approximate solution of the desired equation just like in the case of Example 6.2. Otherwise, our method gives the exact solution of the desired equation just like in the case of Example 6.1. To ensure the exact solution of the desired equation in Example 6.2, one can use the MATLAB package Symbolic Math Toolbox. If the same steps are followed by using this package, the exact solution

    X=[(1+I)i+(I)j(1+I)k0(I)i+5j]M2(HC2)

    of the aforementioned elliptic biquaternion matrix equation is immediately found. It must be noted that using this package always provides an advantage in terms of the exact solution, however using it sometimes causes a disadvantage in terms of the length of the solution.

    Finally, we can give an algorithm for the most general case, that is, for the problems related to Theorem 4.2.

    Algorithm 3

    1. Input Ai,Bi,C(AiMm×n(HCp),BiMu×v(HCp),1ik and CMm×v(HCp)).

    2. Form (Ai)γp,(Bi)γp,(C)γp(1ik).

    3. Compute Y=[Yij]4i,j=1 satisfying (4.2) (YijMn×u(R)).

    4. Calculate X#i,Xi according to (4.5)(0i3).

    5. Output X=(X#0+IX0)+(X#1+IX1)i+(X#2+IX2)j+(X#3+IX3)k.

    In this paper, real representations of elliptic biquaternion matrices, which may be needed to investigate various topics on elliptic biquaternion matrices in the future, are obtained. By means of these representations, a general method on the solutions of linear matrix equations over the elliptic biquaternion algebra HCp is developed. Also, some problems are considered as applications of this method. Lastly, the numerical algorithms for finding the solutions of these problems are provided.

    When p=1 the number system Cp and the set of elliptic biquaternions HCp correspond to the complex number system C and the set of complex quaternions HC, respectively. As a natural consequence of this case, the set of elliptic biquaternion matrices Mm×n(HCp) is reduced to set of complex quaternion matrices Mm×n(HC) when p=1. Therefore, our method solves the linear equations of complex quaternion matrices as well.

    Real or complex quaternion matrices have an important role in many areas of science. Since elliptic biquaternion matrices are generalized form of complex quaternion matrices and so real quaternion matrices, it is expected that the results obtained here will be used as a valuable tool in many areas of science.

    The author would like to thank to Professor Murat Tosun and Assistant Professor Hidayet Hüda Kösal for their help and useful discussions.

    The author declares that there is no conflict of interest.



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