Research article

Research on VIKOR group decision making using WOWA operator based on interval Pythagorean triangular fuzzy numbers

  • A new decision-making method based on interval Pythagorean triangular fuzzy numbers is proposed for fuzzy information decision-making problems, taking the advantages of interval Pythagorean fuzzy numbers and triangular fuzzy numbers into account. The VIse Kriterijumski Optimizacioni Racun (VIKOR) group decision-making method is based on the Weighted Ordered Weighted Average (WOWA) operator of interval Pythagorean triangular fuzzy numbers (IVPTFWOWA). First, this article provides the definition of the IVPTFWOWA operator and proves its degeneracy, idempotence, monotonicity, and boundedness. Second, the decision steps of the VIKOR decision method using the IVPTFWOWA operator are presented. Finally, the scientificity and effectiveness of the proposed method were verified through case studies and comparative discussions. The research results indicate that the following: (1) the IVPTFWOWA operator combines interval Pythagorean fuzzy numbers and triangular fuzzy numbers, complementing the shortcomings of the two fuzzy numbers, and can characterize fuzzy information on continuous geometry, thereby reducing decision errors caused by inaccurate and fuzzy information; (2) the VIKOR decision-making method based on the IVPTFWOWA operator applies comprehensive weights, fully considering the positional weights of the scheme attributes and the weights of raters, and fully utilizing the attribute features of decision-makers and cases; and (3) compared to other methods, there is a significant gap between the decision results obtained using this method, making it easier to identify the optimal solution.

    Citation: Jun Hu, Jie Wu, Mengzhe Wang. Research on VIKOR group decision making using WOWA operator based on interval Pythagorean triangular fuzzy numbers[J]. AIMS Mathematics, 2023, 8(11): 26237-26259. doi: 10.3934/math.20231338

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  • A new decision-making method based on interval Pythagorean triangular fuzzy numbers is proposed for fuzzy information decision-making problems, taking the advantages of interval Pythagorean fuzzy numbers and triangular fuzzy numbers into account. The VIse Kriterijumski Optimizacioni Racun (VIKOR) group decision-making method is based on the Weighted Ordered Weighted Average (WOWA) operator of interval Pythagorean triangular fuzzy numbers (IVPTFWOWA). First, this article provides the definition of the IVPTFWOWA operator and proves its degeneracy, idempotence, monotonicity, and boundedness. Second, the decision steps of the VIKOR decision method using the IVPTFWOWA operator are presented. Finally, the scientificity and effectiveness of the proposed method were verified through case studies and comparative discussions. The research results indicate that the following: (1) the IVPTFWOWA operator combines interval Pythagorean fuzzy numbers and triangular fuzzy numbers, complementing the shortcomings of the two fuzzy numbers, and can characterize fuzzy information on continuous geometry, thereby reducing decision errors caused by inaccurate and fuzzy information; (2) the VIKOR decision-making method based on the IVPTFWOWA operator applies comprehensive weights, fully considering the positional weights of the scheme attributes and the weights of raters, and fully utilizing the attribute features of decision-makers and cases; and (3) compared to other methods, there is a significant gap between the decision results obtained using this method, making it easier to identify the optimal solution.



    Throughout this article, Cm×n denotes the collection of all m×n matrices over the field of complex numbers, A denotes the conjugate transpose of ACm×n, r(A) denotes the rank of ACm×n, R(A) denotes the range of ACm×n, Im denotes the identity matrix of order m, and [A,B] denotes a row block matrix consisting of ACm×n and BCm×p. The Moore-Penrose generalized inverse of ACm×n, denoted by A, is the unique matrix XCn×m satisfying the four Penrose equations

    (1) AXA=A,  (2) XAX=X,  (3) (AX)=AX,  (4) (XA)=XA.

    Further, we denote by

    PA=AA,  EA=ImAA (1.1)

    the two orthogonal projectors induced from ACm×n. For more detailed information regarding generalized inverses of matrices, we refer the reader to [2,3,4].

    Recall that the well-known Kronecker product of any two matrices A=(aij)Cm×n and B=(bij)Cp×q is defined to be

    AB=(aijB)=[a11Ba12Ba1nBa21Ba22Ba2nBam1Bam2BamnB]Cmp×nq.

    The Kronecker product, named after German mathematician Leopold Kronecker, was classified to be a special kind of matrix operation, which has been regarded as an important matrix operation and mathematical technique. This product has wide applications in system theory, matrix calculus, matrix equations, system identification and more (cf. [1,5,6,7,8,9,10,11,12,13,14,16,18,19,20,21,23,24,25,27,28,33,34]). It has been known that the matrices operations based on Kronecker products have a series of rich and good structures and properties, and thus they have many significant applications in the research areas of both theoretical and applied mathematics. In fact, mathematicians established a variety of useful formulas and facts related to the products and used them to deal with various concrete scientific computing problems. Specifically, the basic facts on Kronecker products of matrices in the following lemma were highly appraised and recognized (cf. [15,17,21,32,34]).

    Fact 1.1. Let ACm×n, BCp×q, CCn×s, and DCq×t. Then, the following equalities hold:

          (AB)(CD)=(AC)(BD), (1.2)
    (AB)=AB,   (AB)=AB, (1.3)
    PAB=PAPB,      r(AB)=r(A)r(B). (1.4)

    In addition, the Kronecker product of matrices has a rich variety of algebraic operation properties. For example, one of the most important features is that the product A1A2 can be factorized as certain ordinary products of matrices:

    A1A2=(A1Im2)(In1A2)=(Im1A2)(A1In2) (1.5)

    for any A1Cm1×n1 and A2Cm2×n2, and the triple Kronecker product A1A2A3 can be written as

    A1A2A3=(A1Im2Im3)(In1A2Im3)(In1In2A3), (1.6)
    A1A2A3=(In1A2Im3)(In1(In1In2A3)(A1Im2Im3), (1.7)
    A1A2A3=(Im1Im2A3)(A1In2In3)(Im1A2In3) (1.8)

    for any A1Cm1×n1, A2Cm2×n2 and A3Cm3×n3, where the five matrices in the parentheses on the right hand sides of (1.5)–(1.8) are usually called the dilation expressions of the given three matrices A1, A2 and A3, and the four equalities in (1.5)–(1.8) are called the dilation factorizations of the Kronecker products A1A2 and A1A2A3, respectively. A common feature of the four matrix equalities in (1.5)–(1.8) is that they factorize Kronecker products of any two or three matrices into certain ordinary products of the dilation expressions of A1, A2 and A3. Particularly, a noticeable fact we have to point out is that the nine dilation expressions of matrices in (1.5)–(1.8) commute each other by the well-known mixed-product property in (1.2) when A1, A2 and A3 are all square matrices. It can further be imagined that there exists proper extension of the dilation factorizations to Kronecker products of multiple matrices. Although the dilation factorizations in (1.5)–(1.8) seem to be technically trivial in form, they can help deal with theoretical and computational issues regarding Kronecker products of two or three matrices through the ordinary addition and multiplication operations of matrices.

    In this article, we provide a new analysis of performances and properties of Kronecker products of matrices, as well as present a wide range of novel and explicit facts and results through the dilation factorizations described in (1.5)–(1.8) for the purpose of obtaining a deeper understanding and grasping of Kronecker products of matrices, including a number of analytical formulas for calculating ranks, dimensions, orthogonal projectors, and ranges of the dilation expressions of matrices and their algebraic operations.

    The remainder of article is organized as follows. In section two, we introduce some preliminary facts and results concerning ranks, ranges, and generalized inverses of matrices. In section three, we propose and prove a collection of useful equalities, inequalities, and formulas for calculating the ranks, dimensions, orthogonal projectors, and ranges associated with the Kronecker products A1A2 and A1A2A3 through the dilation expressions of A1, A2 and A3 and their operations. Conclusions and remarks are given in section four.

    One of the remarkable applications of generalized inverses of matrices is to establish various exact and analytical expansion formulas for calculating the ranks of partitioned matrices. As convenient and skillful tools, these matrix rank formulas can be used to deal with a wide variety of theoretical and computational issues in matrix theory and its applications (cf. [22]). In this section, we present a mixture of commonly used formulas and facts in relation to ranks of matrices and their consequences about the commutativity of two orthogonal projectors, which we shall use as analytical tools to approach miscellaneous formulas related to Kronecker products of matrices.

    Lemma 2.1. [22,29] Let ACm×n and BCm×k. Then, the following rank equalities

    r[A,B]=r(A)+r(EAB)=r(B)+r(EBA), (2.1)
    r[A,B]=r(A)+r(B)2r(AB)+r[PAPB,PBPA], (2.2)
    r[A,B]=r(A)+r(B)r(AB)+21r(PAPBPBPA), (2.3)
    r[A,B]=r(A)+r(B)r(AB)+r(P[A,B]PAPB+PAPB) (2.4)

    hold. Therefore,

    PAPB=PBPAP[A,B]=PA+PBPAPBr(EAB)=r(B)r(AB)r[A,B]=r(A)+r(B)r(AB)R(PAPB)=R(PBPA). (2.5)

    If PAPB=PBPA, PAPC=PCPA and PBPC=PCPB, then

    P[A,B,C]=PA+PB+PCPAPBPAPCPBPC+PAPBPC. (2.6)

    Lemma 2.2. [30] Let A,B and CCm×m be three idempotent matrices. Then, the following rank equality

    r[A,B,C]=r(A)+r(B)+r(C)r[AB,AC]r[BA,BC]r[CA,CB]   +r[AB,AC,BA,BC,CA,CB] (2.7)

    holds. As a special instance, if AB=BA, AC=CA and BC=CB, then

    r[A,B,C]=r(A)+r(B)+r(C)r[AB,AC]r[BA,BC]r[CA,CB]+r[AB,AC,BC]. (2.8)

    The formulas and facts in the above two lemmas belong to mathematical competencies and conceptions in ordinary linear algebra. Thus they can easily be understood and technically be utilized to establish and simplify matrix expressions and equalities consisting of matrices and their generalized inverses.

    We first establish a group of formulas and facts associated with the orthogonal projectors, ranks, dimensions, and ranges of the matrix product in (1.5).

    Theorem 3.1. Let A1Cm1×n1 and A2Cm2×n2, and denote by

    M1=A1Im2,  M2=Im1A2

    the two dilation expressions of A1 and A2, respectively. Then, we have the following results.

    (a) The following orthogonal projector equalities hold:

    PA1A2=PM1PM2=PM2PM1=PA1PA2. (3.1)

    (b) The following four rank equalities hold:

    r[A1Im2,Im1A2]=m1m2(m1r(A1))(m2r(A2)), (3.2)
    r[A1Im2,Im1EA2]=m1m2(m1r(A1))r(A2), (3.3)
    r[EA1Im2,Im1A2]=m1m2r(A1)(m2r(A2)), (3.4)
    r[EA1Im2,Im1EA2]=m1m2r(A1)r(A2), (3.5)

    and the following five dimension equalities hold:

    dim(R(M1)R(M2))=r(M1M2)=r(A1)r(A2), (3.6)
    dim(R(M1)R(M2))=r(M1EM2)=r(A1)(m2r(A2)), (3.7)
    dim(R(M1)R(M2))=r(EM1M2)=(m1r(A1))r(A2), (3.8)
    dim(R(M1)R(M2))=r(EM1EM2)=(m1r(A1))(m2r(A2)), (3.9)
    dim(R(M1)R(M2))+dim(R(M1)R(M2))+dim(R(M1)R(M2)) +dim(R(M1)R(M2))=m1m2. (3.10)

    (c) The following range equalities hold:

    R(M1)R(M2)=R(M1M2)=R(M2M1)=R(A1A2), (3.11)
    R(M1)R(M2)=R(M1EM2)=R(EM2M1)=R(A1EA2), (3.12)
    R(M1)R(M2)=R(EM1M2)=R(M2EM1)=R(EA1A2), (3.13)
    R(M1)R(M2)=R(EM1EM2)=R(EM2EM1)=R(EA1EA2), (3.14)
    (R(M1)R(M2))(R(M1)R(M2))(R(M1)R(M2))(R(M1)R(M2))=Cm1m2. (3.15)

    (d) The following orthogonal projector equalities hold:

    PR(M1)R(M2)=PM1PM2=PA1PA2, (3.16)
    PR(M1)R(M2)=PM1EM2=PA1EA2, (3.17)
    PR(M1)R(M2)=EM1PM2=EA1PA2, (3.18)
    PR(M1)R(M2)=EM1EM2=EA1EA2, (3.19)
    PR(M1)R(M2)+PR(M1)R(M2)+PR(M1)R(M2)+PR(M1)R(M2)=Im1m2. (3.20)

    (e) The following orthogonal projector equalities hold:

    P[A1Im2,Im1A2]=PA1Im2+Im1PA2PA1PA2=Im1m2EA1EA2, (3.21)
    P[A1Im2,Im1EA2]=PA1Im2+Im1EA2PA1EA2=Im1m2EA1PA2, (3.22)
    P[EA1Im2,Im1A2]=EA1Im2+Im1PA2EA1PA2=Im1m2PA1EA2, (3.23)
    P[EA1Im2,Im1EA2]=EA1Im2+Im1EA2EA1EA2=Im1m2PA1PA2. (3.24)

    Proof. It can be seen from (1.2) and (1.4) that

    PM1PM2=(A1Im2)(A1Im2)(Im1A2)(Im1A2)=(A1Im2)(A1Im2)(Im1A2)(Im1A2)=(PA1Im2)(Im1PA2)=PA1PA2,PM2PM1=(Im1A2)(Im1A2)(A1Im2)(A1Im2)=(Im1A2)(Im1A2)(A1Im2)(A1Im2)=(Im1PA2)(PA1Im2)=PA1PA2,

    thus establishing (3.1).

    Applying (2.1) to [A1Im2,Im1A2] and then simplifying by (1.2)–(1.4) yields

    r[A1Im2,Im1A2]=r(A1Im2)+r((Im1m2(A1Im2)(A1Im2))(Im1A2))=r(A1Im2)+r((Im1m2(A1Im2)(A1Im2))(Im1A2))=r(A1Im2)+r((Im1m2(A1A1)Im2)(Im1A2))=m2r(A1)+r((Im1A1A1)Im2)(Im1A2))=m2r(A1)+r((Im1A1A1)A2))=m2r(A1)+r(Im1A1A1)r(A2)=m2r(A1)+(m1r(A1))r(A2)=m1m2(m1r(A1))(m2r(A2)),

    as required for (3.2). In addition, (3.2) can be directly established by applying (2.5) to the left hand side of (3.2). Equations (3.3)–(3.5) can be obtained by a similar approach. Subsequently by (3.2),

    dim(R(M1)R(M2))=r(M1)+r(M2)r[M1,M2]=r(A1)r(A2),

    as required for (3.6). Equations (3.7)–(3.9) can be established by a similar approach. Adding (3.7)–(3.9) leads to (3.10).

    The first two equalities in (3.11) follow from (3.6), and the last two range equalities follow from (3.1).

    Equations (3.12)–(3.14) can be established by a similar approach. Adding (3.11)–(3.14) and combining with (3.10) leads to (3.15).

    Equations (3.16)–(3.19) follow from (3.11)–(3.14). Adding (3.16)–(3.19) leads to (3.20).

    Under (3.1), we find from (2.5) that

    P[M1,M2]=PM1+PM2PM1PM2=PA1Im2+Im1PA2PA1PA2=Im1m2EA1EA2,

    as required for (3.21). Equations (3.22)–(3.24) can be established by a similar approach.

    Equation (3.2) was first shown in [7]; see also [27] for some extended forms of (3.2). Obviously, Theorem 3.1 reveals many performances and properties of Kronecker products of matrices, and it is no doubt that they can be used as analysis tools to deal with various matrix equalities composed of algebraic operations of Kronecker products of matrices. For example, applying the preceding results to the Kronecker sum and difference A1Im2±Im1A2 for two idempotent matrices A1 and A2, we obtain the following interesting consequences.

    Theorem 3.2. Let A1Cm1×m1 and A2Cm2×m2. Then, the following rank inequality

    r(A1Im2+Im1A2)m1r(A2)+m2r(A1)2r(A1)r(A2) (3.25)

    holds. If A1=A21 and A2=A22, then the following two rank equalities hold:

    r(A1Im2+Im1A2)=m1r(A2)+m2r(A1)r(A1)r(A2), (3.26)
    r(A1Im2Im1A2)=m1r(A2)+m2r(A1)2r(A1)r(A2). (3.27)

    Proof. Equation (3.25) follows from applying the following well-known rank inequality (cf. [22])

    r(A+B)r[AB]+r[A,B]r(A)r(B)

    and (2.1) to A1Im2+Im1A2. Specifically, if A1=A21 and A2=A22, then it is easy to verify that (A1Im2)2=A21Im2=A1Im2 and (Im1A2)2=Im1A22=Im1A2 under A21=A1 and A22=A2. In this case, applying the following two known rank formulas

    r(A+B)=r[ABB0]r(B)=r[BAA0]r(A),r(AB)=r[AB]+r[A,B]r(A)r(B),

    where A and B are two idempotent matrices of the same size (cf. [29,31]), to A1Im2±Im1A2 and then simplifying by (2.1) and (3.2) yields (3.26) and (3.27), respectively.

    Undoubtedly, the above two theorems reveal some essential relations among the dilation forms of two matrices by Kronecker products, which demonstrate that there still exist various concrete research topics on the Kronecker product of two matrices with analytical solutions that can be proposed and obtained. As a natural and useful generalization of the preceding formulas, we next give a diverse range of results related to the three-term Kronecker products of matrices in (1.6).

    Theorem 3.3. Let A1Cm1×n1,A2Cm2×n2 and A3Cm3×n3, and let

    X1=A1Im2Im3,  X2=Im1A2Im3,  X3=Im1Im2A3 (3.28)

    denote the three dilation expressions of A1, A2 and A3, respectively. Then, we have the following results.

    (a) The following three orthogonal projector equalities hold:

    PX1=PA1Im2Im3,  PX2=Im1PA2Im3,  PX3=Im1Im2PA3, (3.29)

    the following equalities hold:

    PX1PX2=PX2PX1=PA1PA2Im3, (3.30)
    PX1PX3=PX3PX1=PA1Im2PA3, (3.31)
    PX2PX3=PX3PX2=Im1PA2PA3, (3.32)

    and the equalities hold:

    PA1A2A3=PX1PX2PX3=PA1PA2PA3. (3.33)

    (b) The following eight rank equalities hold:

    r[A1Im2Im3,Im1A2Im3,Im1Im2A3]  =m1m2m3(m1r(A1))(m2r(A2))(m3r(A3)), (3.34)
    r[A1Im2Im3,Im1A2Im3,Im1Im2EA3]  =m1m2m3(m1r(A1))(m2r(A2))r(A3), (3.35)
    r[A1Im2Im3,Im1EA2Im3,Im1Im2A3]  =m1m2m3(m1r(A1))r(A2)(m3r(A3)), (3.36)
    r[EA1Im2Im3,Im1A2Im3,Im1Im2A3]  =m1m2m3r(A1)(m2r(A2))(m3r(A3)), (3.37)
    r[A1Im2Im3,Im1EA2Im3,Im1Im2EA3]  =m1m2m3(m1r(A1))r(A2)r(A3), (3.38)
    r[EA1Im2Im3,Im1A2Im3,Im1Im2EA3]  =m1m2m3r(A1)(m2r(A2))r(A3), (3.39)
    r[EA1Im2Im3,Im1EA2Im3,Im1Im2A3]  =m1m2m3r(A1)r(A2)(m3r(A3)), (3.40)
    r[EA1Im2Im3,Im1EA2Im3,Im1Im2EA3]=m1m2m3r(A1)r(A2)r(A3), (3.41)

    the following eight dimension equalities hold:

    dim(R(X1)R(X2)R(X3))=r(A1)r(A2)r(A3), (3.42)
    dim(R(X1)R(X2)R(X3))=r(A1)r(A2)(m3r(A3)), (3.43)
    dim(R(X1)R(X2)R(X3))=r(A1)(m2r(A2))r(A3), (3.44)
    dim(R(X1)R(X2)R(X3))=(m1r(A1))r(A2)r(A3), (3.45)
    dim(R(X1)R(X2)R(X3))=r(A1)(m2r(A2))(m3r(A3)), (3.46)
    dim(R(X1)R(X2)R(X3))=(m1r(A1))r(A2)(m3r(A3)), (3.47)
    dim(R(X1)R(X2)R(X3))=(m1r(A1))(m2r(A2))r(A3), (3.48)
    dim(R(X1)R(X2)R(X3))=(m1r(A1))(m2r(A2))(m3r(A3)), (3.49)

    and the following dimension equality holds:

    dim(R(X1)R(X2)R(X3))+dim(R(X1)R(X2)R(X3))  +dim(R(X1)R(X2)R(X3))+dim(R(X1)R(X2)R(X3))  +dim(R(X1)R(X2)R(X3))+dim(R(X1)R(X2)R(X3))  +dim(R(X1)R(X2)R(X3))+dim(R(X1)R(X2)R(X3)=m1m2m3. (3.50)

    (c) The following eight groups of range equalities hold:

    R(X1)R(X2)R(X3)=R(X1X2X3)=R(A1A2A3), (3.51)
    R(X1)R(X2)R(X3)=R(X1X2EX3)=R(A1A2EA3), (3.52)
    R(X1)R(X2)R(X3)=R(X1EX2X3)=R(A1EA2A3), (3.53)
    R(X1)R(X2)R(X3)=R(EX1X2X3)=R(EA1A2A3), (3.54)
    R(X1)R(X2)R(X3)=R(X1EX2EX3)=R(A1EA2EA3), (3.55)
    R(X1)R(X2)R(X3)=R(EX1X2EX3)=R(EA1A2EA3), (3.56)
    R(X1)R(X2)R(X3)=R(X1EX2X3)=R(EA1EA2A3), (3.57)
    R(X1)R(X2)R(X3)=R(EX1EX2EX3)=R(EA1EA2EA3), (3.58)

    and the following direct sum equality holds:

    (R(X1)R(X2)R(X3))(R(X1)R(X2)R(X3))(R(X1)R(X2)R(X3))(R(X1)R(X2)R(X3))(R(X1)R(X2)R(X3))(R(X1)R(X2)R(X3))(R(X1)R(X2)R(X3))(R(X1)R(X2)R(X3))=Cm1m2m3. (3.59)

    (d) The following eight orthogonal projector equalities hold:

    PR(X1)R(X2)R(X3)=PA1PA2PA3, (3.60)
    PR(X1)R(X2)R(X3)=PA1PA2EA3, (3.61)
    PR(X1)R(X2)R(X3)=PA1EA2PA3, (3.62)
    PR(X1)R(X2)R(X3)=EA1PA2PA3, (3.63)
    PR(X1)R(X2)R(X3)=PA1EA2EA3, (3.64)
    PR(X1)R(X2)R(X3)=EA1PA2EA3, (3.65)
    PR(X1)R(X2)R(X3)=EA1EA2PA3, (3.66)
    PR(X1)R(X2)R(X3)=EA1EA2EA3, (3.67)

    and the following orthogonal projector equality holds:

    PR(X1)R(X2)R(X3)+PR(X1)R(X2)R(X3)  +PR(X1)R(X2)R(X3)+PR(X1)R(X2)R(X3)  +PR(X1)R(X2)R(X3)+PR(X1)R(X2)R(X3)  +PR(X1)R(X2)R(X3)+PR(X1)R(X2)R(X3)=Im1m2m3. (3.68)

    (e) The following eight orthogonal projector equalities hold:

    P[A1Im2Im3,Im1A2Im3,Im1Im2A3]=Im1m2m3EA1EA2EA3, (3.69)
    P[A1Im2Im3,Im1A2Im3,Im1Im2EA3]=Im1m2m3EA1EA2PA3, (3.70)
    P[A1Im2Im3,Im1EA2Im3,Im1Im2A3]=Im1m2m3EA1PA2EA3, (3.71)
    P[EA1Im2Im3,Im1A2Im3,Im1Im2A3]=Im1m2m3PA1EA2EA3, (3.72)
    P[A1Im2Im3,Im1EA2Im3,Im1Im2EA3]=Im1m2m3EA1PA2PA3, (3.73)
    P[EA1Im2Im3,Im1A2Im3,Im1Im2EA3]=Im1m2m3PA1EA2PA3, (3.74)
    P[EA1Im2Im3,Im1EA2Im3,Im1Im2A3]=Im1m2m3PA1PA2EA3, (3.75)
    P[EA1Im2Im3,Im1EA2Im3,Im1Im2EA3]=Im1m2m3PA1PA2PA3. (3.76)

    Proof. By (1.1)–(1.3),

    PX1=(A1Im2Im3)(A1Im2Im3)=(A1Im2Im3)(A1Im2Im3)=(A1A1)Im2Im3=PA1Im2Im3,

    thus establishing the first equality in (3.29). The second and third equalities in (3.29) can be shown in a similar way. Also by (1.1)–(1.3),

    PA1A2A3=(A1A2A3)(A1A2A3)=(A1A2A3)(A1A2A3)=(A1A1)(A2A2)(A3A3)=PA1PA2PA3, (3.77)

    and by (1.2) and (3.29),

    PX1PX2PX3=(PA1Im2Im3)(Im1PA2Im3)(Im1Im2PA3)=PA1PA2PA3. (3.78)

    Combining (3.77) and (3.78) leads to (3.33).

    By (2.1), (1.2)–(1.4) and (3.2),

    r[A1Im2Im3,Im1A2Im3,Im1Im2A3]=r(A1Im2Im3)+r((Im1A1A1)[A2Im3,Im2A3])=m2m3r(A1)+r(Im1A1A1)r[A2Im3,Im2A3]=m2m3r(A1)+(m1r(A1))(m2m3(m2r(A2))(m3r(A3)))=m1m2m3(m1r(A1))(m2r(A2))(m3r(A3)),

    thus establishing (3.34). Equations (3.35)–(3.41) can be established in a similar way.

    By (3.11), we are able to obtain

    R(X1)R(X2)=R(X1X2)=R(X2X1)=R(A1A2Im3).

    Consequently,

    R(X1)R(X2)R(X3)=R(X1X2)R(X3)=R(X1X2X3)=R(A1A2A3),

    as required for (3.51). Equations (3.52)–(3.58) can be established in a similar way. Adding (3.51)–(3.58) leads to (3.59).

    Taking the dimensions of both sides of (3.51)–(3.58) and applying (1.4), we obtain (3.42)–(3.50).

    Equations (3.60)–(3.68) follow from (3.51)–(3.59).

    Equations (3.69)–(3.77) follow from (2.6) and (3.30)–(3.32).

    In addition to (3.28), we can construct the following three dilation expressions

    Y1=Im1A2A3,  Y2=A1Im2A3  and  Y3=A1A2Im3 (3.79)

    from any three matrices A1Cm1×n1,A2Cm2×n2 and A3Cm3×n3. Some concrete topics on rank equalities for the dilation expressions under vector situations were considered in [26]. Below, we give a sequence of results related to the three dilation expressions.

    Theorem 3.4. Let Y1, Y2 and Y3 be the same as given in (3.79). Then, we have the following results.

    (a) The following three projector equalities hold:

    PY1=Im1PA2PA3,  PY2=PA1Im2PA3  and  PY3=PA1PA2Im3. (3.80)

    (b) The following twelve matrix equalities hold:

    PY1PY2=PY2PY1=PY1PY3=PY3PY1=PY2PY3=PY3PY2=PY1PY2PY3=PY1PY3PY2=PY2PY1PY3=PY2PY3PY1=PY3PY1PY2=PY3PY2PY1=PA1PA2PA3. (3.81)

    (c) The following rank equality holds:

    r[Y1,Y2,Y3]=m1r(A2)r(A3)+m2r(A1)r(A3)+m3r(A1)r(A2)2r(A1)r(A2)r(A3). (3.82)

    (d) The following range equality holds:

    R(Y1)R(Y2)R(Y3)=R(A1A2A3). (3.83)

    (e) The following dimension equality holds:

    dim(R(Y1)R(Y2)R(Y3))=r(A1)r(A2)r(A3). (3.84)

    (f) The following projector equality holds:

    PR(Y1)R(Y2)R(Y3)=PA1PA2PA3. (3.85)

    (g) The following projector equality holds:

    P[Y1,Y2,Y3]=Im1PA2PA3+PA1Im2PA3+PA1PA2Im32(PA1PA2PA3). (3.86)

    Proof. Equation (3.80) follows directly from (3.79), and (3.81) follows from (3.80). Since PY1, PY2 and PY3 are idempotent matrices, we find from (2.8) and (3.80) that

    r[Y1,Y2,Y3]=r[PY1,PY2,PY3]=r(PY1)+r(PY2)+r(PY3)   r[PY1PY2,PY1PY3]r[PY2PY1,PY2PY3]r[PY3PY1,PY3PY1]   +r[PY1PY2,PY1PY3,PY2PY2]=r(PY1)+r(PY2)+r(PY3)2r(PA1PA2PA3)=m1r(A2)r(A3)+m2r(A1)r(A3)+m3r(A1)r(A2)2r(A1)r(A2)r(A3),

    thus establishing (3.82). Equations (3.83)–(3.86) are left as exercises for the reader.

    There are some interesting consequences to Theorems 3.3 and 3.4. For example, applying the following well-known rank inequality (cf. [22]):

    r(A+B+C)r[ABC]+r[A,B,C]r(A)r(B)r(C)

    to the sums of matrices in (3.28) and (3.80) yields the two rank inequalities

    r(A1Im2Im3+Im1A2Im3+Im1Im2A3)m1m2r(A3)+m1m3r(A2)+m2m3r(A1)2m1r(A2)r(A3)2m2r(A1)r(A3)   2m3r(A1)r(A2)+2r(A1)r(A2)r(A3)

    and

    r(Im1A2A3+A1Im2A3+A1A2Im3)m1r(A2)r(A3)+m2r(A1)r(A3)+m3r(A1)r(A2)4r(A1)r(A2)r(A3),

    respectively, where A1Cm1×m1,A2Cm2×m2 and A3Cm3×m3.

    We presented a new analysis of the dilation factorizations of the Kronecker products of two or three matrices, and obtained a rich variety of exact formulas and facts related to ranks, dimensions, orthogonal projectors, and ranges of Kronecker products of matrices. Admittedly, it is easy to understand and utilize these resulting formulas and facts in dealing with Kronecker products of matrices under various concrete situations. Given the formulas and facts in the previous theorems, there is no doubt to say that this study clearly demonstrates significance and usefulness of the dilation factorizations of Kronecker products of matrices. Therefore, we believe that this study can bring deeper insights into performances of Kronecker products of matrices, and thereby can lead to certain advances of enabling methodology in the domain of Kronecker products. We also hope that the findings in this resultful study can be taken as fundamental facts and useful supplementary materials in matrix theory when identifying and approaching various theoretical and computational issues associated with Kronecker products of matrices.

    Moreover, the numerous formulas and facts in this article can be extended to the situations for dilation factorizations of multiple Kronecker products of matrices, which can help us a great deal in producing more impressive and useful contributions of researches related to Kronecker products of matrices and developing other relevant mathematical techniques applicable to solving practical topics. Thus, they can be taken as a reference and a source of inspiration for deep understanding and exploration of numerous performances and properties of Kronecker products of matrices.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors would like to express their sincere thanks to anonymous reviewers for their helpful comments and suggestions.

    The authors declare that they have no conflict of interest.



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