This study focused on the dynamical behavior analysis of a discrete fractional Leslie-Gower model incorporating antipredator behavior and a Holling type Ⅱ functional response. Initially, we analyzed the existence and stability of the model's positive equilibrium points. For the interior positive equilibrium points, we investigated the parameter conditions leading to period-doubling bifurcation and Neimark-Sacker bifurcation using the center manifold theorem and bifurcation theory. To effectively control the chaos resulting from these bifurcations, we proposed two chaos control strategies. Numerical simulations were conducted to validate the theoretical results. These findings may contribute to the improved management and preservation of ecological systems.
Citation: Yao Shi, Zhenyu Wang. Bifurcation analysis and chaos control of a discrete fractional-order Leslie-Gower model with fear factor[J]. AIMS Mathematics, 2024, 9(11): 30298-30319. doi: 10.3934/math.20241462
[1] | Yasmina Khiar, Esmeralda Mainar, Eduardo Royo-Amondarain, Beatriz Rubio . High relative accuracy for a Newton form of bivariate interpolation problems. AIMS Mathematics, 2025, 10(2): 3836-3847. doi: 10.3934/math.2025178 |
[2] | Chih-Hung Chang, Ya-Chu Yang, Ferhat Şah . Reversibility of linear cellular automata with intermediate boundary condition. AIMS Mathematics, 2024, 9(3): 7645-7661. doi: 10.3934/math.2024371 |
[3] | Yongge Tian . Miscellaneous reverse order laws and their equivalent facts for generalized inverses of a triple matrix product. AIMS Mathematics, 2021, 6(12): 13845-13886. doi: 10.3934/math.2021803 |
[4] | Yang Chen, Kezheng Zuo, Zhimei Fu . New characterizations of the generalized Moore-Penrose inverse of matrices. AIMS Mathematics, 2022, 7(3): 4359-4375. doi: 10.3934/math.2022242 |
[5] | Pattrawut Chansangiam, Arnon Ploymukda . Riccati equation and metric geometric means of positive semidefinite matrices involving semi-tensor products. AIMS Mathematics, 2023, 8(10): 23519-23533. doi: 10.3934/math.20231195 |
[6] | Zongcheng Li, Jin Li . Linear barycentric rational collocation method for solving a class of generalized Boussinesq equations. AIMS Mathematics, 2023, 8(8): 18141-18162. doi: 10.3934/math.2023921 |
[7] | Maolin Cheng, Bin Liu . A novel method for calculating the contribution rates of economic growth factors. AIMS Mathematics, 2023, 8(8): 18339-18353. doi: 10.3934/math.2023932 |
[8] | Wenyue Feng, Hailong Zhu . The orthogonal reflection method for the numerical solution of linear systems. AIMS Mathematics, 2025, 10(6): 12888-12899. doi: 10.3934/math.2025579 |
[9] | Junsheng Zhang, Lihua Hao, Tengyun Jiao, Lusong Que, Mingquan Wang . Mathematical morphology approach to internal defect analysis of A356 aluminum alloy wheel hubs. AIMS Mathematics, 2020, 5(4): 3256-3273. doi: 10.3934/math.2020209 |
[10] | Sizhong Zhou, Quanru Pan . The existence of subdigraphs with orthogonal factorizations in digraphs. AIMS Mathematics, 2021, 6(2): 1223-1233. doi: 10.3934/math.2021075 |
This study focused on the dynamical behavior analysis of a discrete fractional Leslie-Gower model incorporating antipredator behavior and a Holling type Ⅱ functional response. Initially, we analyzed the existence and stability of the model's positive equilibrium points. For the interior positive equilibrium points, we investigated the parameter conditions leading to period-doubling bifurcation and Neimark-Sacker bifurcation using the center manifold theorem and bifurcation theory. To effectively control the chaos resulting from these bifurcations, we proposed two chaos control strategies. Numerical simulations were conducted to validate the theoretical results. These findings may contribute to the improved management and preservation of ecological systems.
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 A∈Cm×n, r(A) denotes the rank of A∈Cm×n, R(A) denotes the range of A∈Cm×n, Im denotes the identity matrix of order m, and [A,B] denotes a row block matrix consisting of A∈Cm×n and B∈Cm×p. The Moore-Penrose generalized inverse of A∈Cm×n, denoted by A†, is the unique matrix X∈Cn×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=Im−AA† | (1.1) |
the two orthogonal projectors induced from A∈Cm×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
A⊗B=(aijB)=[a11Ba12B⋯a1nBa21Ba22B⋯a2nB⋮⋮⋱⋮am1Bam2B⋯amnB]∈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 A∈Cm×n, B∈Cp×q, C∈Cn×s, and D∈Cq×t. Then, the following equalities hold:
(A⊗B)(C⊗D)=(AC)⊗(BD), | (1.2) |
(A⊗B)∗=A∗⊗B∗, (A⊗B)†=A†⊗B†, | (1.3) |
PA⊗B=PA⊗PB, r(A⊗B)=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 A1⊗A2 can be factorized as certain ordinary products of matrices:
A1⊗A2=(A1⊗Im2)(In1⊗A2)=(Im1⊗A2)(A1⊗In2) | (1.5) |
for any A1∈Cm1×n1 and A2∈Cm2×n2, and the triple Kronecker product A1⊗A2⊗A3 can be written as
A1⊗A2⊗A3=(A1⊗Im2⊗Im3)(In1⊗A2⊗Im3)(In1⊗In2⊗A3), | (1.6) |
A1⊗A2⊗A3=(In1⊗A2⊗Im3)(In1(In1⊗In2⊗A3)(A1⊗Im2⊗Im3), | (1.7) |
A1⊗A2⊗A3=(Im1⊗Im2⊗A3)(A1⊗In2⊗In3)(Im1⊗A2⊗In3) | (1.8) |
for any A1∈Cm1×n1, A2∈Cm2×n2 and A3∈Cm3×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 A1⊗A2 and A1⊗A2⊗A3, 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 A1⊗A2 and A1⊗A2⊗A3 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 A∈Cm×n and B∈Cm×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(A∗B)+r[PAPB,PBPA], | (2.2) |
r[A,B]=r(A)+r(B)−r(A∗B)+2−1r(PAPB−PBPA), | (2.3) |
r[A,B]=r(A)+r(B)−r(A∗B)+r(P[A,B]−PA−PB+PAPB) | (2.4) |
hold. Therefore,
PAPB=PBPA⇔P[A,B]=PA+PB−PAPB⇔r(EAB)=r(B)−r(A∗B)⇔r[A,B]=r(A)+r(B)−r(A∗B)⇔R(PAPB)=R(PBPA). | (2.5) |
If PAPB=PBPA, PAPC=PCPA and PBPC=PCPB, then
P[A,B,C]=PA+PB+PC−PAPB−PAPC−PBPC+PAPBPC. | (2.6) |
Lemma 2.2. [30] Let A,B and C∈Cm×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 A1∈Cm1×n1 and A2∈Cm2×n2, and denote by
M1=A1⊗Im2, M2=Im1⊗A2 |
the two dilation expressions of A1 and A2, respectively. Then, we have the following results.
(a) The following orthogonal projector equalities hold:
PA1⊗A2=PM1PM2=PM2PM1=PA1⊗PA2. | (3.1) |
(b) The following four rank equalities hold:
r[A1⊗Im2,Im1⊗A2]=m1m2−(m1−r(A1))(m2−r(A2)), | (3.2) |
r[A1⊗Im2,Im1⊗EA2]=m1m2−(m1−r(A1))r(A2), | (3.3) |
r[EA1⊗Im2,Im1⊗A2]=m1m2−r(A1)(m2−r(A2)), | (3.4) |
r[EA1⊗Im2,Im1⊗EA2]=m1m2−r(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)(m2−r(A2)), | (3.7) |
dim(R⊥(M1)∩R(M2))=r(EM1M2)=(m1−r(A1))r(A2), | (3.8) |
dim(R⊥(M1)∩R⊥(M2))=r(EM1EM2)=(m1−r(A1))(m2−r(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(A1⊗A2), | (3.11) |
R(M1)∩R⊥(M2)=R(M1EM2)=R(EM2M1)=R(A1⊗EA2), | (3.12) |
R⊥(M1)∩R(M2)=R(EM1M2)=R(M2EM1)=R(EA1⊗A2), | (3.13) |
R⊥(M1)∩R⊥(M2)=R(EM1EM2)=R(EM2EM1)=R(EA1⊗EA2), | (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=PA1⊗PA2, | (3.16) |
PR(M1)∩R⊥(M2)=PM1EM2=PA1⊗EA2, | (3.17) |
PR⊥(M1)∩R(M2)=EM1PM2=EA1⊗PA2, | (3.18) |
PR⊥(M1)∩R⊥(M2)=EM1EM2=EA1⊗EA2, | (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[A1⊗Im2,Im1⊗A2]=PA1⊗Im2+Im1⊗PA2−PA1⊗PA2=Im1m2−EA1⊗EA2, | (3.21) |
P[A1⊗Im2,Im1⊗EA2]=PA1⊗Im2+Im1⊗EA2−PA1⊗EA2=Im1m2−EA1⊗PA2, | (3.22) |
P[EA1⊗Im2,Im1⊗A2]=EA1⊗Im2+Im1⊗PA2−EA1⊗PA2=Im1m2−PA1⊗EA2, | (3.23) |
P[EA1⊗Im2,Im1⊗EA2]=EA1⊗Im2+Im1⊗EA2−EA1⊗EA2=Im1m2−PA1⊗PA2. | (3.24) |
Proof. It can be seen from (1.2) and (1.4) that
PM1PM2=(A1⊗Im2)(A1⊗Im2)†(Im1⊗A2)(Im1⊗A2)†=(A1⊗Im2)(A†1⊗Im2)(Im1⊗A2)(Im1⊗A†2)=(PA1⊗Im2)(Im1⊗PA2)=PA1⊗PA2,PM2PM1=(Im1⊗A2)(Im1⊗A2)†(A1⊗Im2)(A1⊗Im2)†=(Im1⊗A2)(Im1⊗A†2)(A1⊗Im2)(A†1⊗Im2)=(Im1⊗PA2)(PA1⊗Im2)=PA1⊗PA2, |
thus establishing (3.1).
Applying (2.1) to [A1⊗Im2,Im1⊗A2] and then simplifying by (1.2)–(1.4) yields
r[A1⊗Im2,Im1⊗A2]=r(A1⊗Im2)+r((Im1m2−(A1⊗Im2)(A1⊗Im2)†)(Im1⊗A2))=r(A1⊗Im2)+r((Im1m2−(A1⊗Im2)(A†1⊗Im2))(Im1⊗A2))=r(A1⊗Im2)+r((Im1m2−(A1A†1)⊗Im2)(Im1⊗A2))=m2r(A1)+r((Im1−A1A†1)⊗Im2)(Im1⊗A2))=m2r(A1)+r((Im1−A1A†1)⊗A2))=m2r(A1)+r(Im1−A1A†1)r(A2)=m2r(A1)+(m1−r(A1))r(A2)=m1m2−(m1−r(A1))(m2−r(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+PM2−PM1PM2=PA1⊗Im2+Im1⊗PA2−PA1⊗PA2=Im1m2−EA1⊗EA2, |
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 A1⊗Im2±Im1⊗A2 for two idempotent matrices A1 and A2, we obtain the following interesting consequences.
Theorem 3.2. Let A1∈Cm1×m1 and A2∈Cm2×m2. Then, the following rank inequality
r(A1⊗Im2+Im1⊗A2)≥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(A1⊗Im2+Im1⊗A2)=m1r(A2)+m2r(A1)−r(A1)r(A2), | (3.26) |
r(A1⊗Im2−Im1⊗A2)=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 A1⊗Im2+Im1⊗A2. Specifically, if A1=A21 and A2=A22, then it is easy to verify that (A1⊗Im2)2=A21⊗Im2=A1⊗Im2 and (Im1⊗A2)2=Im1⊗A22=Im1⊗A2 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(A−B)=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 A1⊗Im2±Im1⊗A2 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 A1∈Cm1×n1,A2∈Cm2×n2 and A3∈Cm3×n3, and let
X1=A1⊗Im2⊗Im3, X2=Im1⊗A2⊗Im3, X3=Im1⊗Im2⊗A3 | (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=PA1⊗Im2⊗Im3, PX2=Im1⊗PA2⊗Im3, PX3=Im1⊗Im2⊗PA3, | (3.29) |
the following equalities hold:
PX1PX2=PX2PX1=PA1⊗PA2⊗Im3, | (3.30) |
PX1PX3=PX3PX1=PA1⊗Im2⊗PA3, | (3.31) |
PX2PX3=PX3PX2=Im1⊗PA2⊗PA3, | (3.32) |
and the equalities hold:
PA1⊗A2⊗A3=PX1PX2PX3=PA1⊗PA2⊗PA3. | (3.33) |
(b) The following eight rank equalities hold:
r[A1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗A3] =m1m2m3−(m1−r(A1))(m2−r(A2))(m3−r(A3)), | (3.34) |
r[A1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗EA3] =m1m2m3−(m1−r(A1))(m2−r(A2))r(A3), | (3.35) |
r[A1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗A3] =m1m2m3−(m1−r(A1))r(A2)(m3−r(A3)), | (3.36) |
r[EA1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗A3] =m1m2m3−r(A1)(m2−r(A2))(m3−r(A3)), | (3.37) |
r[A1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗EA3] =m1m2m3−(m1−r(A1))r(A2)r(A3), | (3.38) |
r[EA1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗EA3] =m1m2m3−r(A1)(m2−r(A2))r(A3), | (3.39) |
r[EA1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗A3] =m1m2m3−r(A1)r(A2)(m3−r(A3)), | (3.40) |
r[EA1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗EA3]=m1m2m3−r(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)(m3−r(A3)), | (3.43) |
dim(R⊥(X1)∩R⊥(X2)∩R(X3))=r(A1)(m2−r(A2))r(A3), | (3.44) |
dim(R⊥(X1)∩R⊥(X2)∩R(X3))=(m1−r(A1))r(A2)r(A3), | (3.45) |
dim(R(X1)∩R⊥(X2)∩R⊥(X3))=r(A1)(m2−r(A2))(m3−r(A3)), | (3.46) |
dim(R⊥(X1)∩R(X2)∩R⊥(X3))=(m1−r(A1))r(A2)(m3−r(A3)), | (3.47) |
dim(R⊥(X1)∩R⊥(X2)∩R(X3))=(m1−r(A1))(m2−r(A2))r(A3), | (3.48) |
dim(R⊥(X1)∩R⊥(X2)∩R⊥(X3))=(m1−r(A1))(m2−r(A2))(m3−r(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(A1⊗A2⊗A3), | (3.51) |
R(X1)∩R(X2)∩R⊥(X3)=R(X1X2EX3)=R(A1⊗A2⊗EA3), | (3.52) |
R(X1)∩R⊥(X2)∩R(X3)=R(X1EX2X3)=R(A1⊗EA2⊗A3), | (3.53) |
R⊥(X1)∩R(X2)∩R(X3)=R(EX1X2X3)=R(EA1⊗A2⊗A3), | (3.54) |
R(X1)∩R⊥(X2)∩R⊥(X3)=R(X1EX2EX3)=R(A1⊗EA2⊗EA3), | (3.55) |
R⊥(X1)∩R(X2)∩R⊥(X3)=R(EX1X2EX3)=R(EA1⊗A2⊗EA3), | (3.56) |
R⊥(X1)∩R⊥(X2)∩R(X3)=R(X1EX2X3)=R(EA1⊗EA2⊗A3), | (3.57) |
R⊥(X1)∩R⊥(X2)∩R⊥(X3)=R(EX1EX2EX3)=R(EA1⊗EA2⊗EA3), | (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)=PA1⊗PA2⊗PA3, | (3.60) |
PR(X1)∩R(X2)∩R⊥(X3)=PA1⊗PA2⊗EA3, | (3.61) |
PR(X1)∩R⊥(X2)∩R(X3)=PA1⊗EA2⊗PA3, | (3.62) |
PR⊥(X1)∩R(X2)∩R(X3)=EA1⊗PA2⊗PA3, | (3.63) |
PR(X1)∩R⊥(X2)∩R⊥(X3)=PA1⊗EA2⊗EA3, | (3.64) |
PR⊥(X1)∩R(X2)∩R⊥(X3)=EA1⊗PA2⊗EA3, | (3.65) |
PR⊥(X1)∩R⊥(X2)∩R(X3)=EA1⊗EA2⊗PA3, | (3.66) |
PR⊥(X1)∩R⊥(X2)∩R⊥(X3)=EA1⊗EA2⊗EA3, | (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[A1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗A3]=Im1m2m3−EA1⊗EA2⊗EA3, | (3.69) |
P[A1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗EA3]=Im1m2m3−EA1⊗EA2⊗PA3, | (3.70) |
P[A1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗A3]=Im1m2m3−EA1⊗PA2⊗EA3, | (3.71) |
P[EA1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗A3]=Im1m2m3−PA1⊗EA2⊗EA3, | (3.72) |
P[A1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗EA3]=Im1m2m3−EA1⊗PA2⊗PA3, | (3.73) |
P[EA1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗EA3]=Im1m2m3−PA1⊗EA2⊗PA3, | (3.74) |
P[EA1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗A3]=Im1m2m3−PA1⊗PA2⊗EA3, | (3.75) |
P[EA1⊗Im2⊗Im3,Im1⊗EA2⊗Im3,Im1⊗Im2⊗EA3]=Im1m2m3−PA1⊗PA2⊗PA3. | (3.76) |
Proof. By (1.1)–(1.3),
PX1=(A1⊗Im2⊗Im3)(A1⊗Im2⊗Im3)†=(A1⊗Im2⊗Im3)(A†1⊗Im2⊗Im3)=(A1A†1)⊗Im2⊗Im3=PA1⊗Im2⊗Im3, |
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),
PA1⊗A2⊗A3=(A1⊗A2⊗A3)(A1⊗A2⊗A3)†=(A1⊗A2⊗A3)(A†1⊗A†2⊗A†3)=(A1A†1)⊗(A2A†2)⊗(A3A†3)=PA1⊗PA2⊗PA3, | (3.77) |
and by (1.2) and (3.29),
PX1PX2PX3=(PA1⊗Im2⊗Im3)(Im1⊗PA2⊗Im3)(Im1⊗Im2⊗PA3)=PA1⊗PA2⊗PA3. | (3.78) |
Combining (3.77) and (3.78) leads to (3.33).
By (2.1), (1.2)–(1.4) and (3.2),
r[A1⊗Im2⊗Im3,Im1⊗A2⊗Im3,Im1⊗Im2⊗A3]=r(A1⊗Im2⊗Im3)+r((Im1−A1A†1)⊗[A2⊗Im3,Im2⊗A3])=m2m3r(A1)+r(Im1−A1A†1)r[A2⊗Im3,Im2⊗A3]=m2m3r(A1)+(m1−r(A1))(m2m3−(m2−r(A2))(m3−r(A3)))=m1m2m3−(m1−r(A1))(m2−r(A2))(m3−r(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(A1⊗A2⊗Im3). |
Consequently,
R(X1)∩R(X2)∩R(X3)=R(X1X2)∩R(X3)=R(X1X2X3)=R(A1⊗A2⊗A3), |
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=Im1⊗A2⊗A3, Y2=A1⊗Im2⊗A3 and Y3=A1⊗A2⊗Im3 | (3.79) |
from any three matrices A1∈Cm1×n1,A2∈Cm2×n2 and A3∈Cm3×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=Im1⊗PA2⊗PA3, PY2=PA1⊗Im2⊗PA3 and PY3=PA1⊗PA2⊗Im3. | (3.80) |
(b) The following twelve matrix equalities hold:
PY1PY2=PY2PY1=PY1PY3=PY3PY1=PY2PY3=PY3PY2=PY1PY2PY3=PY1PY3PY2=PY2PY1PY3=PY2PY3PY1=PY3PY1PY2=PY3PY2PY1=PA1⊗PA2⊗PA3. | (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(A1⊗A2⊗A3). | (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)=PA1⊗PA2⊗PA3. | (3.85) |
(g) The following projector equality holds:
P[Y1,Y2,Y3]=Im1⊗PA2⊗PA3+PA1⊗Im2⊗PA3+PA1⊗PA2⊗Im3−2(PA1⊗PA2⊗PA3). | (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(PA1⊗PA2⊗PA3)=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(A1⊗Im2⊗Im3+Im1⊗A2⊗Im3+Im1⊗Im2⊗A3)≥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(Im1⊗A2⊗A3+A1⊗Im2⊗A3+A1⊗A2⊗Im3)≥m1r(A2)r(A3)+m2r(A1)r(A3)+m3r(A1)r(A2)−4r(A1)r(A2)r(A3), |
respectively, where A1∈Cm1×m1,A2∈Cm2×m2 and A3∈Cm3×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.
[1] |
P. H. Leslie, Some further notes on the use of matrices in population mathematics, Biometrika, 35 (1948), 213–245. https://doi.org/10.2307/2332342 doi: 10.2307/2332342
![]() |
[2] |
P. H. Leslie, J. Gower, The properties of a stochastic model for the predator-prey type of interaction between two species, Biometrika, 47 (1960), 219–234. https://doi.org/10.2307/2333294 doi: 10.2307/2333294
![]() |
[3] |
S. Hsu, T. Huang, Global stability for a class of predator-prey systems, SIAM J. Appl. Math., 55 (1995), 763–783. https://doi.org/10.1137/S0036139993253201 doi: 10.1137/S0036139993253201
![]() |
[4] |
Y. J. Li, M. X. He, Z. Li, Dynamics of a ratio-dependent Leslie-Gower predator-prey model with Allee effect and fear effect, Math. Comput. Simulation, 201 (2022), 417–439. https://doi.org/10.1016/j.matcom.2022.05.017 doi: 10.1016/j.matcom.2022.05.017
![]() |
[5] |
M. He, Z. Li, Dynamics of a Leslie-Gower predator-pery model with square root response function and generalist predator, Appl. Math. Lett., 157 (2024), 109193. https://doi.org/10.1016/j.aml.2024.109193 doi: 10.1016/j.aml.2024.109193
![]() |
[6] |
X. Wang, L. Zanette, X. Zou, Modelling the fear effect in predator-prey interactions, J. Math. Biol., 73 (2016), 1179–1204. https://doi.org/10.1007/s00285-016-0989-1 doi: 10.1007/s00285-016-0989-1
![]() |
[7] |
S. K. Sasmal, Population dynamics with multiple Allee effects induced by fear facotrs-A mathematical study on prey-predator interactions, Appl. Math. Model., 64 (2018), 1–14. https://doi.org/10.1016/j.apm.2018.07.021 doi: 10.1016/j.apm.2018.07.021
![]() |
[8] |
H. Zhang, Y. Cai, S. Fu, W. Wang, Impact of the fear effect in a prey-predator model incorporating a prey refuge, Appl. Math. Comput., 356 (2019), 328–337. https://doi.org/10.1016/j.amc.2019.03.034 doi: 10.1016/j.amc.2019.03.034
![]() |
[9] |
Y. Xue, Impact of both-density-dependent fear effect in a Leslie-Gower predator-prey model with Beddington-DeAngelis functional response, Chaos Soliton. Fract., 185 (2024), 115055. https://doi.org/10.1016/j.chaos.2024.115055 doi: 10.1016/j.chaos.2024.115055
![]() |
[10] |
R. K. Ghaziani, J. Alidousti, A. B. Eshkaftaki, Stability and dynamics of a fractional order Leslie-Gower prey-predator model, Appl. Math. Model., 40 (2016), 2075–2086. https://doi.org/10.1016/j.apm.2015.09.014 doi: 10.1016/j.apm.2015.09.014
![]() |
[11] |
C. Maji, Impact of fear effect in a fractional-order predator-prey system incorporating constant prey refuge, Nonlinear Dyn., 107 (2022), 1329–1342. https://doi.org/10.1007/s11071-021-07031-9 doi: 10.1007/s11071-021-07031-9
![]() |
[12] |
G. R. Kumar, K. Ramesh, A. Khan, K. Lakshminarayan, T. Abdeljawad, Dynamical study of fractional order Leslie-Gower model of predator-prey with fear, Allee effect, and inter-species rivalry, Res. Control Optim., 14 (2024), 100403. https://doi.org/10.1016/j.rico.2024.100403 doi: 10.1016/j.rico.2024.100403
![]() |
[13] |
H. Zhang, A. Muhammadhaji, Dynamics of a delayed fractional-order predator-prey model with cannibalism and disease in prey, Fractal Fract., 8 (2024), 333. https://doi.org/10.3390/fractalfract8060333 doi: 10.3390/fractalfract8060333
![]() |
[14] |
M. Awadalla, J. Alahmadi, K. R. Cheneke, S. Qureshi, Fractional optimal control model and bifurcation analysis of human syncytial respiratory virus transmission dynamics, Fractal Fract., 8 (2024), 44. https://doi.org/10.3390/fractalfract8010044 doi: 10.3390/fractalfract8010044
![]() |
[15] | I. Podlubny, Fractional differential equations, London: Academic Peress, 1999. |
[16] |
Y. Shi, Y. Q. Ma, X. Ding, Dynamical behaviors in a discrete fractional-order predator-prey system, Filomat, 32 (2018), 5857–5874. https://doi.org/10.2298/FIL1817857S doi: 10.2298/FIL1817857S
![]() |
[17] |
B. Wang, X. Li, Modeling and dynamical analysis of a fractional-order predator-prey system with anti-predator behavior and a Holling type Ⅳ functional response, Fractal Fract., 7 (2023), 722. https://doi.org/10.3390/fractalfract7100722 doi: 10.3390/fractalfract7100722
![]() |
[18] |
A. Singh, V. S. Sharma, Bifurcations and chaos control in a discrete-time prey-predator model with Holling type-Ⅱ functional response and prey refuge, J. Comput. Appl. Math., 418 (2023), 114666. https://doi.org/10.1016/j.cam.2022.114666 doi: 10.1016/j.cam.2022.114666
![]() |
[19] |
M. Berkal, M. B. Almatrafi, Bifurcation and stability of two-dimensional activator-inhibitor model with fractional-order derivative, Fractal Fract., 7 (2023), 344. https://doi.org/10.3390/fractalfract7050344 doi: 10.3390/fractalfract7050344
![]() |
[20] |
R. Saadeh, A. Abbes, A. Al-Husban, A. Ouannas, G. Grassi, The fractional discrete predator-prey model: Chaos, control and synchronization, Fractal Fract., 7 (2023), 120. https://doi.org/10.3390/fractalfract7020120 doi: 10.3390/fractalfract7020120
![]() |
[21] |
Q. Din, R. A. Naseem, M. S. Shabbir, Predator-prey interaction with fear effects: Stability, bifurcation and two-parameter analysis incorporating complex and fractal behavior, Fractal Fract., 8 (2024), 221. https://doi.org/10.3390/fractalfract8040221 doi: 10.3390/fractalfract8040221
![]() |
[22] |
Q. Din, Complexity and chaos control in a discrete-time prey-predator model, Commun. Nonlinear Sci., 49 (2017), 113–134. https://doi.org/10.1016/j.cnsns.2017.01.025 doi: 10.1016/j.cnsns.2017.01.025
![]() |
[23] |
Q. Din, Bifurcation analysis and chaos control in discrete-time glycolysis models, J. Math. Chem., 56 (2018), 904–931. https://doi.org/10.1007/s10910-017-0839-4 doi: 10.1007/s10910-017-0839-4
![]() |
[24] |
Q. Din, W. Ishaque, M.A. Iqbal, U. Saeed, Modification of Nicholson-Bailey model under refuge effects with stability, bifurcation, and chaos control, J. Vib. Control, 28 (2022), 3524–3538. https://doi.org/10.1177/10775463211034021 doi: 10.1177/10775463211034021
![]() |
[25] |
W. Ishaque, Q. Din, K. Khan, R. Mabela, Dynamics of predator-prey model based on fear effect with bifurcation analysis and chaos control, Qual. Theory Dyn. Syst., 23 (2024), 26. https://doi.org/10.1007/s12346-023-00878-w doi: 10.1007/s12346-023-00878-w
![]() |
[26] | S. Wiggins, Introduction to applied nonlinear dynamical systems and chaos, 2 Eds., New York: Springer New York, 2003. https://doi.org/10.1007/b97481 |
[27] |
H. Fan, J. Tang, K. Shi, Y. Zhao, Hybrid impulsive feedback control for drive-response synchronization of fractional-order multi-link Memristive neural networks with multi-delays, Fractal Fract., 7 (2023), 495. https://doi.org/10.3390/fractalfract7070495 doi: 10.3390/fractalfract7070495
![]() |
[28] |
K. Ding, Q. Zhu, Intermittent static output feedback control for stochastic delayed-switched positive systems with only partially measurable information, IEEE Trans. Autom. Control, 68 (2023), 8150–8157. https://doi.org/10.1109/TAC.2023.3293012 doi: 10.1109/TAC.2023.3293012
![]() |