Taking into account the delayed fear induced by predators on the birth rate of prey, the counter-predation sensitiveness of prey, and the direct consumption by predators with stage structure and interference impacts, we proposed a prey-predator model with fear, Crowley-Martin functional response, stage structure and time delays. By use of the functional differential equation theory and Sotomayor's bifurcation theorem, we established some criteria of the local asymptotical stability and bifurcations of the system equilibrium points. Numerically, we validated the theoretical findings and explored the effects of fear, counter-predation sensitivity, direct predation rate and the transversion rate of the immature predator. We found that the functional response as well as the stage structure of predators affected the system stability. The fear and anti-predation sensitivity have positive and negative impacts to the system stability. Low fear level and high anti-predation sensitivity are beneficial to the system stability and the survival of prey. Meanwhile, low anti-predation sensitivity can make the system jump from one equilibrium point to another or make it oscillate between stability and instability frequently, leading to such phenomena as the bubble, or bistability. The fear and mature delays can make the system change from unstable to stable and cause chaos if they are too large. Finally, some ecological suggestions were given to overcome the negative effect induced by fear on the system stability.
Citation: Weili Kong, Yuanfu Shao. The effects of fear and delay on a predator-prey model with Crowley-Martin functional response and stage structure for predator[J]. AIMS Mathematics, 2023, 8(12): 29260-29289. doi: 10.3934/math.20231498
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Taking into account the delayed fear induced by predators on the birth rate of prey, the counter-predation sensitiveness of prey, and the direct consumption by predators with stage structure and interference impacts, we proposed a prey-predator model with fear, Crowley-Martin functional response, stage structure and time delays. By use of the functional differential equation theory and Sotomayor's bifurcation theorem, we established some criteria of the local asymptotical stability and bifurcations of the system equilibrium points. Numerically, we validated the theoretical findings and explored the effects of fear, counter-predation sensitivity, direct predation rate and the transversion rate of the immature predator. We found that the functional response as well as the stage structure of predators affected the system stability. The fear and anti-predation sensitivity have positive and negative impacts to the system stability. Low fear level and high anti-predation sensitivity are beneficial to the system stability and the survival of prey. Meanwhile, low anti-predation sensitivity can make the system jump from one equilibrium point to another or make it oscillate between stability and instability frequently, leading to such phenomena as the bubble, or bistability. The fear and mature delays can make the system change from unstable to stable and cause chaos if they are too large. Finally, some ecological suggestions were given to overcome the negative effect induced by fear on the system stability.
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] | A. Lotka, Elements physical biology, Baltimore: Williams and Wilkins, 1924. |
[2] |
V. Volterra, Fluctuations in the abundance of a species considered mathematically, Nature, 118 (1926), 558–560. http://dx.doi.org/10.1038/118558a0 doi: 10.1038/118558a0
![]() |
[3] |
J. Collings, The effects of the functional response on the bifurcation behavior of a mite predator-prey interaction model, J. Math. Biol., 36 (1997), 149–168. http://dx.doi.org/10.1007/s002850050095 doi: 10.1007/s002850050095
![]() |
[4] |
T. Kar, Modelling and analysis of a harvested prey-predator system incorporating a prey refuge, J. Comput. Appl. Math., 185 (2006), 19–33. http://dx.doi.org/10.1016/j.cam.2005.01.035 doi: 10.1016/j.cam.2005.01.035
![]() |
[5] |
Y. Huang, F. Chen, Z. Li, Stability analysis of a prey-predator model with holling type III response function incorporating a prey refuge, Appl. Math. Comput., 182 (2006), 672–683. http://dx.doi.org/10.1016/j.amc.2006.04.030 doi: 10.1016/j.amc.2006.04.030
![]() |
[6] |
J. Dawes, M. Souza, A derivation of Hollings type I, II and III functional responses in predator-prey systems, J. Theor. Biol., 327 (2017), 11–22. http://dx.doi.org/10.1016/j.jtbi.2013.02.017 doi: 10.1016/j.jtbi.2013.02.017
![]() |
[7] |
K. Antwi-Fordjour, R. Parshad, M. Beauregard, Dynamics of a predator-prey model with generalized functional response and mutual interference, Math. Biosci., 360 (2020), 108407. http://dx.doi.org/10.1016/j.mbs.2020.108407 doi: 10.1016/j.mbs.2020.108407
![]() |
[8] |
J. Roy, D. Barman, S. Alam, Role of fear in a predator-prey system with ratio-dependent functional response in deterministic and stochastic environment, Biosystems, 197 (2020), 104176. http://dx.doi.org/10.1016/j.biosystems.2020.104176 doi: 10.1016/j.biosystems.2020.104176
![]() |
[9] |
P. Panja, Dynamics of a predator-prey model with Crowley-Martin functional response, refuge on predator and harvesting of super-predator, J. Biol. Syst., 29 (2021), 631–646. http://dx.doi.org/10.1142/S0218339021500121 doi: 10.1142/S0218339021500121
![]() |
[10] |
J. Danane, Stochastic predator-prey Lévy jump model with Crowley-Martin functional response and stage structure, J. Appl. Math. Comput., 67 (2021), 41–67. http://dx.doi.org/10.1007/s12190-020-01490-w doi: 10.1007/s12190-020-01490-w
![]() |
[11] |
J. Tripathi, S. Abbas, M. Thakur, Dynamical analysis of a prey-predator model with Beddington-DeAngelis type function response incorporating a prey refuge, Nonlinear Dyn., 80 (2015), 177–196. http://dx.doi.org/10.1007/s11071-014-1859-2 doi: 10.1007/s11071-014-1859-2
![]() |
[12] |
C. Xu, P. Li, Oscillations for a delayed predator-prey modelwith Hassell-Varley-type functional response, C. R. Biol., 338 (2015), 227–240. http://dx.doi.org/10.1016/j.crvi.2015.01.002 doi: 10.1016/j.crvi.2015.01.002
![]() |
[13] |
J. Tripathi, S. Tyagi, S. Abbas, Global analysis of a delayed density dependent predator-prey model with Crowley-Martin functional response, Commun. Nonlinear Sci., 30 (2016), 45–69. http://dx.doi.org/10.1016/j.cnsns.2015.06.008 doi: 10.1016/j.cnsns.2015.06.008
![]() |
[14] |
A. De Roos, L. Persson, H. Thieme, Emergent allee effects in top predators feeding on structured prey populations, Proc. R. Soc. Lond. B, 270 (2003), 611–618. http://dx.doi.org/10.1098/rspb.2002.2286 doi: 10.1098/rspb.2002.2286
![]() |
[15] |
V. Pavlovˊa, L. Berec, Impacts of predation on dynamics of age-structured prey: Allee effects and multi-stability, Theor. Ecol., 5 (2012), 533–544. http://dx.doi.org/10.1007/s12080-011-0144-y doi: 10.1007/s12080-011-0144-y
![]() |
[16] |
J. Cui, L. Chen, W. Wang, The effect of dispersal on population growth with stage-structure, Comput. Math. Appl., 39 (2000), 91–102. http://dx.doi.org/10.1016/S0898-1221(99)00316-8 doi: 10.1016/S0898-1221(99)00316-8
![]() |
[17] |
P. Panday, N. Pal, S. Samanta, P. Tryjanowski, J. Chattopadhyay, Dynamics of a stage-structured predator-prey model: cost and benefit of fear-induced group defense, J. Theor. Biol., 528 (2021), 110846. http://dx.doi.org/10.1016/j.jtbi.2021.110846 doi: 10.1016/j.jtbi.2021.110846
![]() |
[18] |
X. Sun, H. Huo, H. Xiang, Bifurcation and stability analysis in predator-prey model with a stage-structure for predator, Nonlinear Dyn., 58 (2009), 497–513. http://dx.doi.org/10.1007/s11071-009-9495-y doi: 10.1007/s11071-009-9495-y
![]() |
[19] |
T. Kostova, J. Li, M. Friedman, Two models for competition between age classes, Math. Biosci., 157 (1999), 65–89. http://dx.doi.org/10.1016/S0025-5564(98)10077-9 doi: 10.1016/S0025-5564(98)10077-9
![]() |
[20] | R. Taylor, Predation, New York: Chapman and Hall, 1984. |
[21] |
S. Lima, L. Dill, Behavioral decisions made under the risk of predation: a review and prospectus, Can. J. Zool., 68 (1990), 619–640. http://dx.doi.org/10.1139/z90-092 doi: 10.1139/z90-092
![]() |
[22] | J. Brown, Vigilance, patch use and habitat selection: foraging under predation risk, Evol. Ecol. Res., 1 (1999), 49–71. |
[23] |
W. Cresswell, Predation in bird populations, J. Ornithol., 152 (2011), 251–263. http://dx.doi.org/10.1007/s10336-010-0638-1 doi: 10.1007/s10336-010-0638-1
![]() |
[24] |
A. Wirsing, W. Ripple, A comparison of shark and wolf research reveals similar behavioral responses by prey, Front. Ecol. Environ., 9 (2011), 335–341. http://dx.doi.org/10.1890/090226 doi: 10.1890/090226
![]() |
[25] |
S. Mortoja, P. Panja, S. Mondal, Dynamics of a predator-prey model with stage-structure on both species and anti-predator behavior, Informatics in Medicine Unlocked, 10 (2018), 50–57. http://dx.doi.org/10.1016/j.imu.2017.12.004 doi: 10.1016/j.imu.2017.12.004
![]() |
[26] | W. Ripple, R. Beschta, Wolves and the ecology of fear: can predation risk structure ecosystems? BioScience, 54 (2004), 755–766. http://dx.doi.org/10.1641/0006-3568(2004)054[0755: WATEOF]2.0.CO; 2 |
[27] |
A. Sih, Optimal behavior: can foragers balance two conflicting demands, Science, 210 (1980), 1041–1043. http://dx.doi.org/10.1126/science.210.4473.1041 doi: 10.1126/science.210.4473.1041
![]() |
[28] | B. Pierce, R. Bowyer, V. Bleich, Habitat selection by mule deer: forage benefits or risk of predation? J. Wildl. Manage., 68 (2004), 533–541. http://dx.doi.org/10.2193/0022-541X(2004)068[0533: HSBMDF]2.0.CO; 2 |
[29] |
S. Creel, D. Christianson, S. Liley, J. Winnie, Predation risk affects reproductive physiology and demography of elk, Science, 315 (2007), 960. http://dx.doi.org/10.1126/science.1135918 doi: 10.1126/science.1135918
![]() |
[30] | Y. Kuang, Delay differential equations: with applications in population dynamics, New York: Academic Press, 1993. |
[31] |
S. Gourley, Y. Kuang, A stage structured predator-prey model and its dependence on maturation delay and death rate, J. Math. Biol., 49 (2019), 188–200. http://dx.doi.org/10.1007/s00285-004-0278-2 doi: 10.1007/s00285-004-0278-2
![]() |
[32] |
N. Pal, S. Samanta, S. Biswas, M. Alquran, K. Al-Khaled, J. Chattopadhyay, Stability and bifurcation analysis of a three-species food chain model with delay, Int. J. Bifurcat. Chaos, 25 (2015), 1550123. http://dx.doi.org/10.1142/S0218127415501230 doi: 10.1142/S0218127415501230
![]() |
[33] |
S. Pal, N. Pal, S. Samanta, J. Chattopadhyay, Effect of hunting cooperation and fear in a predator-prey model, Ecol. Complex., 39 (2019), 100770. http://dx.doi.org/10.1016/j.ecocom.2019.100770 doi: 10.1016/j.ecocom.2019.100770
![]() |
[34] |
Y. Shao, Global stability of a delayed predator-prey system with fear and Holling-type II functional response in deterministic and stochastic environments, Math. Comput. Simulat., 200 (2022), 65–77. http://dx.doi.org/10.1016/j.matcom.2022.04.013 doi: 10.1016/j.matcom.2022.04.013
![]() |
[35] |
B. Kumar Das, D. Sahoo, G. Samanta, Impact of fear in a delayed-induced predator-prey system with intraspecific competition within predator species, Math. Comput. Simulat., 191 (2022), 134–156. http://dx.doi.org/10.1016/j.matcom.2021.08.005 doi: 10.1016/j.matcom.2021.08.005
![]() |
[36] |
B. Dubey, S. Ankit Kumar, Stability switching and chaos in a multiple delayed prey-predator model with fear effect and anti-predator behavor, Math. Comput. Simulat., 188 (2021), 164–192. http://dx.doi.org/10.1016/j.matcom.2021.03.037 doi: 10.1016/j.matcom.2021.03.037
![]() |
[37] |
K. Chakraborty, S. Haldar, T. Kar, Global stability and bifurcation analysis of a delay induced prey-predator system with stage structure, Nonlinear Dyn., 73 (2013), 1307–1325. http://dx.doi.org/10.1007/s11071-013-0864-1 doi: 10.1007/s11071-013-0864-1
![]() |
[38] |
S. Mortoja, P. Panja, S. Mondal, Dynamics of a predator-prey model with nonlinear incidence rate, Crowley-Martin type functional response and disease in prey population, Ecological Genetics and Genomics, 10 (2019), 100035. http://dx.doi.org/10.1016/j.egg.2018.100035 doi: 10.1016/j.egg.2018.100035
![]() |
[39] |
Y. Shao, W. Kong, A predator-prey model with Beddington-DeAngelis functional response and multiple delays in deterministic and stochastic environments, Mathematics, 10 (2022), 3378. http://dx.doi.org/10.3390/math10183378 doi: 10.3390/math10183378
![]() |
[40] |
E. Beretta, Y. Kuang, Geometric stability switch criteria in delay differential systems with delay-dependent parameters, SIAM J. Math. Anal., 33 (2002), 1144–1165. http://dx.doi.org/10.1137/S0036141000376086 doi: 10.1137/S0036141000376086
![]() |
[41] | L. Perko, Differential equations and dynamical systems, New York: Springer, 2001. http://dx.doi.org/10.1007/978-1-4613-0003-8 |
[42] | V. Kolmanovskii, A. Myshkis, Applied theory of functional differential differential equations, Dordrecht: Springer, 1992. http://dx.doi.org/10.1007/978-94-015-8084-7 |
[43] |
A. Dhooge, W. Govaerts, Y. Kuznetsov, H. Meijer, B. Sautois, New features of the software matcont for bifurcation analysis of dynamical systems, Math. Comp. Model. Dyn., 14 (2007), 147–175. http://dx.doi.org/10.1080/13873950701742754 doi: 10.1080/13873950701742754
![]() |
[44] |
Y. Zhao, S. Yuan, Stability in distribution of a stochastic hybrid competitive Lotka-Volterra model with Lévy jumps, Chaos Soliton. Fract., 85 (2016), 98–109. http://dx.doi.org/10.1016/j.chaos.2016.01.015 doi: 10.1016/j.chaos.2016.01.015
![]() |
[45] |
S. Mondal, A. Maiti, G. Samanta, Effects of fear and additional food in a delayed predator-prey model, Biophysical Reviews and Letters, 13 (2018), 157–177. http://dx.doi.org/10.1142/S1793048018500091 doi: 10.1142/S1793048018500091
![]() |
[46] |
A. Thirthar, S. Majeed, M. Alqudah, P. Panja, T. Abdeljawad, Fear effect in a predator-prey model with additional food, prey refuge and harvesting on super predator, Chaos Soliton. Fract., 159 (2022), 112091. http://dx.doi.org/10.1016/j.chaos.2022.112091 doi: 10.1016/j.chaos.2022.112091
![]() |