N | Error | Error [22] | N[21] | Error [21] |
9 | 5.3874×10−9 | 9.1283×10−6 | 20 | 1.2500×10−3 |
10 | 2.7917×10−9 | 2.2831×10−6 | 40 | 2.8647×10−4 |
11 | 1.5313×10−9 | 7.1562×10−7 | 80 | 6.6144×10−5 |
12 | 8.8111×10−10 | 2.6679×10−7 | 160 | 1.5345×10−5 |
Caputo-Hadamard-type fractional calculus involves the logarithmic function of an arbitrary exponent as its convolutional kernel, which causes challenges in numerical approximations. In this paper, we construct and analyze a spectral collocation approach using mapped Jacobi functions as basis functions and construct an efficient algorithm to solve systems of fractional pantograph delay differential equations involving Caputo-Hadamard fractional derivatives. What we study is the error estimates of the derived method. In addition, we tabulate numerical results to support our theoretical analysis.
Citation: M. A. Zaky, M. Babatin, M. Hammad, A. Akgül, A. S. Hendy. Efficient spectral collocation method for nonlinear systems of fractional pantograph delay differential equations[J]. AIMS Mathematics, 2024, 9(6): 15246-15262. doi: 10.3934/math.2024740
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Caputo-Hadamard-type fractional calculus involves the logarithmic function of an arbitrary exponent as its convolutional kernel, which causes challenges in numerical approximations. In this paper, we construct and analyze a spectral collocation approach using mapped Jacobi functions as basis functions and construct an efficient algorithm to solve systems of fractional pantograph delay differential equations involving Caputo-Hadamard fractional derivatives. What we study is the error estimates of the derived method. In addition, we tabulate numerical results to support our theoretical analysis.
Fractional differential equations have gained growing attention in recent years and many monographs have appeared [1,2]. The most common definitions of fractional calculus (differentiation and integration) are for Caputo, Riemann-Liouville, and Grunwald-Letnikov derivatives [3,4,5,6,7]. Compared with these two types of definitions, the Hadamard fractional calculus, which was first introduced in 1892 by Hadamard [8], did not receive much attention. The kernel of the integrand in the definition of the fractional Hadamard derivative contains a logarithmic function with an arbitrary exponent different from the Riemann-Liouville fractional derivatives. Recently, the Hadamard derivative and Hadamard-type fractional differential equations have been useful in practical problems related to mechanics and engineering, such as fracture analysis or both planar and three-dimensional elasticities [9]. Kilbas discussed Hadamard-type fractional differential equations in different spaces [10]. Recently, Ma and Li described the properties of Hadamard calculus [11] and they also proposed the definite conditions for Hadamard-type fractional differential equations.
The Caputo-Hadamard (C-H) fractional derivative is a kind of fractional derivative that is useful in describing abnormal diffusion processes, especially ultra-slow diffusion. Gohar et al. [12] studied the existence and uniqueness of the solution to Caputo-Hadamard fractional differential equations and the corresponding continuation theorem. Wang et al. [13] investigated the stability of the zero solution of a class of nonlinear Hadamard-type fractional differential systems by utilizing a new fractional comparison principle. Belbali et al. [14] discussed the existence, uniqueness, and stability of solutions for a nonlinear fractional differential system consisting of a nonlinear Caputo-Hadamard fractional initial value problem. Aljoudi et al. [15] studied a coupled system of Caputo-Hadamard-type sequential fractional differential equations supplemented with nonlocal boundary conditions involving Hadamard fractional integrals. Dhaniya et al. [16] established the existence, uniqueness, and Hyers-Ulam stability of the solution to the nonlinear Langevin fractional differential equation that involves the C-H and Caputo fractional operators, with nonperiodic and nonlocal integral boundary conditions. Beyenea et al. [17] established sufficient conditions for the existence and uniqueness of solutions to nonlinear Caputo-Hadamard fractional differential equations involving Hadamard integrals and unbounded delays. He et al. [18] considered the Hadamard and the Caputo-Hadamard fractional derivatives and the stability of related systems without and with delay.
Due to the complex form of C-H fractional operators, one often needs to find a suitable numerical scheme to approximate it, which greatly improves the efficiency of the actual calculation process. The studies on numerical methods for nonlinear C-H fractional differential equations are still in their early stages. Gohar [19] studied finite difference methods for fractional differential equations with C-H derivatives and investigated the smoothness properties of the solution. Li et al. [20] obtained the analytical solution to a certain linear fractional partial differential equation with the C-H fractional derivative by introducing a new modified Laplace transform, and derived a numerical algorithm for such kinds of equations. Fan et al. [21] proposed three kinds of numerical formulas for approximating the C-H fractional derivatives, which are called L1−2 formula, L2−1σformula, and H2N2 formula.
Most numerical methods for solving fractional differential equations are based on local difference schemes. Compared with the previous works, the main contribution of this paper is to extend the results in [22,23] by constructing and analyzing a nonlocal spectral collocation method for the following system of fractional pantograph delay differential equations:
{CHℓDρtX1(t)=g1(t,X1(t),…,XM(t),X1(qt),…,XM(qt)), t∈I,CHℓDρtX2(t)=g2(t,X1(t),…,XM(t),X1(qt),…,XM(qt)), t∈I,⋮CHℓDρtXM(t)=gM(t,X1(t),…,XM(t),X1(qt),…,XM(qt)), t∈I,Xi(t)=ˉXi(t), for qt≤ℓ i=1,2,…,M,ℓ∈(0,t), ρ∈(0,1),q∈(0,1), | (1.1) |
where gi:I×RM→R are given continuous functions, I=(ℓ,eℓ), and the C-H derivative CHℓDρt of order 0<ρ<1 is given by (2.2).
The outline of this paper is as follows: In Section 2, we introduce some necessary definitions and preliminaries. In Section 3, we construct the spectral collocation scheme. In Section 4, we provide some auxiliary lemmas. The convergence analysis is discussed in Section 5. The numerical results are provided in Section 6.
In this section, some relevant properties of the C-H fractional calculus and the logarithmic Jacobi (log J) approximation are presented.
Definition 2.1. The C-H fractional integral with order ρ>0 is defined as [24]
ℓJρzX(z)=1Γ(ρ)∫zℓκρ−1(z,w)X(w)dww,z>ℓ>0, | (2.1) |
where κ(z,w)=log(zw).
Definition 2.2. The C-H fractional differential operator of order 0<ρ<1 is given as [1]
CHℓDρzX(z)=1Γ(1−ρ)∫zℓκ−ρ(z,w)X′(w)dw. | (2.2) |
Definition 2.3. Let ρ,η>−1, I:=[ℓ,ℓe], and ℓ>0. The log J functions of order p are given by [23]
Pρ,η,ℓp(z)=Pρ,ηp(κ2(z,ℓ)−1)(η,ρ>−1, ℓ>0, ∀z∈I)=Γ(p+ρ+1)p!Γ(p+ρ+η+1)p∑k=0(pk)Γ(p+k+ρ+η+1)Γ(k+ρ+1)(κ(z,ℓ)−1)k, | (2.3) |
where Pρ,ηp(z) is the Jacobi polynomial and it is defined as
Pρ,ηp(z)=Γ(p+ρ+1)Γ(p+1+ρ+η)p!p∑k=0(pk)Γ(p+k+ρ+η+1)Γ(k+ρ+1)(z−12)k. |
We define the space of logarithmic functions of order s by
Plogs(Ω):=span{1,κ(z,ℓ),κ(z,ℓ)2,…,κ(z,ℓ)s}, |
where Ω=[ℓ,+∞), ℓ>0. Let
χρ,η,ℓ(z):=z−1κ(z,ℓ)η(1−κ(z,ℓ))ρ. | (2.4) |
We denote by L2χρ,η,ℓ(I) the weighted L2 space with the following inner product and norm:
(X,ϕ)χρ,η,ℓ=∫IX(z)ϕ(z)χρ,η,ℓ(z)dz,‖X‖χρ,η,ℓ=(X,X)1/2χρ,η,ℓ. | (2.5) |
One of the most important properties of the log J polynomials is that they are mutually orthogonal in L2χρ,η,ℓ(I), i.e.,
(Pρ,η,ℓm(z),Pρ,η,ℓj(z))χρ,η,ℓ=0,∀j≠m,‖Pρ,η,ℓj(z)‖χρ,η,ℓ=ˆθρ,ηj=Γ(j+ρ+1)Γ(j+η+1)(2j+ρ+η+1)j!Γ(j+ρ+η+1). | (2.6) |
We define the following first-order differential operator:
D1logϕ(z)=ddκ(z,ℓ)ϕ(z)=zϕ′(z), | (2.7) |
and an induction leads to
Dklogϕ(z)=k⏞D1log⋅D1log⋯D1logϕ(z). | (2.8) |
We also define the non-uniformly weighted log J Sobolev space as
Bi,ℓρ,η(I):={ϕ:Djlogϕ∈L2χρ+j,η+j,ℓ(I),0≤j≤i},i∈N, |
with
(ψ,ϕ)Bi,ℓρ,η=i∑k=0(Dklogψ,Dklogϕ)χρ+k,η+k,ℓ,‖ϕ‖Bi,ℓρ,η=(ϕ,ϕ)1/2Bi,ℓρ,η,|ϕ|Bi,ℓρ,η=‖Dilogϕ‖χρ+i,η+i,ℓ. |
For the usual shifted-weighted Jacobi Sobolev space, we define
Biρ,η(Λ):={ϕ:∂jzϕ∈L2χρ+j,η+j(Λ),0≤j≤i},i∈N, |
where χρ,η=(−z+1)ρzη with z∈Λ=[0,1] is the classical Jacobi weight function.
Assume that x0<x1<⋯<xM−1<xM in I are the roots of Pρ,η,ℓM+1(x). Let z(x)=logxℓ. Then zj:=z(xj)=logxjℓ,0≤j≤M, are zeros of Pρ,ηM+1(x), and {χi}Mi=0 are the corresponding weights.
The log J-Gauss quadrature enjoys the exactness
∫IX(z)χρ,η,ℓ(z)dz=M∑i=0X(zi)χi,∀X(z)∈Plog2M+1. | (2.9) |
Hence,
M∑k=0Pρ,η,ℓq(zk)Pρ,η,ℓj(zk)χk=ˆθρ,ηqδq,j,∀ 0≤q+j≤2M+1. | (2.10) |
For any X(ℓez)∈C(I), the log J-Gauss interpolation operator Iρ,η,ℓz,M:C(I)⟶PlogM is determined uniquely by
Iρ,η,ℓz,MX(zq)=X(zq),0≤q≤M. | (2.11) |
From the above condition, we have Iρ,η,ℓz,MX=X for all X∈PlogM. On the other hand, since Iρ,η,ℓz,MX∈PlogM, we can write
Iρ,η,ℓz,MX(x)=M∑i=0ˆXρ,η,ℓiPρ,η,ℓi(x),ˆXρ,η,ℓi=1ˆθρ,ηiM∑j=0X(xj)Pρ,η,ℓi(xj)χj, ∀X∈PlogM(I). | (2.12) |
The L∞(I) space is the set of all measurable functions that are essentially bounded. That is, functions g that are bounded almost everywhere on a set of finite measures. The essential supermom norm is used to define the norm of this space and is given as
‖g‖∞=ess supx∈I|g(x)|. |
Definition 2.4. Let A(z)=(aij(z))m×n be an (m×n) matrix function with z∈I. We consider the non-negative real-valued function
|A(z)|=m∑i=1n∑j=1|aij(z)|, | (2.13) |
and the norms
‖A‖χρ,η,ℓ:=(∫I|A(z)|2χρ,η,ℓdz)1/2,‖A‖∞:=esssupz∈I|A(z)|. | (2.14) |
Proposition 2.1. It holds for any ψ(ℓex)∈Bmρ,η(Λ), m≥1 and M+1≥m≥q≥0
‖Dqlog(ψ−Iρ,η,ℓMψ)‖χρ+q,η+q,ℓ≤c√(1+M−m)!M!Mq−(1+m)/2‖∂mx{ψ(ℓex)}‖χρ+m,η+m, | (2.15) |
and it takes the form
‖Dqlog(ψ−Iρ,η,ℓMψ)‖χρ+q,η+q,ℓ≤cMq−m‖∂mx{ψ(ℓex)}‖χρ+m,η+m, c≈1,for fixedmandM≫1. | (2.16) |
In the case of q=0,1, we can write
‖ψ−Iρ,η,ℓMψ‖χρ,η,ℓ≤cM−m‖∂mx{ψ(ℓex)}‖χρ+m,η+m, | (2.17) |
‖∂x(ψ−Iρ,η,ℓMψ)‖˜χρ,η,ℓ≤cM1−m‖∂mx{ψ(ℓex)}‖χρ+m,η+m, | (2.18) |
where ˜χρ,η,ℓ=x(1−log(xℓ))ρ+1(log(xℓ))η+1.
Lemma 2.1. [23] For any ρ,η∈(−1,−12) and for all ψ(x)∈B1,ℓρ,η(I), ψ(ξ)=0 for some ξ∈I, it holds
‖ψ‖∞≤√2‖∂xψ‖1/2˜χρ,η,ℓ‖ψ‖1/2χρ,η,ℓ. | (2.19) |
Proposition 2.2. [23] For ρ,η∈(−1,−12],
‖ψ−Iρ,η,ℓMψ‖∞≤cM1/2−m‖∂mxψ(ℓex)‖χρ+m,η+m,∀ψ(ℓex)∈Bmρ,η(Λ), m≥1. | (2.20) |
Lemma 2.2. [23]
‖Iρ,η,ℓM‖∞:=maxx∈IM∑j=0|hρ,η,ℓj(x)|={O(logM),−1<ρ,η≤−12,O(Mμ+12),μ=max(ρ,η), otherwise, | (2.21) |
where {hρ,η,ℓj(x)}Mj=0 are the logarithmic Lagrange interpolation functions that are related to Pρ,η,ℓM+1(x).
To begin with, we rewrite the differential equation (1.1) in the following equivalent compact integral form:
Z(t)=Zℓ+1Γ(ρ)∫tℓ(κ(t,s))ρ−1Q(s,Z(s),Z(qs))dss, t∈(ℓ,eℓ], | (3.1) |
where
Z(t)=[X1(t),X2(t),…,XM(t)]T,Zℓ=[ˉX1(ℓ),ˉX2(ℓ),…,ˉXM(ℓ)]T,Z(qt)={[X1(qt),X2(qt),…,XM(qt)]T,ifqt>ℓ,[ˉX1(qt),ˉX2(qt),…,ˉXM(qt)]T,ifqt≤ℓ,Q(t)=[g1,g2,…,gM]T. |
In the following, we will make some useful transformations, which in turn are the basis for the numerical solution scheme and its numerical analysis. In order to convert the integral interval (ℓ,t) to I, we consider
κ(s,ℓ)=κ(t,ℓ)κ(r,ℓ), |
or
s=s(t,r)=ℓ(rℓ)κ(t,ℓ). |
Hence, the system(3.1) becomes
Z(t)=Zℓ+(κ(t,ℓ))ρΓ(ρ)∫I(1−κ(r,ℓ))ρ−1G(s(t,r),Z(s(t,r)),Z(qs(t,r)))drr. | (3.2) |
The non-polynomial spectral collocation scheme for (3.2) is to find Xm,N(t)∈PlogN(I), m=1,2,…,M such that
ZN(t)=Zℓ+1Γ(ρ)I0,0,ℓt,N(κ(t,ℓ))ρ∫Ir−1(1−κ(r,ℓ))ρ−1Iρ−1,0,ℓr,NQ(s(t,r),ZN(s(t,r)),ZN(qs(t,r)))dr, | (3.3) |
where
ZN(t)=[X1,N,X2,N,…,XM,N]T, |
and Iρ,η,ℓz,N the log J-Gauss interpolation operator in the z-direction. For simplicity, we will consider the trial functions as
Xm,N(t)=N∑i=0Xm,iP0,0,ℓi(t),m=1,…,M. | (3.4) |
Also, we can use the following approximation:
I0,0,ℓt,NIρ−1,0,ℓr,N(κ(t,ℓ))ρgm(s(t,r),ZN(s(t,r)),ZN(qs(t,r)))=N∑i=0N∑j=0vm,i,jP0,0,ℓi(t)Pρ−1,0,ℓj(r),m=1,…,M. | (3.5) |
A straightforward calculation by using (3.5) and (2.6) gives
1Γ(ρ)I0,0,ℓt,N[(κ(t,ℓ))ρ∫Ir−1(1−κ(r,ℓ))ρ−1Iρ−1,0,ℓr,Ngm(s(t,r),ZN(s(t,r)),ZN(qs(t,r)))dr]=1Γ(ρ)N∑i=0N∑j=0vm,i,jP0,0,ℓi(t)∫Ir−1(1−κ(r,ℓ))ρ−1Pρ−1,0,ℓj(r)dr=1Γ(ρ+1)N∑i=0vm,i,0P0,0,ℓi(t),m=1,…,M. | (3.6) |
Let {χρ,η,ℓp,xρ,η,ℓp}Np=0 be the weights and the nodes of Gauss-type logarithmic Jacobi interpolation. A direct application of (3.5) and (2.12) yields
vm,i,0=ρ(2i+1)N∑p=0N∑q=0(κ(t0,0,ℓp,ℓ))ρP0,0,ℓi(t0,0,ℓp)×gm(s(t0,0,ℓp,rρ−1,0,ℓq),ZN(s(t0,0,ℓp,rρ−1,0,ℓq)),ZN(qs(t0,0,ℓp,rρ−1,0,ℓq)))χ0,0,ℓpχρ−1,0,ℓq. | (3.7) |
Hence, we deduce that
N∑i=0Xm,iP0,0,ℓi(t)=XℓP0,0,ℓ0(t)+1Γ(ρ+1)N∑i=0vm,i,0P0,0,ℓi(t). | (3.8) |
We compared the coefficients of (3.8) to get
Xm,0=Zℓ+vm,0,0Γ(ρ+1),Xm,i=vm,i,0Γ(ρ+1),1≤i≤N,m=1,…,M, | (3.9) |
where Zℓ is the vector of initial values defined in (3.1).
Here, we derive the rate of convergence of the scheme (3.3) in the L2χ0,0,ℓ-norm. Accordingly, we introduce some lemmas.
Let rρ,η,ℓi be the log J-Gauss nodes in I, and sρ,η,ℓi=s(x,rρ,η,ℓi). The mapped log J-Gauss interpolation operator x˜Iρ,η,ℓs,N:C(ℓ,x)⟶PlogN(ℓ,x) is defined by
x˜Iρ,η,ℓs,Nu(sρ,η,ℓi)=u(sρ,η,ℓi),0≤i≤N. | (4.1) |
Hence,
x˜Iρ,η,ℓs,Nu(sρ,η,ℓi)=u(sρ,η,ℓi)=u(s(x,rρ,η,ℓi))=Iρ,η,ℓr,Nu(s(x,rρ,η,ℓi)), | (4.2) |
and
x˜Iρ,η,ℓs,Nu(s)=Iρ,η,ℓr,Nu(s(x,r))|κ(r,ℓ)=κ(s,ℓ)κ(x,ℓ). | (4.3) |
Moreover, the following results can be easily derived:
∫xℓs−1(κ(x,s))ρ−1x˜Iρ−1,0,ℓs,NX(s)ds=(κ(x,ℓ))ρ∫Ir−1(1−κ(r,ℓ))ρ−1Iρ−1,0,ℓr,NX(s(x,r))dr=(κ(x,ℓ))ρN∑j=0X(s(x,rρ−1,0,ℓj))χρ−1,0,ℓj=(κ(x,ℓ))ρN∑j=0X(sρ−1,0,ℓj)χρ−1,0,ℓj. | (4.4) |
Similarly,
∫xℓs−1(κ(x,s))ρ−1(x˜Iρ−1,0,ℓs,NX(s))2ds=(κ(x,ℓ))ρN∑j=0X2(sρ−1,0,ℓj)χρ−1,0,ℓj. | (4.5) |
Then, for any 1≤s≤N+1, we have
∫xℓs−1(κ(x,s))ρ−1|(I−x˜Iρ−1,0,ℓs,N)X(s)|2ds=(κ(x,ℓ))ρ∫Ir−1(1−κ(r,ℓ))ρ−1|(I−Iρ−1,0,ℓr,N)X(s(x,r))|2dr≤cN−2m(κ(x,ℓ))ρ∫Ir−1(1−κ(r,ℓ))ρ+m−1(κ(r,ℓ))m|Dmlog,rX(s(x,r))|2dr=cN−2m∫xℓs−1(κ(x,s))ρ+m−1(κ(s,ℓ))m|Dmlog,sX(s)|2ds, | (4.6) |
where I is the identity operator.
Lemma 4.1. The following estimate holds for the error function eN(x)=Z(x)−ZN(x):
‖eN‖χ0,0,ℓ≤3∑j=1‖Ξj‖χ0,0,ℓ, | (4.7) |
where
Ξ1=Z(x)−I0,0,ℓx,NZ(x),Ξ2=I0,0,ℓx,N∫xℓR(x,s)(I−x˜Iρ−1,0,ℓs,N)Q(s,Z(s),Z(qs))ds,Ξ3=I0,0,ℓx,N∫xℓR(x,s)x˜Iρ−1,0,ℓs,N(Q(s,Z(s),Z(qs))−Q(s,ZN(s),ZN(qs)))ds, |
and R(x,s)=(Rij) with Rij=s−1(κ(x,s))ρ−1Γ(ρ)δij, i, j=1,…,M.
Proof.
‖eN‖χ0,0,ℓ≤‖Z−I0,0,ℓx,NZ‖χ0,0,ℓ+‖I0,0,ℓx,NZ−ZN‖χ0,0,ℓ. | (4.8) |
It is clear from (3.1) that
I0,0,ℓx,NZ(x)=Zℓ+1Γ(ρ)I0,0,ℓx,N∫xℓs−1(κ(x,s))ρ−1Q(s,Z(s),Z(qs))ds, | (4.9) |
and
ZN(x)=Zℓ+1Γ(ρ)I0,0,ℓx,N∫xℓs−1(κ(x,s))ρ−1x˜Iρ−1,0,ℓs,NQ(s,ZN(s),ZN(qs))ds. | (4.10) |
Subtracting (4.9) from (4.10) yields
I0,0,ℓx,NZ(x)−ZN(x)=1Γ(ρ)I0,0,ℓx,N∫xℓs−1(κ(x,s))ρ−1(Q(s,Z(s),Z(qs))−x˜Iρ−1,0,ℓs,NQ(s,ZN(s),ZN(qs)))ds, | (4.11) |
which has the form:
I0,0,ℓx,NZ(x)−ZN(x)=1Γ(ρ)I0,0,ℓx,N∫xℓs−1(κ(x,s))ρ−1(I−x˜Iρ−1,0,ℓs,N)Q(s,Z(s),Z(qs))ds+1Γ(ρ)I0,0,ℓx,N∫xℓs−1(κ(x,s))ρ−1x˜Iρ−1,0,ℓs,N(Q(s,Z(s),Z(qs))−Q(s,ZN(s),ZN(qs)))ds. | (4.12) |
Theorem 5.1. Let Z(x) be the solutions of the systems (3.1) and (3.3), respectively. Then we have the following estimate:
‖Z−ZN‖χ0,0,ℓ≤cN−m(‖DmlogZ‖2χm,m,ℓ+‖DmlogQ(x,Z(x),Z(qx))‖2χρ+m−1,m,ℓ), | (5.1) |
where 1≤m≤N+1 and N≥1.
Proof. Using Proposition 2.1, we get
‖Ξ1‖χ0,0,ℓ=‖Z−I0,0,ℓx,NZ‖χ0,0,ℓ≤cN−m‖DmlogZ‖2χm,m,ℓ≤cN−m‖∂mxZ(ℓex)‖χm,m. | (5.2) |
Using the log J-Gauss integration formula, gives
‖Ξ2‖χ0,0,ℓ=‖I0,0,ℓx,N∫xℓR(x,s)(I−x˜Iρ−1,0,ℓs,N)Q(s,Z(s),Z(qs))ds‖χ0,0,ℓ=‖M∑k=1I0,0,ℓx,N∫xℓRkk(x,s)(I−x˜Iρ−1,0,ℓs,N)gk(s,Z(s),Z(qs))ds‖χ0,0,ℓ=[∫Iχ0,0,ℓ(M∑k=1I0,0,ℓx,N∫xℓRkk(x,s)(I−x˜Iρ−1,0,ℓs,N)gk(s,Z(s),Z(qs))ds)dx]1/2=[N∑j=0χ0,0,ℓj(M∑k=1∫x0,0,ℓjℓRkk(x0,0,ℓj,s)(I−x0,0,ℓj˜Iρ−1,0,ℓs,N)gk(s,Z(s),Z(qs))ds)2]1/2≤[N∑j=0χ0,0,ℓjM∑k=1(∫x0,0,ℓjℓRkk(x0,0,ℓj,s)(I−x0,0,ℓj˜Iρ−1,0,ℓs,N)gk(s,Z(s),Z(qs))ds)2M∑k=1(1)2]1/2. |
Using Cauchy-Schwarz inequality leads to the following estimate:
‖Ξ2‖χ0,0,ℓ≤C[N∑j=0M∑k=1χ0,0,ℓj∫x0,0,ℓjℓRkk(x0,0,ℓj,s) ds∫x0,0,ℓjℓRkk(x0,0,ℓj,s)|(I−x0,0,ℓj˜Iρ−1,0,ℓs,N)gk(s,Z(s),Z(qs))|2ds]1/2≤C[N∑j=0M∑k=1χ0,0,ℓj(κ(x0,0,ℓj,ℓ))ρ∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1|(I−x0,0,ℓj˜Iρ−1,0,ℓs,N)gk(s,Z(s),Z(qs))|2ds]1/2≤C(N∑j=0χ0,0,ℓj(κ(x0,0,ℓj,ℓ))ρ)1/2(M∑k=1∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1|(I−x0,0,ℓj˜Iρ−1,0,ℓs,N)gk(s,Z(s),Z(qs))|2ds)1/2≤C(N∑j=0χ0,0,ℓj(κ(x0,0,ℓj,ℓ))ρ)1/2(∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1|(I−x0,0,ℓj˜Iρ−1,0,ℓs,N)Q(s,Z(s),Z(qs))|2ds)1/2≤cN−m[N∑j=0χ0,0,ℓj(κ(x0,0,ℓj,ℓ))ρ∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ+m−1(κ(s,ℓ))m|Dmlog,sQ(s,Z(s),Z(qs))|2ds]1/2≤cN−m‖DmlogQ(⋅,Z(⋅),Z(q⋅))‖2χρ+m−1,m,ℓ. | (5.3) |
An estimate for the term ‖E3‖χ0,0,ℓ can be obtained by using the log J-Gauss integration formula, to give
‖Ξ3‖2χ0,0,ℓ=‖R(x,s)I0,0,ℓx,N∫xℓx˜Iρ−1,0,ℓs,N(Q(s,Z(s),Z(qs))−Q(s,ZN(s),ZN(qs)))ds‖2χ0,0,ℓ=1Γ2(ρ)∫Iχ0,0,ℓ×(M∑k=1I0,0,ℓx,N∫xℓs−1(κ(x,s))ρ−1x˜Iρ−1,0,ℓs,N(gk(s,Z(s),Z(qs))−gk(s,ZN(s),ZN(qs)))ds)2dx=1Γ2(ρ)N∑j=0χ0,0,ℓj×(∫x0,0,ℓjℓs−1(κ(x,s))ρ−1M∑k=1x0,0,ℓj˜Iρ−1,0,ℓs,N(gk(s,Z(s),Z(qs))−gk(s,ZN(s),ZN(qs)))ds)2. |
Using the Cauchy-Schwarz inequality, we get
‖Ξ3‖2χ0,0,ℓ≤1Γ2(ρ)N∑j=0χ0,0,ℓj∫x0,0,ℓjℓs−1(κ(x,s))ρ−1ds×∫x0,0,ℓjℓs−1(κ(x,s))ρ−1(|M∑k=1x0,0,ℓj˜Iρ−1,0,ℓs,N(gk(s,Z(s),Z(qs))−gk(s,ZN(s),ZN(qs)))|)2ds≤1Γ2(ρ)N∑j=0χ0,0,ℓj(logxℓ)ρ∫x0,0,ℓjℓs−1(κ(x,s))ρ−1×(M∑k=1|x0,0,ℓj˜Iρ−1,0,ℓs,N(gk(s,Z(s),Z(qs))−gk(s,ZN(s),ZN(qs)))|)2ds, | (5.4) |
and using the logarithmic Jacobi-Gauss quadrature formula (4.4), we obtain
‖Ξ3‖χ0,0,ℓ≤1Γ(ρ+1)[N∑j=0ρχ0,0,ℓj(κ(x0,0,ℓj,ℓ))2ρ×N∑q=0χρ−1,0,ℓq(M∑k=1|gk(s(x0,0,ℓj,rρ−1,0,ℓq),Z(s(x0,0,ℓj,rρ−1,0,ℓq)),Z(qs(x0,0,ℓj,rρ−1,0,ℓq)))−gk(s(x0,0,ℓj,rρ−1,0,ℓq),ZN(s(x0,0,ℓj,rρ−1,0,ℓq)),ZN(qs(x0,0,ℓj,rρ−1,0,ℓq)))|)2]1/2. | (5.5) |
Using the Lipschitz condition, we obtain
‖Ξ3‖χ0,0,ℓ≤LΓ(ρ+1)×[N∑j=0ρχ0,0,ℓj(κ(x0,0,ℓj,ℓ))2ρN∑q=0(M∑i=1χρ−1,0,ℓq|Xi(s(x0,0,ℓj,rρ−1,0,ℓq))−XN,i(s(x0,0,ℓj,rρ−1,0,ℓq))|)2]1/2, | (5.6) |
using (4.5), we get
‖Ξ3‖χ0,0,ℓ≤LΓ(ρ+1)×[N∑j=0ρχ0,0,ℓj(κ(x0,0,ℓj,ℓ))ρ∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1(M∑i=1|x0,0,ℓj˜Iρ−1,0,ℓs,N(Xi(s)−XN,i(s))|)2ds]1/2.‖E3‖χ0,0,ℓ≤LΓ(ρ+1)(N∑j=0ρχ0,0,ℓj(κ(x0,0,ℓj,ℓ))ρ)1/2×max0≤j≤N(∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1(M∑i=1|x0,0,ℓj˜Iρ−1,0,ℓs,N(Xi(s)−Xi,N(s))|)2ds)1/2. | (5.7) |
For any x0,0,ℓj∈I. Let f(ρ)=(κ(x0,0,ℓj,ℓ))ρ. We note that f(ρ) is a convex function of ρ. Hence, by Jensen's inequality for all ρ∈(0,1),
f(ρ)=(1−ρ)f(0)+ρf(1). |
The above inequality yields
ρN∑j=0χ0,0,ℓj(κ(x0,0,ℓj,ℓ))ρ≤ρN∑j=0χ0,0,ℓj[1−ρ+ρ(κ(x0,0,ℓj,ℓ))]≤ρ[1−ρ+ρ∫Is−1(logxa)dx]≤ρ(1−ρ2)≤12. | (5.8) |
Hence, by using the above inequality, the triangle inequality, (4.6) and (4.5), we deduce that
‖Ξ3‖χ0,0,ℓ≤L√2Γ(ρ+1)max0≤j≤N(∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1(M∑i=1|x0,0,ℓj˜Iρ−1,0,ℓs,N(Xi(s)−XN,i(s))|)2ds)1/2≤L√2 Γ(ρ+1)×max0≤j≤N[(∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1(M∑i=1|x0,0,ℓj˜Iρ−1,0,ℓs,NXi(s)−Xi(s)|)2ds)1/2+(∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1(M∑i=1|Xi(s)−XN,i(s)|)2ds)1/2]≤cN−mmax0≤j≤N(∫xjℓ(κ(s,ℓ))m(M∑i=1|Dmlog,sXi(s)|)2ds)1/2+L√2 Γ(ρ+1)×max0≤j≤N(∫x0,0,ℓjℓs−1(κ(x0,0,ℓj,s))ρ−1(M∑i=1|Xi(s)−XN,i(s)|)2ds)1/2≤cN−m‖DmlogZ‖2χm,m,ℓ+L√2Γ(ρ+1)‖eN‖2χm,m,ℓ. | (5.9) |
Hence, a combination of (5.2), (5.3), (5.9) and the Lipschitz constant L<Γ(ρ+1) leads to the desired result.
In order to illustrate the significance of our key findings, we provide two numerical examples in this section.
Example 6.1. We consider the following initial value problem:
CH1DρtX(t)=g(x),X(1)=0, t∈(1,e), ρ∈(0,1]. | (6.1) |
Table 1 shows a comparison of the maximum absolute errors that are obtained from the method that we have presented and those given in [22] and [21]. The numerical results depict that, by using the method proposed in this paper, higher accuracy is achieved.
N | Error | Error [22] | N[21] | Error [21] |
9 | 5.3874×10−9 | 9.1283×10−6 | 20 | 1.2500×10−3 |
10 | 2.7917×10−9 | 2.2831×10−6 | 40 | 2.8647×10−4 |
11 | 1.5313×10−9 | 7.1562×10−7 | 80 | 6.6144×10−5 |
12 | 8.8111×10−10 | 2.6679×10−7 | 160 | 1.5345×10−5 |
To investigate numerically the stability of the spectral collocation scheme, we consider the initial value problem (6.1) and the following problems, whose right-hand side, the initial value, and the order of the differential operator suffer perturbations.
CH1DρtY(t)=g(x)+εg,Y(1)=0,t∈(1,e),ρ=0.5. | (6.2) |
CH1Dρ+ερtY(t)=g(x),Y(1)=0,t∈(1,e),ρ=0.5,ερ∈(−0.5,0.5). | (6.3) |
CH1DρtY(t)=g(x),Y(1)=εY0,t∈(1,e),ρ=0.5. | (6.4) |
The maximum absolute errors |XN−YN|, where XN is the numerical solution of problem (6.1) and YN is the numerical solution of the perturbed problems (6.2), (6.3), and (6.4), are displayed in Tables 2, 3, and 4, respectively. We observe that ‖XN−YN‖∞=O(εg), ‖XN−YN‖∞=O(ερ), and ‖XN−YN‖∞=O(εy0), respectively, independently of N.
N | εg=0.1 | εg=0.01 | εg=0.001 |
5 | 1.130×10−1 | 1.131×10−2 | 1.131×10−3 |
10 | 1.127×10−1 | 1.127×10−2 | 1.127×10−3 |
15 | 1.128×10−1 | 1.128×10−2 | 1.128×10−3 |
N | ερ=0.1 | ερ=0.01 | ερ=0.001 |
5 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
10 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
15 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
N | εY0=0.1 | εY0=0.01 | εY0=0.001 |
5 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
10 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
15 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
Example 6.2. We consider the following coupled system:
CH1DρtX1(t)=X22(qt)+g1(t),ρ∈(0,1),CH1DρtX2(t)=X21(qt)+g2(t),ρ∈(0,1). | (6.5) |
For this problem, the exact solution is given as
X1(t)=(logt)5+2(logt)3, |
X2(t)=−(logt)4+2(logt)3. |
We employ the proposed method to solve this problem with various N and ρ values. In Table 5, we list the errors for different values of N and ρ. The numerical results show the convergence of the scheme, which confirms our error analysis.
The errors for X1 | ||||
N | ρ=0.2 | ρ=0.4 | ρ=0.6 | ρ=0.8 |
5 | 6.8×10−8 | 4.6×10−7 | 1.3×10−6 | 2.1×10−6 |
10 | 6.7×10−10 | 6.0×10−9 | 2.4×10−8 | 5.0×10−8 |
15 | 3.9×10−11 | 4.2×10−10 | 1.9×10−9 | 4.9×10−9 |
20 | 5.0×10−12 | 6.0×10−11 | 3.1×10−10 | 8.8×10−10 |
25 | 9.9×10−13 | 1.3×10−11 | 7.5×10−11 | 2.3×10−10 |
30 | 2.6×10−13 | 7.2×10−13 | 2.3×10−11 | 7.6×10−11 |
The errors for X2 | ||||
N | ρ=0.2 | ρ=0.4 | ρ=0.6 | ρ=0.8 |
5 | 5.7×10−8 | 4.3×10−7 | 1.3×10−6 | 2.1×10−6 |
10 | 5.3×10−10 | 5.3×10−9 | 2.2×10−8 | 4.9×10−8 |
15 | 3.0×10−11 | 3.6×10−10 | 1.8×10−9 | 4.7×10−9 |
20 | 3.8×10−12 | 5.2×10−11 | 2.9×10−10 | 8.4×10−10 |
25 | 7.6×10−13 | 1.1×10−11 | 7.0×10−11 | 2.2×10−10 |
30 | 2.0×10−13 | 3.21×10−12 | 2.1×10−11 | 7.2×10−11 |
We provided a collocation spectral scheme for nonlinear systems of fractional pantograph delay differential equations. We constructed a mapped Jacobi spectral collocation scheme, described its effective implementation, and derived its convergence analysis. In addition, we provided a numerical example to support our theoretical analysis. The numerical results demonstrate the accuracy and effectiveness of the proposed scheme. We also conclude that the described technique produces very accurate results, even when employing a small number of base functions. Preserving some important mathematical properties and physical structures, such as existence, positivity preservation, the maximum principle, long-time behavior, and singular solutions, may be considered in future work [25,26].
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RP23095).
The authors assert that they do not have any known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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N | εg=0.1 | εg=0.01 | εg=0.001 |
5 | 1.130×10−1 | 1.131×10−2 | 1.131×10−3 |
10 | 1.127×10−1 | 1.127×10−2 | 1.127×10−3 |
15 | 1.128×10−1 | 1.128×10−2 | 1.128×10−3 |
N | ερ=0.1 | ερ=0.01 | ερ=0.001 |
5 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
10 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
15 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
N | εY0=0.1 | εY0=0.01 | εY0=0.001 |
5 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
10 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
15 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
The errors for X1 | ||||
N | ρ=0.2 | ρ=0.4 | ρ=0.6 | ρ=0.8 |
5 | 6.8×10−8 | 4.6×10−7 | 1.3×10−6 | 2.1×10−6 |
10 | 6.7×10−10 | 6.0×10−9 | 2.4×10−8 | 5.0×10−8 |
15 | 3.9×10−11 | 4.2×10−10 | 1.9×10−9 | 4.9×10−9 |
20 | 5.0×10−12 | 6.0×10−11 | 3.1×10−10 | 8.8×10−10 |
25 | 9.9×10−13 | 1.3×10−11 | 7.5×10−11 | 2.3×10−10 |
30 | 2.6×10−13 | 7.2×10−13 | 2.3×10−11 | 7.6×10−11 |
The errors for X2 | ||||
N | ρ=0.2 | ρ=0.4 | ρ=0.6 | ρ=0.8 |
5 | 5.7×10−8 | 4.3×10−7 | 1.3×10−6 | 2.1×10−6 |
10 | 5.3×10−10 | 5.3×10−9 | 2.2×10−8 | 4.9×10−8 |
15 | 3.0×10−11 | 3.6×10−10 | 1.8×10−9 | 4.7×10−9 |
20 | 3.8×10−12 | 5.2×10−11 | 2.9×10−10 | 8.4×10−10 |
25 | 7.6×10−13 | 1.1×10−11 | 7.0×10−11 | 2.2×10−10 |
30 | 2.0×10−13 | 3.21×10−12 | 2.1×10−11 | 7.2×10−11 |
N | Error | Error [22] | N[21] | Error [21] |
9 | 5.3874×10−9 | 9.1283×10−6 | 20 | 1.2500×10−3 |
10 | 2.7917×10−9 | 2.2831×10−6 | 40 | 2.8647×10−4 |
11 | 1.5313×10−9 | 7.1562×10−7 | 80 | 6.6144×10−5 |
12 | 8.8111×10−10 | 2.6679×10−7 | 160 | 1.5345×10−5 |
N | εg=0.1 | εg=0.01 | εg=0.001 |
5 | 1.130×10−1 | 1.131×10−2 | 1.131×10−3 |
10 | 1.127×10−1 | 1.127×10−2 | 1.127×10−3 |
15 | 1.128×10−1 | 1.128×10−2 | 1.128×10−3 |
N | ερ=0.1 | ερ=0.01 | ερ=0.001 |
5 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
10 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
15 | 1.192×10−1 | 1.249×10−2 | 1.255×10−3 |
N | εY0=0.1 | εY0=0.01 | εY0=0.001 |
5 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
10 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
15 | 1.000×10−1 | 1.000×10−2 | 1.000×10−3 |
The errors for X1 | ||||
N | ρ=0.2 | ρ=0.4 | ρ=0.6 | ρ=0.8 |
5 | 6.8×10−8 | 4.6×10−7 | 1.3×10−6 | 2.1×10−6 |
10 | 6.7×10−10 | 6.0×10−9 | 2.4×10−8 | 5.0×10−8 |
15 | 3.9×10−11 | 4.2×10−10 | 1.9×10−9 | 4.9×10−9 |
20 | 5.0×10−12 | 6.0×10−11 | 3.1×10−10 | 8.8×10−10 |
25 | 9.9×10−13 | 1.3×10−11 | 7.5×10−11 | 2.3×10−10 |
30 | 2.6×10−13 | 7.2×10−13 | 2.3×10−11 | 7.6×10−11 |
The errors for X2 | ||||
N | ρ=0.2 | ρ=0.4 | ρ=0.6 | ρ=0.8 |
5 | 5.7×10−8 | 4.3×10−7 | 1.3×10−6 | 2.1×10−6 |
10 | 5.3×10−10 | 5.3×10−9 | 2.2×10−8 | 4.9×10−8 |
15 | 3.0×10−11 | 3.6×10−10 | 1.8×10−9 | 4.7×10−9 |
20 | 3.8×10−12 | 5.2×10−11 | 2.9×10−10 | 8.4×10−10 |
25 | 7.6×10−13 | 1.1×10−11 | 7.0×10−11 | 2.2×10−10 |
30 | 2.0×10−13 | 3.21×10−12 | 2.1×10−11 | 7.2×10−11 |