In this paper, a two-grid mixed finite volume element (MFVE) algorithm is presented for the nonlinear time fractional reaction-diffusion equations, where the Caputo fractional derivative is approximated by the classical L1-formula. The coarse and fine grids (containing the primal and dual grids) are constructed for the space domain, then a nonlinear MFVE scheme on the coarse grid and a linearized MFVE scheme on the fine grid are given. By using the Browder fixed point theorem and the matrix theory, the existence and uniqueness for the nonlinear and linearized MFVE schemes are obtained, respectively. Furthermore, the stability results and optimal error estimates are derived in detailed. Finally, some numerical results are given to verify the feasibility and effectiveness of the proposed algorithm.
Citation: Zhichao Fang, Ruixia Du, Hong Li, Yang Liu. A two-grid mixed finite volume element method for nonlinear time fractional reaction-diffusion equations[J]. AIMS Mathematics, 2022, 7(2): 1941-1970. doi: 10.3934/math.2022112
[1] | Raweerote Suparatulatorn, Wongthawat Liawrungrueang, Thanasak Mouktonglang, Watcharaporn Cholamjiak . An algorithm for variational inclusion problems including quasi-nonexpansive mappings with applications in osteoporosis prediction. AIMS Mathematics, 2025, 10(2): 2541-2561. doi: 10.3934/math.2025118 |
[2] | Salman khan, Muhammad Naeem, Muhammad Qiyas . Deep intelligent predictive model for the identification of diabetes. AIMS Mathematics, 2023, 8(7): 16446-16462. doi: 10.3934/math.2023840 |
[3] | Federico Divina, Miguel García-Torres, Francisco Gómez-Vela, Domingo S. Rodriguez-Baena . A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach. AIMS Mathematics, 2024, 9(5): 13358-13384. doi: 10.3934/math.2024652 |
[4] | Hend Khalid Alkahtani, Nuha Alruwais, Asma Alshuhail, Nadhem NEMRI, Achraf Ben Miled, Ahmed Mahmud . Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems. AIMS Mathematics, 2024, 9(6): 15076-15096. doi: 10.3934/math.2024731 |
[5] | Turki Althaqafi . Mathematical modeling of a Hybrid Mutated Tunicate Swarm Algorithm for Feature Selection and Global Optimization. AIMS Mathematics, 2024, 9(9): 24336-24358. doi: 10.3934/math.20241184 |
[6] | Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui . Federated and ensemble learning framework with optimized feature selection for heart disease detection. AIMS Mathematics, 2025, 10(3): 7290-7318. doi: 10.3934/math.2025334 |
[7] | Mohammed Abdul Kader, Muhammad Ahsan Ullah, Md Saiful Islam, Fermín Ferriol Sánchez, Md Abdus Samad, Imran Ashraf . A real-time air-writing model to recognize Bengali characters. AIMS Mathematics, 2024, 9(3): 6668-6698. doi: 10.3934/math.2024325 |
[8] | Jiawen Ye, Lei Dai, Haiying Wang . Enhancing sewage flow prediction using an integrated improved SSA-CNN-Transformer-BiLSTM model. AIMS Mathematics, 2024, 9(10): 26916-26950. doi: 10.3934/math.20241310 |
[9] | Senyuan Yang, Bo Yu, Jianxin Pan, Wuliang Yin, Hua Wang, Kai Yang, Qingtai Xiao . Application of a hybrid nonlinear algorithm driven by machine learning and feature importance identification for temperature control prediction of the bath smelting process. AIMS Mathematics, 2025, 10(6): 13104-13129. doi: 10.3934/math.2025588 |
[10] | Bao Ma, Yanrong Ma, Jun Ma . Adaptive robust AdaBoost-based kernel-free quadratic surface support vector machine with Universum data. AIMS Mathematics, 2025, 10(4): 8036-8065. doi: 10.3934/math.2025369 |
In this paper, a two-grid mixed finite volume element (MFVE) algorithm is presented for the nonlinear time fractional reaction-diffusion equations, where the Caputo fractional derivative is approximated by the classical L1-formula. The coarse and fine grids (containing the primal and dual grids) are constructed for the space domain, then a nonlinear MFVE scheme on the coarse grid and a linearized MFVE scheme on the fine grid are given. By using the Browder fixed point theorem and the matrix theory, the existence and uniqueness for the nonlinear and linearized MFVE schemes are obtained, respectively. Furthermore, the stability results and optimal error estimates are derived in detailed. Finally, some numerical results are given to verify the feasibility and effectiveness of the proposed algorithm.
Error-correcting codes have been a widely studied topic for the past six decades. Among all linear codes, cyclic codes have received significant attention. Boucher et al. [9] generalized the concept of cyclic codes over finite fields to skew cyclic codes. Siap et al. [25] extended their results to skew cyclic codes of arbitrary length. For the last twenty years, scholars have focused on error-correcting codes over finite rings.
There exist a host of studies on skew cyclic codes and double skew cyclic codes over finite commutative rings. Boucher et al. [10] and Jitman et al. [17] considered skew constacyclic codes over Galois rings and finite chain rings, respectively. Abualrub et al. [1] built θ-cyclic codes over F2+vF2. Gao [13] investigated the algebraic structure of skew cyclic codes over Fp+vFp. Gursoy et al. [15] accomplished the construction of skew cyclic codes over Fq+vFq. The algebraic properties of skew cyclic codes over the finite semi-local ring Fpm+vFpm were studied by M. Ashraf [3]. Shi et al. [22,23] established skew cyclic codes over Fq+vFq+v2Fq and Fq+vFq+⋯+vm−1Fq. Bagheri et al. [6] studied skew cyclic codes of length ps over Fpm+uFpm and obtained some torsion codes of skew cyclic codes. Shi et al. [24] proved the structure of skew cyclic codes over a finite non-chain ring. Recently, Prakash [21] studied the structure of skew cyclic codes over Fq[u,v,w]/⟨u2−1,v2−1,w2−1,uv−vu,vw−wv,wu−uw⟩. Gao et al. [14] studied weight distribution of double cyclic codes over Galois rings. Aydogdu et al. [4] characterized the algebraic structure of double skew cyclic codes over Fq.
Since Wood [26] proved that the finite Frobenius rings can serve as the alphabets of coding theory, many papers on cyclic codes over matrix rings have been published (see [2,7,11,16,18,19,20]). However, there are few papers investigating skew and double skew cyclic codes over matrix rings. In this study, we use the F4-cyclic algebra given in [5] to build the algebraic structure of skew cyclic codes over M2(F2). Additionally, we discuss the dual codes of skew cyclic codes and double skew cyclic codes over M2(F2).
This article is organized as follows: Section 2 provides some basic facts, and considers the algebraic structure of skew cyclic codes over M2(F2). We prove that a skew cyclic code C with a polynomial of minimum degree d(x) is a free submodule generated by d(x). We discuss Euclidean and Hermitian dual codes of θ-cyclic codes in Section 3. In Section 4, the spanning sets of double skew cyclic codes over M2(F2) are obtained, and Section 5 concludes this paper.
Let R be a finite ring with identity 1≠0. A left (resp. right) R-module M is denoted by RM (resp. MR). The socle of a module M is defined as the sum of its minimal submodules, denoted by Soc(M). The ring R is called a Frobenius ring if Soc(RR) (resp. Soc(RR)) is a principal left (resp. right) ideal. The ring R is called a local ring if R has a unique left (resp. right) maximal ideal (or equivalently, if R/rad(R) is a division ring). A ring R is called a left (resp. right) chain ring if the set of all left (resp. right) ideals of R is linearly ordered under the set inclusion. Lemma 2.1 describes the equivalence conditions between chain rings, principal ideal rings and local rings (cf. [12,Theorem 2.1]).
Lemma 2.1. For any finite ring R, the following conditions are equivalent:
(i) R is a local principal ideal ring;
(ii) R is a local ring and the unique maximal ideal M of R is principal;
(iii) R is a chain ring whose ideal are ⟨ri⟩, 0≤i≤N(r), where N(r) is the nilpotency index of r.
Moreover, if R is a finite chain ring with the unique maximal ideal ⟨r⟩ and the nilpotency index of r is z, then the cardinality of ⟨ri⟩ is |R/⟨r⟩|z−i for i=0,1,⋯,z−1.
We denote the 2×2 matrix ring over finite field F2 by M2(F2). By [5], we have M2(F2) is isomorphic to the F4-cyclic algebra R=F4⊕eF4 with e2=1 under the map δ,
δ:(0110)↦e,δ:(0111)↦ω, |
where F4=F2[ω] and ω2+ω+1=0. Note that (e+1)2=0, then e+1 is a nilpotent element of order 2. The multiplication in R is given by re=eσ(r) for any r∈R, where σ(r)=r2 is the Frobenius map on F4 and the addition is usual.
The set of the unit elements of R is {1,ω,1+ω,e,eω,e(1+ω)}. It is easy to know that the ring R has the unique maximal ideal ⟨e+1⟩. Since the n×n matrix ring Mn(R) over a Frobenius ring R is also Frobenius, then R is a finite local Frobenius ring.
Define a map θ:R→R by θ(a+eb)=σ(a)+eσ(b), a,b∈F4. One can verify that θ is an automorphism of R with order 2. The set F4,θ={0,1,e,1+e} is the fixed commutative subring of R by θ. Define the skew polynomial ring
R[x,θ]={rnxn+rn−1xn−1+⋯+r1x+r0|ri∈R,n∈N} |
with the usual polynomial addition, and the multiplication is defined by the rule r1xi⋅r2xj=r1θi(r2)xi+j, i,j∈N. Then for every a+eb∈R,
xi(a+eb)={(a+eb)xi,if i is even,(a2+eb2)xi,if i is odd. |
Note that R[x,θ] is non-commutative for multiplication, therefore, the submodules we discussed in this paper are always left. It should be noted also that R[x,θ] is not a unique factorization ring, for instance, x2=x⋅x=ex⋅ex, x3=x⋅x⋅x=x⋅ex⋅ex. In addition, the right division can be defined.
Lemma 2.2. Let f(x), g(x)∈R[x,θ], where the leading coefficient of g(x) is invertible. Then there exist unique q(x), r(x)∈R[x,θ] such that
f(x)=q(x)g(x)+r(x), |
where r(x)=0 or deg(r(x))<deg(g(x)). The polynomials q(x) and r(x) are called the right quotient and right remainder, respectively. The polynomial g(x) is called a right divisor of f(x) if g(x)|f(x).
Proof. The proof is similar to that of Lemma 2.3 of [13].
Proposition 2.3. The center Z(R[x,θ]) of R[x,θ] is F4,θ[x2].
Proof. This proof is similar to that of [13,Theorem 1]. We give the proof briefly. Since |⟨θ⟩|=2, then x2i⋅r=(θ2)i(r)x2i=rx2i with any r∈R. Thus x2i∈Z(R[x,θ]). It implies that f(x)=∑sj=0rjx2j∈Z(R[x,θ]), where rj∈F4,θ. Conversely, for any fz∈Z(R[x,θ]) and r∈R, if rfz=fzr and xfz=fzx, then the coefficients of fz are all in F4,θ and fz∈R[x2,θ]. Therefore fz∈F4,θ[x2].
Corollary 2.4. We have that xn+1∈Z(R[x,θ]) if and only if n is even.
Let Rn be the set of all n-tuples over R. Then a code C of length n over R is a nonempty subset of Rn. If C is a left (resp. right) R-submodule of Rn, then C is called a left (resp. right) linear code of length n over R. Every element c=(c0,c1,⋯,cn−1) in C is called a codeword.
Define the Gray map φ(a+eb)=(b,a+b) from R to F24 following the method in [8]. This map φ is a linear bijection and can be extended to a map from Rn onto F2n4 by concatenating the images of each component. For any element a+eb∈R, Lee weight of a+eb is defined as
wL(a+eb)=wHam(b)+wHam(a+b), |
where wHam(∗) stands for Hamming weight over finite fields. If x=(x0,x1,⋯,xn−1)∈Rn, then Lee weight of x is defined as
wL(x)=wL(x0)+wL(x1)+⋯+wL(xn−1). |
If C is a linear code of length n over R, then Hamming and Lee distance of C are defined as dHam=min{wHam(x)|x∈C} and dL=min{wL(x)|x∈C}, respectively. Analogously to [19,Theorem 7], we get the following result. It is a proposition of the image of the linear code C over R under the Gray map φ.
Proposition 2.5. Let C be a linear code over R of length n with size M and minimum Lee distance dL. Then φ(C) is a linear code over F4 of length 2n with size M and minimum Hamming distance dHam.
We discuss the algebraic structure of skew cyclic codes over R below. Let n be a positive integer. The set Rn=R[x,θ]/⟨xn−1⟩ is a ring if n is even. When n is odd, the set Rn is a left R[x,θ]-module under the multiplication defined by
f1(x)(f2(x)+(xn−1))=f1(x)f2(x)+(xn−1). |
Denote by T the standard shift operator on a linear code C, i.e., T(c)=(cn−1,c0,⋯,cn−2) for any codeword c=(c0,c1,⋯,cn−1)∈C. A linear code C over R is cyclic if any cyclic shift of a codeword c∈C is also a codeword, i.e., T(c)∈C. A linear code over R is called quasi-cyclic of index ℓ (or ℓ-quasi-cyclic) if and only if it is invariant under Tℓ. If ℓ=1, then it is a cyclic code. A linear code C of length n is called a skew cyclic code if and only if
θ(c)=(θ(cn−1),θ(c0),⋯,θ(cn−2))∈C, |
for any codeword c=(c0,c1,⋯,cn−1)∈C.
In a set of polynomials, a polynomial is called the polynomial of minimum degree if and only if it is not a polynomial of lower degree by removing any of its terms. Let d(x)=xn−m+∑n−m−1i=0dixi be a monic right divisor of xn−1. Then a m×n generator matrix G of the skew cyclic code C=⟨d(x)⟩ is given by
G=(d0d1⋯dn−m−110⋯000θ(d0)⋯θ(dn−m−2)θ(dn−m−1)1⋯0000⋯θ2(dn−m−3)θ2(dn−m−2)θ2(dn−m−1)⋯00⋮⋮⋯⋮⋮⋮⋯⋮⋮00⋯θm−2(d0)θm−2(d1)θm−2(d3)⋯1000⋯0θm−1(d0)θm−1(d1)⋯θm−1(dn−m−1)1). |
Proposition 2.6 shows that skew cyclic codes with a polynomial of minimum degree over R are free codes. Proposition 2.9 gives a sufficient and necessary condition for a skew cyclic code over R to become a cyclic code. Propositions 2.7 and 2.8 describe the relationship between skew cyclic codes of length n and cyclic codes, quasi-cyclic codes, respectively.
Proposition 2.6. Let n be a positive integer and C be a skew cyclic code of length n over R with a polynomial of minimum degree d(x), where the leading coefficient of d(x) is a unit. Then C is a free R[x,θ]-submodule of Rn such that C=⟨d(x)⟩, where d(x) is a right divisor of xn−1. Moreover, the code C has a basis B={d(x),xd(x),⋯,xn−deg(d(x))−1d(x)} and the number of codewords in C is |R|n−deg(d(x)).
Proof. By Lemma 2.2, any polynomial in C is divisible by d(x). It implies that d(x)|xn−1 and C=⟨d(x)⟩. For the second statement, let xn−1=q(x)d(x) for some q(x)∈R[x,θ]. Since the leading coefficient of d(x) is a unit, then the leading coefficient of q(x) is also invertible. Let m be the degree of q(x), then the degree of d(x) is n−m. Let q(x)=∑mi=0qixi, where qm is invertible. Therefore ∑mi=0qixid(x)=0 in Rn. It follows that xjd(x) with j≥m can be linearly presented by the elements of the set B={d(x),xd(x),⋯,xm−1d(x)}.
Let ∑m−1i=0ai⋅xid(x)=0, where ai∈R, i=0,1,⋯,m−1. Thus a(x)d(x)=0, where a(x)=∑m−1i=0aixi. The polynomial a(x)d(x) can be represented to a(x)d(x)=ζ(x)(xn−1) for some ζ(x)∈R[x,θ]. The degree of a(x)d(x) is n−1, while the degree of ζ(x)(xn−1) is greater than or equal to n if ζ(x)≠0. This is a contradiction. Therefore, we have ζ(x)=a(x)=0, i.e. ai=0 for i=0,1,⋯,m−1. The set B={d(x),xd(x),⋯,xm−1d(x)} is R-linearly independent. Consequently B is a basis of C and |C|=|R|n−deg(d(x)). This completes the proof.
Proposition 2.7. If C is a skew cyclic code of odd length over R, then C is a cyclic code over R.
Proof. Let n be odd and C be a skew cyclic code of length n over R. There exist two integers s,t such that 2s+nt=1. Thus, we have 2s=1−nt. If c(x)=∑n−1i=0cixi is any codeword in C, then
x2sc(x)=x1−ntc(x)=n−1∑i=0cixi+1−nt. |
Since xn=1, then
x2sc(x)=n−1∑i=0xi+1=xc(x)∈C. |
It follows that (cn−1,c0,⋯,cn−2)∈C for any codeword (c0,c1,⋯,cn−1) in C.
Proposition 2.8. If C is a skew cyclic code of even length over R, then C is a quasi-cyclic code of index 2.
Proof. Let C be a skew cyclic code of length 2t over R and c=(c0,0,c0,1,c1,0,⋯,ct−1,0,ct−1,1)∈C. Since θ(c)∈C and θ2=1, it follows that θ2(c)=(ct−1,0,ct−1,1,c0,0,c0,1,⋯,ct−2,0,ct−2,1)∈C. By the definition of quasi-cyclic codes, the code C is a 2-quasi-cyclic code of length 2t.
Proposition 2.9. Let C be a skew cyclic code generated by d(x) of even length n over R, where d(x) is a monic right divisor of xn−1. Then C is a cyclic code over R if and only if d(x) is fixed by θ.
Proof. Let d(x)=xl+dl−1xl−1+⋯+d1x+d0, where θ(di)=di, i=0,1,2,⋯,l−1. Then xd(x)=d(x)x∈C. It follows that the code C=⟨d(x)⟩ is cyclic over R.
Conversely, if C is a cyclic code of even length n over R, then C is a left ideal of Rn and an ideal of R[x]/⟨xn−1⟩. Therefore, d(x)x is a codeword in C. Since C is linear, we have d(x)x−xd(x)∈C. It implies that ∑l−1i=0(di−θ(di))xi+1 is a left multiple of d(x) such that d(x)x−xd(x)=rd(x) with r∈R. Note that the constant term of d(x)x−xd(x) is 0, then d(x)x−xd(x) must be 0. It shows that θ(di)=di, i=0,1,⋯,l−1. The proof is done.
Example 2.10. There are two examples:
(1) Let C be a skew cyclic code of length 4 over R generated by the following matrix,
(ee+1100ee+11). |
The generated polynomial of C is d(x)=x2+(e+1)x+e. Note that d(x) is a commutative right divisor of x4−1, and all coefficients of d(x) are fixed by θ. By Proposition 2.9, the code C is cyclic.
(2) Let C1 be a skew cyclic code of length 4 generated by the following matrix,
(ω1000ω+11000ω1). |
The generated polynomial of C1 is d1(x)=x+ω, and it is a right divisor of x4−1. The coefficients of d1(x) are not all fixed by θ. By Propositions 2.8 and 2.9, the code C1 is not cyclic but 2-quasi-cyclic.
This section investigates the dual codes of skew cyclic codes over the ring R. For any x=(x0,x1,⋯,xn−1) and y=(y0,y1,⋯,yn−1)∈Rn, the Euclidean inner product on Rn is defined by ⟨x,y⟩=∑n−1i=0xiyi. If the order of the automorphism θ is 2, then the Hermitian inner product of any x,y∈Rn is defined by ⟨x,y⟩H=∑n−1i=0xiθ(yi).
The elements x,y∈Rn are called Euclidean or Hermitian orthogonal if ⟨x,y⟩=0 or ⟨x,y⟩H=0, respectively. Let C be a skew cyclic code over R. Then its Euclidean dual code C⊥ is defined as C⊥={y∈Rn|⟨y,x⟩=0 for all x∈C }. The Hermitian dual code C⊥H of C is defined as C⊥H={z∈Rn|⟨z,x⟩H=0 for all x∈C }. A code C is called Euclidean or Hermitian self-dual if C=C⊥ or C=C⊥H, respectively.
Jitman et al. [17] described the algebraic structure of skew constacyclic codes over finite chain rings, and provided the generators of Euclidean and Hermitian dual codes of such codes. The ring R=F4⊕eF4 can be alternatively represented as F4⊕uF4 with u2=(e+1)2=0. This indicates that R is a finite chain ring under the change of basis.
In this section, Proposition 3.2 delineates a sufficient and necessary condition for Hermitian dual code of a skew cyclic code with length n over R. Proposition 3.3 is the self-dual skew condition of Hermitian dual code. Propositions 3.2 and 3.3 can be seen as corollaries of Theorems 3.7 and 3.8 of [17], respectively. The main work of this section is to depict the structure of Euclidean dual codes of skew cyclic codes over R. Proposition 3.6 illustrates that the Euclidean dual codes of skew cyclic codes of even length generated by a monic polynomial over R are also free and gives their generator polynomials.
By Lemmas 3.1 and 3.5 of [17], we acquire the following statement.
Lemma 3.1. Let C be a linear code of length n over R.
(i) For any integer n, the code C is a skew cyclic code if and only if C⊥ is skew cyclic.
(ii) For even integer n, the code C is a skew cyclic code if and only if C⊥H is skew cyclic.
From [17], the ring automorphism ρ on R[x,θ] is given as
ρ(t∑i=0rixi)=t∑i=0θ(ri)xi. |
By Theorems 3.7 and 3.8 of [17], we have the following results.
Proposition 3.2. Let n be even. If d(x) is a monic right divisor of xn−1 and ˆd(x)=xn−1d(x), then C is a free skew cyclic code generated by d(x) if and only if C⊥H is a skew cyclic code generated by
d⊥(x)=ρ(xdeg(ˆd(x))ϕ(ˆd(x))), |
where ϕ:R[x,θ]→R[x,θ]S−1 is the anti-monomorphism of rings defined by
ϕ(t∑i=0rixi)=t∑i=0x−iri |
with S={xi|i∈N}.
Proposition 3.3. Let n=2k. If d(x)=xk+∑k−1i=0dixi is a right divisor of xn−1, then the skew cyclic code C=⟨d(x)⟩ is a Hermitian self-dual code if and only if
(xk+k−1∑i=0dixi)(θ−k−1(d−10))+k−1∑i=1θi−k−1((d−10dk−i)xi+xk)=xn−1. |
This is called the self-dual skew condition.
Next, we discuss the algebraic properties of Euclidean dual codes of skew cyclic codes over R.
Lemma 3.4. Let d(x),q(x)∈R[x,θ], where the leading coefficient of q(x) is a unit. If d(x)q(x)∈Z(R[x,θ]) is a monic polynomial, then d(x)q(x)=q(x)d(x).
Proof. It is easy to prove by q(x)(d(x)q(x))=(d(x)q(x))q(x) and Lemma 2.2.
Lemma 3.5. Let n be even and xn−1=q(x)d(x), where the leading coefficient of q(x) is a unit. If C=⟨d(x)⟩ is a skew cyclic code of length n over R, then c(x)∈Rn is in C if and only if c(x)q(x)=0 in Rn.
Proof. Let c(x)∈C. Then c(x)=r(x)d(x) for some r(x)∈R[x,θ]. Since xn−1=q(x)d(x)∈Z(R[x,θ]), we have q(x)d(x)=d(x)q(x). Hence c(x)q(x)=r(x)d(x)q(x)=r(x)q(x)d(x)=0 in Rn.
Conversely, if c(x)q(x)=0 in Rn for some c(x)∈R[x,θ], then there exists r(x)∈R[x,θ] such that c(x)q(x)=r(x)(xn−1)=r(x)q(x)d(x)=r(x)d(x)q(x), i.e., c(x)=r(x)d(x)∈C.
Proposition 3.6. Let C=⟨d(x)⟩ be a skew cyclic code of even length n over R, where d(x) is a monic right divisor of xn−1. Let xn−1=q(x)d(x), q(x)=xm+∑m−1j=0qjxj and d(x)=xn−m+∑n−m−1i=0dixi. Then C⊥ is generated by the polynomial q∗(x)=1+∑mi=0θi(qm−i)xi.
Proof. Let c(x)=∑n−1i=0cixi be a codeword in C. Then c(x)q(x)=0 in Rn by Lemma 3.5. The coefficients of xm,xm+1,⋯,xn−1 are all zeros in c(x)q(x). Therefore, we have
c0+c1θ(qm−1)+c2θ2(qm−2)+⋯cmθm(q0)=0,c1+c2θ2(qm−1)+c3θ3(qm−2)+⋯cm+1θm+1(q0)=0,c2+c3θ3(qm−1)+c4θ4(qm−2)+⋯cm+2θm+2(q0)=0,⋮cn−m−1+cn−mθn−m(qm−1)+cn−m−1θn−m(qm−2)+⋯cn−1θn−1(q0)=0. |
We set
Q∗=(1θ(qm−1)θ2(qm−2)…θm−1(q1)θm(q0)…001θ2(qm−1)…θm−1(q2)θm(q1)…0001…θm−1(q3)θm(q2)…0⋮⋮⋮⋮⋮⋮⋮⋮000…1θn−m(qm−1)…θn−1(q0)). |
It is easy to know that each row vector of Q∗ is orthogonal to every codeword in C. Thus, all the row vectors of Q∗ are in C⊥. Since C is a Frobenius ring and deg(d(x))=n−m, then |C||C⊥|=|R|n, |C|=|R|m and |C⊥|=|R|n−m. Note that the rows of Q∗ are linearly independent. Consequently, the cardinality of the row spanning of Q∗ is |R|n−m. It follows that Q∗ is a generator matrix of C⊥. Observe that Q∗ is a circular matrix, then the corresponding polynomial q∗(x)=1+∑mi=0θi(qm−i)xi is a generator polynomial of C⊥. The proof is done.
Example 3.7. Let C1=⟨d1(x)⟩ be a skew cyclic code of length 4 over R. With the same notation as in Example 2.10, by Proposition 3.6, we have that the generated polynomial of dual code C⊥1 of C1 is q∗1(x)=ωx3+x2+ωx. The generated matrix of C⊥1 is
(0ω1ω). |
Both double cyclic codes and double skew cyclic codes are good linear codes because of their specific closure properties under the standard shift and addition operations. Double cyclic codes can be extended to double skew cyclic codes. We investigate double skew cyclic codes over R in this section.
A code C of length n is called double skew linear code if any codeword in C is partitioned into two blocks of lengths n1 and n2 such that the set of the first blocks of n1 symbols and the set of second blocks of n2 symbols form skew linear codes of lengths n1 and n2 over R, respectively.
For any r∈R and c=(u0,u1,⋯,un1−1,v0,v1,⋯,vn2−1)∈Rn1+n2, we define
rc=(ru0,ru1,⋯,run1−1,rv0,rv1,⋯,rvn2−1). |
It implies that Rn1+n2 is an R-module under the multiplication and a double skew linear code is an R-submodule of Rn1+n2.
A double linear code C of length n=n1+n2 over R is called double cyclic code if
(u0,u1,⋯,un1−1,v0,v1,⋯,vn2−1)∈C |
implies
(un1−1,u0,⋯,un1−2,vn2−1,v0,⋯,vn2−2)∈C. |
A double skew linear code C of length n1+n2 over R is called a double skew cyclic code if and only if
(θ(un1−1),θ(u0),⋯,θ(un1−2),θ(vn2−1),θ(v0),⋯,θ(vn2−2))∈C |
for any codeword
c=(u0,u1,⋯,un1−1,v0,v1,⋯,vn2−1)∈C. |
We denote the codeword c=(u0,u1,⋯,un1−1,v0,v1,⋯,vn2−1)∈C by c(x)=(c1(x)|c2(x)), where
c1(x)=n1−1∑i=0uixi∈R[x,θ]/⟨xn1−1⟩ |
and
c2(x)=n2−1∑j=0vjxj∈R[x,θ]/⟨xn2−1⟩. |
It gives a bijection between Rn1+n2 and Rn1,n2=R[x,θ]/⟨xn1−1⟩×R[x,θ]/⟨xn2−1⟩. Define the multiplication of any r(x)∈R[x,θ] and (c1(x)|c2(x))∈Rn1,n2 as
r(x)(c1(x)|c2(x))=(r(x)c1(x)|r(x)c2(x)). |
Under the multiplication, we have that Rn1,n2 is a left R[x,θ]-module. If c(x)=(c1(x)|c2(x)) is a codeword in C, then xc(x) is the standard skew cyclic shift of c.
Propositions 4.1 to 4.3 depict the structural properties of double skew cyclic codes of length n1+n2 over R.
Proposition 4.1. A code C is a double skew cyclic code over R if and only if C is a R[x,θ]-submodule of Rn1,n2.
Proof. Let C be a double skew cyclic code and c=(c1(x)|c2(x))∈C. Notice that xc(x)∈C and C is linear, then r(x)c(x)∈C for any r(x)∈R[x,θ]. Therefore C is a left R[x,θ]-submodule of the left module Rn1,n2. The converse is trivial.
Proposition 4.2. A double skew cyclic code of length n1+n2 is a double cyclic code if n1 and n2 are both odd.
Proof. The proof follows by Proposition 2.7.
Proposition 4.3. Let xn1−1=q1(x)d1(x) and xn2−1=q2(x)d2(x), where d1(x) and d2(x) are two monic polynomials. If C1=⟨d1(x)⟩ and C2=⟨d2(x)⟩ are two free skew cyclic codes of length n1 and n2 over R, respectively, then the code C generated by d(x)=(d1(x)|d2(x)) is a double skew cyclic code. Furthermore, A={d(x),xd(x),⋯,xl−1d(x)} is a spanning set of C, where l=deg(q(x)) and q(x)=lcm{q1(x),q2(x)}=∑li=0qixi.
Proof. By the definition of double skew cyclic codes, it is clear that C=⟨d(x)⟩ is a double skew cyclic code. The first statement follows. For the second statement, since q(x) is the least common multiple of q1(x) and q2(x), we have q(x)d(x)=q(x)(d1(x)|d2(x))=0 and xjd(x) with j≥l can be linearly represented by the elements of the set A={d(x),xd(x),⋯,xl−1d(x)}. Now let c(x)∈C be any non-zero codeword in C. Then c(x)=a(x)d(x) for some a(x)∈R[x,θ]. If deg(a(x))≥l, then a(x)=p(x)q(x)+r(x) by Lemma 2.2, where r(x)=0 or deg(r(x))<deg(q(x)). It follows that c(x)=a(x)d(x)=r(x)d(x). Since r(x)=0 or deg(r(x))≤l−1, then any non-zero codeword in C is a linear combination of the elements in A. The proof is done.
In this paper, we examine the structure of skew cyclic codes over M2(F2). All skew cyclic codes of length n over M2(F2) can be identified as left R[x,θ]-submodules of left module Rn=R[x,θ]/⟨xn−1⟩. Our results show that a skew cyclic code C with a polynomial of minimum degree d(x) is a free submodule ⟨d(x)⟩. We prove that a skew cyclic code of odd or even length over M2(F2) is a cyclic or 2-quasi-cyclic code. We give the self-dual skew condition of the Hermitian dual code and the generator of Euclidean dual code of a skew cyclic code, respectively. Furthermore, a spanning set of a double skew cyclic code over M2(F2) is obtained.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
This work was supported by the Natural Science Foundation of Shandong Province, China (Grant number ZR2019BA011) and by the National Natural Science Foundation of China (Grant number 11401285).
The authors declare no conflicts of interest.
[1] | Ortigueira, Fractional calculus for scientists and engineers, New York: Springer, 2011. |
[2] | R. Hilfer, Applications of fractional calculus in physics, Singapore: World Scientific, 2000. |
[3] | R. L. Magin, Fractional calculus in bioengineering, Redding: Begell House, 2006. |
[4] |
Y. M. Lin, C. J. Xu, Finite difference/spectral approximations for the time-fractional diffusion equation, J. Comput. Phys., 225 (2007), 1533–1552. doi: 10.1016/j.jcp.2007.02.001. doi: 10.1016/j.jcp.2007.02.001
![]() |
[5] |
Z. Z. Sun, X. N. Wu, A fully discrete difference scheme for a diffusion-wave system, Appl. Numer. Math., 56 (2006), 193–209. doi: 10.1016/j.apnum.2005.03.003. doi: 10.1016/j.apnum.2005.03.003
![]() |
[6] |
H. F. Ding, C. P. Li, A high-order algorithm for time-Caputo-tempered partial differential equation with Riesz derivatives in two spatial dimensions, J. Sci. Comput., 80 (2019), 81–109. doi: 10.1007/s10915-019-00930-5. doi: 10.1007/s10915-019-00930-5
![]() |
[7] |
Y. Zhao, C. Shen, M. Qu, W. P. Bu, Y. F. Tang, Finite element methods for fractional diffusion equations, Int. J. Model. Simul. Sci. Comput., 11 (2020), 2030001. doi: 10.1142/S1793962320300010. doi: 10.1142/S1793962320300010
![]() |
[8] |
Y. M. Zhao, W. P. Bu, J. F. Huang, D. Y. Liu, Y. F. Tang, Finite element method for two-dimensional space-fractional advection-dispersion equations, Appl. Math. Comput., 257 (2015), 553–565. doi: 10.1016/j.amc.2015.01.016. doi: 10.1016/j.amc.2015.01.016
![]() |
[9] |
H. L. Liao, D. F. Li, J. W. Zhang, Sharp error estimate of the nonuniform L1 formula for linear reaction-subdiffusion equations, SIAM J. Numer. Anal., 56 (2018), 1112–1133. doi: 10.1137/17M1131829. doi: 10.1137/17M1131829
![]() |
[10] |
J. Q. Xie, D. Liang, Z. Y. Zhang, Two novel energy dissipative difference schemes for the strongly coupled nonlinear space fractional wave equations with damping, Appl. Numer. Math., 157 (2020), 178–209. doi: 10.1016/j.apnum.2020.06.002. doi: 10.1016/j.apnum.2020.06.002
![]() |
[11] |
S. B. Yuste, Weighted average finite difference methods for fractional diffusion equations, J. Comput. Phys., 216 (2006), 264–274. doi: 10.1016/j.jcp.2005.12.006. doi: 10.1016/j.jcp.2005.12.006
![]() |
[12] |
Y. Liu, Y. W. Du, H. Li, F. W. Liu, Y. J. Wang, Some second-order θ schemes combined with finite element method for nonlinear fractional cable equation, Numer. Algor., 80 (2019), 533–555. doi: 10.1007/s11075-018-0496-0. doi: 10.1007/s11075-018-0496-0
![]() |
[13] |
L. B. Feng, F. W. Liu, I. Turner, An unstructured mesh control volume method for two-dimensional space fractional diffusion equations with variable coefficients on convex domains, J. Comput. Appl. Math., 364 (2020), 112319. doi: 10.1016/j.cam.2019.06.035. doi: 10.1016/j.cam.2019.06.035
![]() |
[14] |
Y. Liu, Z. D. Yu, H. Li, F. W. Liu, J. F. Wang, Time two-mesh algorithm combined with finite element method for time fractional water wave model, Int. J. Heat Mass Tran., 120 (2018), 1132–1145. doi: 10.1016/j.ijheatmasstransfer.2017.12.118. doi: 10.1016/j.ijheatmasstransfer.2017.12.118
![]() |
[15] |
N. Sene, Analytical solutions and numerical schemes of certain generalized fractional diffusion models, Eur. Phys. J. Plus, 134 (2019), 199. doi: 10.1140/epjp/i2019-12531-4. doi: 10.1140/epjp/i2019-12531-4
![]() |
[16] |
M. Yavuz, N. Sene, Stability analysis and numerical computation of the fractional predator-prey model with the harvesting rate, Fractal Fract., 4 (2020), 35. doi: 10.3390/fractalfract4030035. doi: 10.3390/fractalfract4030035
![]() |
[17] |
G. H. Gao, Z. Z. Sun, H. W. Zhang, A new fractional numerical differentiation formula to approximate the Caputo fractional derivative and its applications, J. Comput. Phys., 259 (2014), 33–50. doi: 10.1016/j.jcp.2013.11.017. doi: 10.1016/j.jcp.2013.11.017
![]() |
[18] |
A. A. Alikhanov, A new difference scheme for the time fractional diffusion equation, J. Comput. Phys., 280 (2015), 424–438. doi: 10.1016/j.jcp.2014.09.031. doi: 10.1016/j.jcp.2014.09.031
![]() |
[19] |
R. Mokhtari, F. Mostajeran, A high order formula to approximate the caputo fractional derivative, Commun. Appl. Math. Comput., 2 (2020), 1–29. doi: 10.1007/s42967-019-00023-y. doi: 10.1007/s42967-019-00023-y
![]() |
[20] |
R. Mokhtari, M. Ramezani, G. Haase, Stability and convergence analyses of the FDM based on some L-type formulae for solving the subdiffusion equation, Numer. Math. Theor. Meth. Appl., 14 (2021), 945–971. doi: 10.4208/nmtma.OA-2021-0020. doi: 10.4208/nmtma.OA-2021-0020
![]() |
[21] |
J. Zhao, Z. C. Fang, H. Li, Y. Liu, A Crank-Nicolson finite volume element method for time fractional Sobolev equations on triangular grids, Mathematics, 8 (2020), 1591. doi: 10.3390/math8091591. doi: 10.3390/math8091591
![]() |
[22] |
J. Zhao, H. Li, Z. C. Fang, Y. Liu, A mixed finite volume element method for time-fractional reaction-diffusion equations on triangular grids, Mathematics, 7 (2019), 600. doi: 10.3390/math7070600. doi: 10.3390/math7070600
![]() |
[23] |
J. Zhao, Z. C. Fang, H. Li, Y. Liu, Finite volume element method with the WSGD formula for nonlinear fractional mobile/immobile transport equations, Adv. Differ. Equ., 2020 (2020), 360. doi: 10.1186/s13662-020-02786-8. doi: 10.1186/s13662-020-02786-8
![]() |
[24] |
J. C. Xu, A novel two-grid method for semilinear elliptic equations, SIAM J. Sci. Comput., 15 (1994), 231–237. doi: 10.1137/0915016. doi: 10.1137/0915016
![]() |
[25] |
J. C. Xu, Two-grid discretization techniques for linear and nonlinear PDEs, SIAM J. Numer. Anal., 33 (1996), 1759–1777. doi: 10.1137/S0036142992232949. doi: 10.1137/S0036142992232949
![]() |
[26] |
C. N. Dawson, M. F. Wheeler, Two-grid methods for mixed finite element approximations of nonlinear parabolic equations, Contemp. Math., 180 (1994), 191–203. doi: 10.1090/conm/180/01971. doi: 10.1090/conm/180/01971
![]() |
[27] |
J. L. Yan, Q. Zhang, L. Zhu, Z. Y. Zhang, Two-grid methods for finite volume element approximations of nonlinear sobolev equations, Numer. Func. Anal. Opt., 37 (2016), 391–414. doi: 10.1080/01630563.2015.1115415. doi: 10.1080/01630563.2015.1115415
![]() |
[28] |
T. L. Hou, L. P. Chen, Y. Yang, Two-grid methods for expanded mixed finite element approximations of semi-linear parabolic integro-differential equations, Appl. Numer. Math., 132 (2018), 163–181. doi: 10.1016/j.apnum.2018.06.001. doi: 10.1016/j.apnum.2018.06.001
![]() |
[29] |
W. Liu, A two-grid method for the semi-linear reaction-diffusion system of the solutes in the groundwater flow by finite volume element, Math. Comput. Simulat., 142 (2017), 34–50. doi: 10.1016/j.matcom.2017.04.004. doi: 10.1016/j.matcom.2017.04.004
![]() |
[30] |
Y. Liu, Y. W. Du, H. Li, J. C. Li, S. He, A two-grid mixed finite element method for a nonlinear fourth-order reaction-diffusion problem with time-fractional derivative, Comput. Math. Appl., 70 (2015), 2474–2492. doi: 10.1016/j.camwa.2015.09.012. doi: 10.1016/j.camwa.2015.09.012
![]() |
[31] |
Y. Liu, Y. W. Du, H. Li, J. F. Wang, A two-grid finite element approximation for a nonlinear time-fractional Cable equation, Nonlinear Dyn., 85 (2016), 2535–-2548. doi: 10.1007/s11071-016-2843-9. doi: 10.1007/s11071-016-2843-9
![]() |
[32] |
Q. F. Li, Y. P. Chen, Y. Q. Huang, Y. Wang, Two-grid methods for semilinear time fractional reaction diffusion equations by expanded mixed finite element method, Appl. Numer. Math., 157 (2020), 38–54. doi: 10.1016/j.apnum.2020.05.024. doi: 10.1016/j.apnum.2020.05.024
![]() |
[33] |
Q. F. Li, Y. P. Chen, Y. Q. Huang, Y. Wang, Two-grid methods for nonlinear time fractional diffusion equations by L1-Galerkin FEM, Math. Comput. Simulat., 185 (2021), 436–451. doi: 10.1016/j.matcom.2020.12.033. doi: 10.1016/j.matcom.2020.12.033
![]() |
[34] |
C. J. Chen, H. Liu, X. C. Zheng, H. Wang, A two-grid MMOC finite element method for nonlinear variable-order time-fractional mobile/immobile advection-diffusion equations, Comput. Math. Appl., 79 (2020), 2771–2783. doi: 10.1016/j.camwa.2019.12.008. doi: 10.1016/j.camwa.2019.12.008
![]() |
[35] |
Y. Liu, N. Liu, H. Li, J. F. Wang, Fast calculation based on a spatial two-grid finite element algorithm for a nonlinear space-time fractional diffusion model, Numer. Meth. Part. D. E., 236 (2020), 1904–1921. doi: 10.1002/num.22509. doi: 10.1002/num.22509
![]() |
[36] | J. E. Jones, A Mixed finite volume element method for accurate computation of fluid velocities in porous media, University of Colorado, Denver, CO, 1995. |
[37] |
S. H. Chou, D. Y. Kwak, P. S. Vassilevski, Mixed covolume methods for the elliptic problems on triangular grids, SIAM J. Numer. Anal., 35 (1998), 1850–1861. doi: 10.1137/S0036142997321285. doi: 10.1137/S0036142997321285
![]() |
[38] |
S. Yang, Z. W. Jiang, Mixed covolume method for parabolic problems on triangular grids, Appl. Math. Comput., 215 (2009), 1251–1265. doi: 10.1016/j.amc.2009.06.068. doi: 10.1016/j.amc.2009.06.068
![]() |
[39] |
H. X. Rui, T. C. Lu, An expanded mixed covolume method for elliptic problems, Numer. Meth. Part. D. E., 21 (2005), 8–23. doi: 10.1002/num.20024. doi: 10.1002/num.20024
![]() |
[40] | Z. C. Fang, J. Zhao, H. Li, Y. Liu, Finite volume element methods for two-dimensional time fractional reaction-diffusion equations on triangular grids, 2021. |
[41] |
D. F. Li, H. L. Liao, W. W. Sun, J. L. Wang, J. W. Zhang, Analysis of L1-Galerkin FEMs for time-fractional nonlinear parabolic problems, Commun. Comput. Phys., 24 (2018), 86–103. doi: 10.4208/cicp.OA-2017-0080. doi: 10.4208/cicp.OA-2017-0080
![]() |
[42] |
F. E. Browder, Existence and uniqueness theorems for solutions of nonlinear boundary value problems, Proc. Symp. Appl. Math., 17 (1965), 24–49. doi: 10.1090/psapm/017/0197933. doi: 10.1090/psapm/017/0197933
![]() |
[43] |
Z. C. Fang, H. Li, An expanded mixed covolume method for sobolev equation with convection term on triangular grids, Numer. Meth. Part. D. E., 29 (2013), 1257–1277. doi: 10.1002/num.21754. doi: 10.1002/num.21754
![]() |
[44] |
J. Douglas, J. E. Roberts, Global estimates for mixed methods for second order elliptic equations, Math. Comput., 44 (1985), 39–52. doi: 10.1090/S0025-5718-1985-0771029-9. doi: 10.1090/S0025-5718-1985-0771029-9
![]() |
[45] |
B. T. Jin, B. Y. Li, Z. Zhou, Correction of high-order BDF convolution quadrature for fractional evolution equations, SIAM J. Sci. Comput., 39 (2017), A3129–A3152. doi: 10.1137/17M1118816. doi: 10.1137/17M1118816
![]() |
[46] |
M. Stynes, E. O'Riordan, J. L. Gracia, Error analysis of a finite difference method on graded meshes for a time-fractional diffusion equation, SIAM J. Numer. Anal., 55 (2017), 1057–1079. doi: 10.1137/16M1082329. doi: 10.1137/16M1082329
![]() |
[47] |
F. H. Zeng, Z. Q. Zhang, G. E. Karniadakis, Second-order numerical methods for multi-term fractional differential equations: Smooth and non-smooth solutions, Comput. Meth. Appl. Mech. Eng., 327 (2017), 478–502. doi: 10.1016/j.cma.2017.08.029. doi: 10.1016/j.cma.2017.08.029
![]() |
[48] |
X. C. Zheng, H. Wang, An optimal-order numerical approximation to variable-order space-fractional diffusion equations on uniform or graded meshes, SIAM J. Numer. Anal., 58 (2020), 330–352. doi: 10.1137/19M1245621. doi: 10.1137/19M1245621
![]() |
[49] |
B. L. Yin, Y. Liu, H. Li, Z. M. Zhang, Finite element methods based on two families of second-order numerical formulas for the fractional Cable model with smooth solutions, J. Sci. Comput., 84 (2020), 2. doi: 10.1007/s10915-020-01258-1. doi: 10.1007/s10915-020-01258-1
![]() |
[50] |
B. L. Yin, Y. Liu, H. Li, Z. M. Zhang, Approximation methods for the distributed order calculus using the convolution quadrature, Discrete Contin. Dyn.-B, 26 (2021), 1447–1468. doi: 10.3934/dcdsb.2020168. doi: 10.3934/dcdsb.2020168
![]() |