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Research article Special Issues

The shift-based scaling and recursing algorithm for evaluating the action of the matrix φ-functions

  • Received: 09 November 2024 Revised: 21 January 2025 Accepted: 05 February 2025 Published: 17 February 2025
  • MSC : 65L05, 65F10, 65F30

  • In this paper, we present an efficient method for computing the action of matrix φ-functions. Our approach is based on a scaling and recursing procedure and incorporates a shifting technique as a preprocessing step to enhance efficiency. We conduct a forward error analysis to determine the optimal scaling parameter and polynomial degree for achieving the desired accuracy. Numerical comparisons with existing algorithms demonstrate that the proposed algorithm performs well in terms of both accuracy and efficiency.

    Citation: Xianan Lu, Dongping Li, Zhixin Zhao. The shift-based scaling and recursing algorithm for evaluating the action of the matrix φ-functions[J]. AIMS Mathematics, 2025, 10(2): 2854-2868. doi: 10.3934/math.2025133

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  • In this paper, we present an efficient method for computing the action of matrix φ-functions. Our approach is based on a scaling and recursing procedure and incorporates a shifting technique as a preprocessing step to enhance efficiency. We conduct a forward error analysis to determine the optimal scaling parameter and polynomial degree for achieving the desired accuracy. Numerical comparisons with existing algorithms demonstrate that the proposed algorithm performs well in terms of both accuracy and efficiency.



    The linear complexity and the k-error linear complexity are important cryptographic characteristics of stream cipher sequences. The linear complexity of an N-periodic sequence s={su}u=0, denoted by LC(s), is defined as the length of the shortest linear feedback shift register (LFSR) that generates it [1]. With the Berlekamp-Massey (B-M) algorithm [2], if LC(s)N/2, then s is regarded as a good sequence with respect to its linear complexity. For an integer k0, the k-error linear complexity LCk(s) is the smallest linear complexity that can be obtained by changing at most k terms of s in the first period and periodically continued [3]. The cryptographic background of the k-error linear complexity is that some key streams with large linear complexity can be approximated by some sequences with much lower linear complexity [2]. For a sequence to be cryptographically strong, its linear complexity should be large enough, and its k-error linear complexity should be close to the linear complexity.

    The relationship between the linear complexity and the DFT of the sequence was given by Blahut in [4]. Let m be the order of 2 modulo an odd number N. For a primitive N-th root βF2m of unity, the DFT of s is defined by

    ρi=N1u=0suβiu0iN1. (1.1)

    Then

    LC(s)=WH(ρ0,ρ1,,ρN1), (1.2)

    where WH(A) is the hamming weight of the sequence A. The polynomial

    G(X)=N1i=0ρiXiF2m[X] (1.3)

    is called the Mattson-Solomon polynomial (M-S polynomial) of s [5]. It can be deduced from Eqs (1.2)and (1.3) that the linear complexity of s is equal to the number of the nonzero terms of G(X), namely

    LC(s)=|G(X)|. (1.4)

    By the inverse DFT,

    su=N1i=0ρiβiu=G(βu)0uN1. (1.5)

    There are many studies about two-prime generators. In 1997–1998, Ding calculated the linear complexity and the autocorrelation values of binary Whiteman generalized cyclotomic sequences of order two [6,7]. In 2013, Li defined a new generalized cyclotomic sequence of order two of length pq, which is based on Whiteman generalized cyclotomic classes, and calculated its linear complexity [8]. In 2015, Wei determined the k-error linear complexity of Legendre sequences for k=1,2 [9]. In 2018, Hofer and Winterhof studied the 2-adic complexity of the two-prime generator of period pq [10]. Alecu and Sălăgean transformed the optimisation problem of finding the k-error linear complexity of a sequence into an optimisation problem in the DFT domain, by using Blahut's theorem in the same year [11]. In 2019, in terms of the DFT, Chen and Wu discussed the k-error linear complexity for Legendre, Ding-Helleseth-Lam, and Hall's sextic residue sequences of odd prime period p [12]. In 2020, Zhou and Liu presented a type of binary sequences based on a general two-prime generalized cyclotomy, and derived their minimal polynomial and linear complexity [13]. In 2021, the autocorrelation distribution and the 2-adic complexity of generalized cyclotomic binary sequences of order 2 with period pq were determined by Jing [14].

    This paper is organized as follows. Firstly, we present some preliminaries about Whiteman generalized cyclotomic classes and the linear complexity in Section 2. In Section 3, we give main results about the linear complexity of Whiteman generalized cyclotomic sequences of order two. In Section 4, we give the 1-error linear complexity of these sequences. At last, we conclude this paper in Section 5.

    Let p and q be two distinct odd primes with gcd(p1,q1)=2, and N=pq, e=(p1)(q1)/2. By the Chinese Remainder Theorem, there is a fixed common primitive root g of both p and q such that ordN(g)=e. Let x be an integer satisfying

    x=g(modp)x=1(modq).

    Then the set

    Di={gsximodN:s=0,1,,e1}

    for i=0,1 is called a Whiteman generalized cyclotomic class of order two [15].

    As pointed out in [14], the unit group of the ring ZN is

    ZN={a(mod N):gcd(a,N)=1}={ip+jq(mod N):1iq11jp1}.

    Let P={p,2p,,(q1)p}, Q={q,2q,,(p1)q} and R={0}. Then ZN=ZNPQR. The sequence s(a,b,c)={su}u=0 over F2 is defined by

    su={c,if u=0,a,if uP,b,if uQ,12(1(up)(uq)),if uZN,

    where () denotes the Legendre symbol and a,b,cF2 [14].

    Lemma 2.1. [7] 1D1, if |pq|/2 is odd; and 1D0, if |pq|/2 is even.

    Lemma 2.2. [6]

    (1)Ifp±1(mod8),q±1(mod8)orp±3(mod8),q±3(mod8),then2D0.(2)Ifp±1(mod8),q±3(mod8)orp±3(mod8),q±1(mod8),then2D1.

    Lemma 2.3. [6] (1) If aP, then aP=P and aQ=R.

    (2) If aQ, then aP=R and aQ=Q.

    (3) If aDi, then aP=P, aQ=Q, and aDj=D(i+j)mod2, where i,j=0,1.

    It was shown in [6] that, for a primitive N-th root βF2m of unity, we have

    iPβi=1,iQβi=1,

    and

    iD0βi+iD1βi+iPβi+iQβi=1. (2.1)

    Lemma 2.4. [6]

    uDjβiu={p12(mod2),ifiP,q12(mod2),ifiQ.

    Actually, if p1(mod8) or p3(mod8), then (p1)/2=1; if p1(mod8) or p3(mod8), then (p1)/2=0. By symmetry, if q1(mod8) or q3(mod8), then (q1)/2=1; if q1(mod8) or q3(mod8), then (q1)/2=0.

    Lemma 2.5. Define

    Di(X)=uDiXuF2[X],i=0,1.

    Then for β, we have D0(β)=0 and D1(β)=1 if 2D0; D0(β)=ω and D1(β)=1+ω if 2D1, where ωF4F2.

    Proof. (1) If 2D0, by Lemma 2.3 we have

    [Di(β)]2=Di(β2)=2uDiβ2u=Di(β)F2.

    (2) If 2D1, by Lemma 2.3 we have

    [Di(β)]2=Di(β2)=2uDi+1β2u=Di+1(β),[Di(β)]4=[Di(β)2]2=[Di+1(β)]2=Di+1(β2)=2uDiβ2u=Di(β).

    Hence Di(β)F4F2.

    And by Eq (2.1), we have D0(β)D1(β) and D0(β)+D1(β)=1. Assume that D0(β)=0, D1(β)=1 for 2D0, and D0(β)=ω, D1(β)=1+ω for 2D1, where ωF4F2.

    Let LC(s(a,b,c)) be the linear complexity of s(a,b,c), and the other symbols be the same as before.

    Theorem 3.1. Let pv(mod8) and qw(mod8), where v,w=±1,±3. Then the linear complexity of s(a,b,c) respect to different values of p and q is as shown as Table 1.

    Table 1.  The linear complexity of s(a,b,c).
    s(0,0,0) s(0,0,1) s(0,1,0) s(0,1,1) s(1,0,0) s(1,0,1) s(1,1,0)) s(1,1,1)
    (1,3) or (3,1) pqp pqq+1 pq1 pqpq+2 pqpq+1 pq pqq pqp+1
    (1,3) or (3,1) pq1 pqpq+2 pqp pqq+1 pqq pqp+1 pqpq+1 pq
    (1,1) or (3,3) pqp+q12 pq+pq+12 pq+p+q32 pqpq+32 pqpq+12 pq+p+q12 pq+pq12 pqp+q+12
    (1,1) or (3,3) pq+p+q32 pqpq+32 pqp+q12 pq+pq+12 pq+pq12 pqp+q+12 pqpq+12 pq+p+q12
    (3,1) or (1,3) pqq pqp+1 pqpq+1 pq pq1 pqpq+2 pqp pqq+1
    (1,1) or (3,3) pq+pq12 pqp+q+12 pqpq+12 pq+p+q12 pq+p+q32 pqpq+32 pqp+q12 pq+pq+12

     | Show Table
    DownLoad: CSV

    Proof. We provide the process of calculating LC(s(0,0,0)) when v=1 and w=3, and can prove other cases in a similar way.

    By Lemmas 2.1–2.3 and Eq (1.1), we have 1D1, 2D1, then

    ρi=N1u=0suβiu=uD1βiu=uD0βiu,

    and ρ0=0. By Eq (1.3), we have

    G(X)=N1i=0ρiXi=iD0ρiXi+iD1ρiXi+iPρiXi+iQρiXi+ρ0=iD0uD0βiuXi+iD1uD0βiuXi+iPuD0βiuXi+iQuD0βiuXi.

    Let t=iu. Then by Lemmas 2.3–2.5, we have

    G(X)=iD0tD0βtXi+iD1tD1βtXi+iPp12Xi+iQq12Xi=D0(β)D0(X)+D1(β)D1(X)+iPXi=ωD0(X)+(1+ω)D1(X)+iPXi.

    By Eq (1.4) we can get the linear complexity of s(0,0,0) as

    LC(s(0,0,0))=|G(X)|=pqp.

    Actually, the linear complexity of s(1,0,0) was studied by Ding in [6] with its minimal polynomial.

    Let LCk(s(a,b,c)) be the k-error linear complexity of s(a,b,c), ˜s={˜su}u=0 be the new sequence obtained by changing at most k terms of s, that ˜s=s+e, where e={eu}u=0 is an error sequence of period N. Ding has provided in [2] that, the k-error linear complexity of a sequence can be expressed as

    LCk(s)=minWH(e)k{LC(s+e)}. (4.1)

    It is clearly that LC0(s)=LC(s) and

    NLC0(s)LC1(s)LCl(s)=0,

    where l=WH(s).

    Let G(X), Gk(X) and ˜G(X) be the M-S polynomials of s, e and ˜s respectively. Note that

    G(X)=N1i=0ρiXi, Gk(X)=N1i=0ηiXi, ˜G(X)=N1i=0ξiXi, (4.2)

    where ρi, ηi and ξi are the DFTs of s, e and ˜s respectively. By Eqs (1.5), (4.1) and (4.2), we have ˜G(X)=G(X)+Gk(X), then

    ξi=ρi+ηi. (4.3)

    Assume that eu0=1 for 0u0N1 and eu=0 for uu0 in the first period of e. Then the DFT of e is

    ηi=N1u=0euβiu=βiu00iN1.

    Specially, if u0=0, then ηi=1 for all 0iN1; otherwise, η0=1 and the order of ηi is N for 1iN1.

    Theorem 4.1. Let pv(mod8) and qw(mod8), where v,w=±1,±3, and the other symbols be the same as before. Then the 1-error linear complexity of s(a,b,c) is as shown as Table 2.

    Table 2.  The 1-error linear complexity of s(a,b,c).
    s(0,0,0) and s(0,0,1) s(0,1,0) and s(0,1,1) s(1,0,0) and s(1,0,1) s(1,1,0)) and s(1,1,1)
    (1,3) or (3,1) (1) pqp, if p>q;
    (2) pqq+1, if p<q.
    pqpq+2 pqpq+1 (1) pqp+1, if p>q;
    (2) pqq, if p<q.
    (1,3) or (3,1) pqpq+2 (1) pqp, if p>q;
    (2) pqq+1, if p<q.
    (1) pqp+1, if p>q;
    (2) pqq, if p<q.
    pqpq+1
    (1,1) or (3,3) (1) pqp+q12, if p>q;
    (2) pq+pq+12, if p<q.
    pqpq+32 pqpq+12 (1) pqp+q+12, if p>q;
    (2) pq+pq12, if p<q.
    (1,1) or (3,3) pqpq+32 (1) pqp+q12, if p>q;
    (2) pq+pq+12, if p<q.
    (1) pqp+q+12, if p>q;
    (2) pq+pq12, if p<q.
    pqpq+12
    (3,1) or (1,3) (1) pqp+1, if p>q;
    (2) pqq, if p<q.
    pqpq+1 pqpq+2 (1) pqp, if p>q;
    (2) pqq+1, if p<q.
    (1,1) or (3,3) (1) pqp+q+12, if p>q;
    (2) pq+pq12, if p<q.
    pqpq+12 pqpq+32 (1) pqp+q12, if p>q;
    (2) pq+pq+12, if p<q.

     | Show Table
    DownLoad: CSV

    Proof. We consider the case v=1,w=3 for LC1(s(0,0,0)). By Lemmas 2.1–2.5 and Eq (1.1), we have 1D1, 2D1 and

    ξi=ρi+ηi=uD0βiu+βiu0={ω+βiu0,if iD0,1+ω+βiu0,if iD1,1+βiu0,if iP,βiu0,if iQ,1,if i=0.

    Then by Eq (4.2), we can get

    ˜G(X)=N1i=0ξiXi=iD0(ω+βiu0)Xi+iD1(1+ω+βiu0)Xi+iP(1+βiu0)Xi+iQβiu0Xi+1.

    According to Lemma 2.3, we can get the following results.

    (1) If u0=0, then

    ˜G(X)=iD0(ω+1)Xi+iD1ωXi+iQXi+1,|˜G(X)|=pqq+1.

    (2) If u0Q, then

    ˜G(X)=iD0(ω+βiu0)Xi+iD1(1+ω+βiu0)Xi+iQβiu0Xi+1,|˜G(X)|=pqq+1.

    (3) If u0D0 or u0D1 or u0P, then

    ˜G(X)=iD0(ω+βiu0)Xi+iD1(1+ω+βiu0)Xi+iP(1+βiu0)Xi+iQβiu0Xi+1,|˜G(X)|=pq.

    Compare the results of Cases (1)–(3) with LC(s(0,0,0))=pqp. If p>q, then pqp<pqq+1<pq; if p<q, then pqq+1<pqp<pq. Hence

    LC1(s(0,0,0))={pqp,if p>q,pqq+1,if p<q.

    Similarly we can prove the other cases for LC1(s(a,b,c)).

    All results of LC(s(a,b,c)) and LC1(s(a,b,c)) in Sections 3 and 4 have been tested by MAGMA program.

    The purpose of this paper is to determine the linear complexity and the 1-error linear complexity of s(a,b,c). In most of the cases, s(a,b,c) possesses high linear complexity, namely LC(s(a,b,c))>N/2, consequently has decent stability against 1-bit error. Notice that the linear complexity of some of the sequences above is close to N/2. Then the sequences can be selected to construct cyclic codes by their minimal generating polynomials with the method introduced by Ding [16].

    This work was supported by Fundamental Research Funds for the Central Universities (No. 20CX05012A), the Major Scientific and Technological Projects of CNPC under Grant (No. ZD2019-183-008), the National Natural Science Foundation of China (Nos. 61902429, 11775306) and Shandong Provincial Natural Science Foundation of China (ZR2019MF070).

    The authors declare that they have no conflicts of interest.



    [1] M. Hochbruck, A. Ostermann, Exponential Integrators, Acta Numer., 19 (2010), 209–286. https://doi.org/10.1017/S0962492910000048 doi: 10.1017/S0962492910000048
    [2] B. V. Minchev, W. M. Wright, A review of exponential integrators for first order semi-linear problems, Tech. report 2/05, Department of Mathematics, NTNU, 2005. https://cds.cern.ch/record/848122
    [3] A. H. Al-Mohy, N. J. Higham, A new scaling and squaring algorithm for the matrix exponential, SIAM J. Matrix Anal. Appl., 31 (2009), 970–989. https://doi.org/10.1137/09074721X doi: 10.1137/09074721X
    [4] E. Defez, J. Ibáñez, J. Sastre, J. Peinado, P. Alonso, A new efficient and accurate spline algorithm for the matrix exponential computation, J. Comput. Appl. Math., 337 (2018), 354–365. https://doi.org/10.1016/j.cam.2017.11.029 doi: 10.1016/j.cam.2017.11.029
    [5] N. J. Higham, The scaling and squaring method for the matrix exponential revisited, SIAM J. Matrix Anal. Appl., 26 (2005), 1179–1193. https://doi.org/10.1137/090768539 doi: 10.1137/090768539
    [6] Y. Y. Lu, Computing a matrix function for exponential integrators, J. Comput. Appl. Math., 161 (2003), 203–216. https://doi.org/10.1016/j.cam.2003.08.006 doi: 10.1016/j.cam.2003.08.006
    [7] C. Moler, C. V. Loan, Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later, SIAM Review, 45 (2003), 3–49. https://doi.org/10.1137/S00361445024180 doi: 10.1137/S00361445024180
    [8] I. Najfeld, T. F. Havel, Derivatives of the matrix exponential and their computation, Adv. Appl. Math., 16 (1995), 321–375. https://doi.org/10.1006/aama.1995.1017 doi: 10.1006/aama.1995.1017
    [9] J. Sastre, J. Ibáñez, E. Defez, P. Ruiz, Efficient orthogonal matrix polynomial based method for computing matrix exponential, Appl. Math. Comput., 217 (2011), 6451–6463. https://doi.org/10.1016/j.amc.2011.01.004 doi: 10.1016/j.amc.2011.01.004
    [10] J. Sastre, J. Ibáñez, E. Defez, P. Ruiz, New scaling-squaring Taylor algorithms for computing the matrix exponential, SIAM J. Sci. Comput., 37 (2015), 439–455. https://doi.org/10.1137/090763202 doi: 10.1137/090763202
    [11] B. Skaflestad, W. M. Wright, The scaling and modified squaring method for matrix functions related to the exponential, Appl. Numer. Math., 59 (2009), 783–799. https://doi.org/10.1016/j.apnum.2008.03.035 doi: 10.1016/j.apnum.2008.03.035
    [12] R. C. Ward, Numerical computation of the matrix exponential with accuracy estimate, SIAM J. Numer. Anal., 14 (1977), 600–610. https://doi.org/10.1137/0714039 doi: 10.1137/0714039
    [13] A. H. Al-Mohy, N. J. Higham, Computing the action of the matrix exponential, with an application to exponential integrators, SIAM J. Sci. Comput., 33 (2011), 488–511. https://doi.org/10.1137/100788860 doi: 10.1137/100788860
    [14] Y. Saad, Analysis of some Krylov subspace approximations to the matrix exponential operator, SIAM J. Numer. Anal., 29 (1992), 209–228. https://doi.org/10.1137/0729014 doi: 10.1137/0729014
    [15] M. Caliari, P. Kandolf, A. Ostermann and S. Rainer, Comparison of software for computing the action of the matrix exponential, BIT Numer. Math., 54 (2014), 113–128. http://doi.org/10.1007/s10543-013-0446-0 doi: 10.1007/s10543-013-0446-0
    [16] M. Caliari, P. Kandolf, A. Ostermann, S. Rainer, The leja method revisited: backward error analysis for the matrix exponential, SIAM J. Sci. Comput., 38 (2016), A1639–A1661. https://doi.org/10.1137/15M1027620 doi: 10.1137/15M1027620
    [17] R. B. Sidje, Expokit: A software package for computing matrix exponentials, ACM Trans. Math. Softw., 24 (1998), 130–156. https://doi.org/10.1145/285861.285868 doi: 10.1145/285861.285868
    [18] D. P. Li, Y. H. Cong, Approximation of the linear combination of φ-functions using the block shift-and-invert Krylov subspace method, J. Appl. Anal. Comput., 4 (2017), 1402–1416. https://doi.org/10.11948/2017085 doi: 10.11948/2017085
    [19] S. Gaudrealt, G. Rainwater, M. Tokman, KIOPS: A fast adaptive Krylov subspace solver for exponential integrators, J. Comput. Phys., 372 (2018), 236–255. https://doi.org/10.1016/j.jcp.2018.06.026 doi: 10.1016/j.jcp.2018.06.026
    [20] J. Niesen, W. Wright, Algorithm 919: A Krylov subspace algorithm for evaluating the phi-functions appearing in exponential integrators, ACM Trans. Math. Software, 38 (2012), Article 22. https://doi.org/10.1145/2168773.2168781 doi: 10.1145/2168773.2168781
    [21] D. P. Li, S. Y. Yang, J. M. Lan, Efficient and accurate computation for the φ-functions arising from exponential integrators, Calcolo, 59 (2022), 1–24. http://doi.org/10.1007/s10092-021-00453-2 doi: 10.1007/s10092-021-00453-2
    [22] N. J. Higham, F. Tisseur, A block algorithm for matrix 1-norm estimation, with an application to 1-norm pseudospectra, SIAM J. Matrix Anal. Appl., 21 (2000), 1185–1201. https://doi.org/10.1137/S0895479899356080 doi: 10.1137/S0895479899356080
    [23] T. A. Davis, Y. F. Hu, The university of florida sparse matrix collection, ACM Trans. Math. Software, 38 (2011), Article 1. https://doi.org/10.1145/2049662.2049663 doi: 10.1145/2049662.2049663
    [24] M. Hochbruck, C. Lubich, H. Selhofer, Exponential integrators for large systems of differential equations, SIAM J. Sci. Comput., 19 (1998), 1552–1574. https://doi.org/10.1137/S1064827595295337 doi: 10.1137/S1064827595295337
    [25] T. Penzl, LYAPACK: A MATLAB toolbox for large Lyapunov and Riccati equations, model reduction problems, and linear-quadratic optimal control problems, users' guide (Version 1.0), 1999. https://netlib.org/lyapack/guide.pdf
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