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Research article

Estimations and optimal censoring schemes for the unified progressive hybrid gamma-mixed Rayleigh distribution


  • Received: 16 January 2023 Revised: 21 May 2023 Accepted: 13 June 2023 Published: 05 July 2023
  • Censoring is a common occurrence in reliability engineering tests. This article considers estimation of the model parameters and the reliability characteristics of the gamma-mixed Rayleigh distribution based on a novel unified progressive hybrid censoring scheme (UPrgHyCS), where experimenters are allowed more flexibility in designing the test and higher efficiency. The maximum likelihood estimates of the model parameters and reliability are provided using the stochastic expectation–maximization algorithm based on the UPrgHyCS. Further, the Bayesian inference associated with any parametric function of the model parameters is considered using the Markov chain Monte Carlo method with the Metropolis-Hastings (M-H) algorithm. Asymptotic confidence and credible intervals of the proposed quantities are also created. The maximum a posteriori estimates of the model parameters are acquired. Due to the importance of determining the optimal censoring scheme for reliability problems, different optimality criteria are proposed and derived to find it. This method can help to design experiments and get more information about unknown parameters for a given sample size. Finally, comprehensive simulation experiments are provided to investigate the performances of the considered estimates, and a real dataset is analyzed to elucidate the practical application and the optimality criterion work in real life scenarios. The Bayes estimates using the M-H technique show the best performance in terms of error values.

    Citation: Showkat Ahmad Lone, Hanieh Panahi, Sadia Anwar, Sana Shahab. Estimations and optimal censoring schemes for the unified progressive hybrid gamma-mixed Rayleigh distribution[J]. Electronic Research Archive, 2023, 31(8): 4729-4752. doi: 10.3934/era.2023242

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  • Censoring is a common occurrence in reliability engineering tests. This article considers estimation of the model parameters and the reliability characteristics of the gamma-mixed Rayleigh distribution based on a novel unified progressive hybrid censoring scheme (UPrgHyCS), where experimenters are allowed more flexibility in designing the test and higher efficiency. The maximum likelihood estimates of the model parameters and reliability are provided using the stochastic expectation–maximization algorithm based on the UPrgHyCS. Further, the Bayesian inference associated with any parametric function of the model parameters is considered using the Markov chain Monte Carlo method with the Metropolis-Hastings (M-H) algorithm. Asymptotic confidence and credible intervals of the proposed quantities are also created. The maximum a posteriori estimates of the model parameters are acquired. Due to the importance of determining the optimal censoring scheme for reliability problems, different optimality criteria are proposed and derived to find it. This method can help to design experiments and get more information about unknown parameters for a given sample size. Finally, comprehensive simulation experiments are provided to investigate the performances of the considered estimates, and a real dataset is analyzed to elucidate the practical application and the optimality criterion work in real life scenarios. The Bayes estimates using the M-H technique show the best performance in terms of error values.



    Cluster algebras were introduced by Fomin and Zelevinsky in [1]. The core idea to define cluster algebra of rank n is that one should have a cluster seed and an operator on cluster seeds, called mutation. Roughly, a cluster seed Σt0 is a collection of variables x1;t0,,xn;t0 (cluster variables) and binomials F1;t0,,Fn;t0 (exchange polynomials). One can apply mutation to a cluster seed to produce a new seed, i.e., new variables and new binomials. Note that the exchange polynomial in cluster algebra is always a binomial. One of the main results in cluster algebras is that they have the Laurent phenomenon [1].

    In the theory of cluster algebras, the following are interesting conjectures on seeds of cluster algebras: in a cluster algebra of rank n, (1) each seed is uniquely defined by its cluster; (2) any two seeds with n1 common cluster variables are connected with each other by one step of mutation. One can refer to [2,3] for detailed proof.

    Significant notations of the upper cluster algebra, upper bound and lower bound associated with the cluster seed were introduced by Berenstein, Fomin and Zelevinsky to study the structure of cluster algebras in [4]. There are some theorems of upper bounds and lower bounds: (a) under the condition of coprimeness, the upper bound is invariant under seed mutations; (b) the standard monomials in x1,x1,,xn,xn are linearly independent over ZP if and only if the cluster seed is acyclic; (c) under the conditions of acyclicity and coprimeness, then the upper bound coincides with the lower bound.

    Muller showed that locally acyclic cluster algebras coincide with their upper cluster algebras in [5]. Gekhtman, Shapiro and Vainshte in [3] proved (a) for generalized cluster algebras, then Bai, Chen, Ding and Xu demonstrated (c) and the sufficiency of (b) in [6]. Besides, Bai discovered that acyclic generalized cluster algebras coincide with their generalized upper cluster algebras.

    Laurent phenomenon (LP) algebras were introduced by Lam and Pylyavskyy in [7], which generalize cluster algebras from the perspective of exchange relations. The exchange polynomials in LP algebras were allowed to have arbitrarily many monomials, rather than being just binomials. It turns out that the Laurent phenomenon also appears in LP algebras [7].

    One should note that our method also works for cluster algebras and generalized cluster algebras. We do not talk much about generalized cluster algebras in this paper, and one can refer to [2,6,8,9,10] for details.

    In this paper, we first affirm the conjectures on seeds of cluster algebras with respect to LP algebras.

    Theorem 1.1. In a LP algebra of rank n,

    1) (Theorem 3.1) each LP seed is uniquely defined by its cluster.

    2) (Theorem 3.7) any two LP seeds with n1 common cluster variables are connected with each other by one step of mutation.

    Second, we affirm theorems of upper bounds and lower bounds with respect to LP algebras under some conditions, by using the similar methods developed in [4].

    Condition 1.2. Let Mk be the lexicographically first monomial in the irreducible polynomial Fk and fk(xi) be the polynomial on xi in R[x2,,ˆxi,,ˆxk,,xn](xi) without constant terms in Fk for any ik. Assume that for a LP seed (x,F) of rank n, k[1,n], Fk satisfies the following conditions:

    (i) ˆFk=Fk.

    (ii) Mk is of the form xvk={xvk+1,kk+1xvn,knk[1,n1]1k=n, where vkZnk0 for k[1,n1].

    (iii) when x1Fk for k1, Fk=Mk+fk(x1).

    (iv) when x1Fk for k1 or 2, if there exist an index i in [2,k1] such that xkMi, then Fk=Mk+fk(xi).

    Theorem 1.3. (a) (Theorem 4.9) Under (i) of Condition 1.2, the upper bound is invariant under LP mutations.

    (b) (Theorem 4.16) Under (i) and (ii) of Condition 1.2, the standard monomials in x1,x1,,xn,xn form an R-basis for L(Σ).

    (c) (Theorem 4.9) Under Condition 1.2, the upper bound coincides with the lower bound.

    This paper is organized as follows: In Section 2, some basic definitions are given. In Section 3, we prove Theorem 1.1, and we give the corresponding results and applications in cluster algebras. In Section 4, we affirm Theorem 1.3.

    Let a,b be positive integers satisfying ab, write [a,b] for {a,a+1,,b}.

    Let R be a unique factorization domain over Z, and the ambient field F be the rational function field in n independent variables over the field of fractions Frac(R). Recall that an element f of R is irreducible if it is non-zero, not a unit, and not be expressed as the product f=gh of two elements g,hR which are non-units.

    Definition 2.1. A Laurent phenomenon (LP) seed of rank n in F is a pair (x,F), in which

    (i) x={x1,xn} is a transcendence basis for F over Frac(R), where x is called the cluster of (x,F) and x1,xn are called cluster variables.

    (ii) F={F1,,Fn} is a collection of irreducible polynomials in R[x1,,xn] such that for each i,j[1,n], xjFi (Fi is not divisible by xj) and Fi does not depend on xi, where F1,,Fn are called the exchange polynomials of (x,F).

    The following notations, definitions and propositions can refer to [7,11].

    Let F,N be two rational functions in x1,,xn. Denote by F|xjN the expression obtained by substituting xj in F by N. And if F involves the variable xi, then we write xiF. Otherwise, we write xiF.

    Definition 2.2. Let (x,F) be a LP seed in F. For each FjF, define a Laurent polynomial ˆFj=Fjxa11xaj1j1xaj+1j+1xann, where akZ0 is maximal such that Fakk divides Fj|xkFk/xk as an element in R[x1,,xk1,(xk)1,xk+1,,xn]. The Laurent polynomials in ˆF:={ˆF1,,ˆFn} are called the exchange Laurent polynomials.

    From the definition of exchange Laurent polynomials, we know that Fj/ˆFj is a monomial in R[x1,,ˆxj,,xn], where ˆxj means xj vanishes in the {x1,,xn}. And ˆFj|xkFk/xk is not divisible by Fk.

    Proposition 2.3. (Lemma 2.4 of [7])Let (x,F) be a LP seed in F, then F={F1,,Fn} and ˆF={ˆF1,,ˆFn} determine each other uniquely.

    Proposition 2.4. (Lemma 2.7 of [7])If xkFi, then xiFk/ˆFk. In particular, xkFi implies that ˆFk|xi0 is well defined and ˆFk|xi0R[x±11,,ˆxi,,ˆxk,,x±1n].

    Definition 2.5. Let (x,F) be a LP seed in F and k[1,n]. Define a new pair

    ({x1,,xn},{F1,,Fn}):=μk(x,F),

    where xk=ˆFk/xk and xi=xi for ik. And the exchange polynomials change as follows:

    (1) Fk:=Fk;

    (2) If xkFi, then Fi:=riFi, where ri is a unit in R;

    (3) If xkFi, then Fi is obtained from the following three steps:

    (i) Define Gi:=Fi|xkNk, where Nk=ˆFk|xi0xk. Then we have

    GiR[x±11,,ˆxi,,xk1,,x±1n]=R[x1±1,,^xi,,xk1,,xn±1].

    (ii) Define Hi to be Gi with all common factors (in R[x1,,ˆxi,,ˆxk,,xn]) removed. Note that Hi is unique up to a unit in R and HiR[x1±1,,^xi,,xk1,,xn±1].

    (iii) Let M be a Laurent monomial in x1,,^xi,,xn with coefficient a unit in R such that Fi:=MHiR[x1,,xn] and is not divisible by any variable in {x1,,xn}. Thus

    FiR[x1,,^xi,,xk,,xn].

    Then we say that the new pair μk(x,F) is obtained from the LP seed (x,F) by the LP mutation in direction k.

    Example 2.6. Let R=Z and F=Q(a,b,c). Consider the LP seed (x,F), where x={a,b,c} and F={b+1, a+c, b+1}. From the definition of exchange Laurent polynomials, we can get ˆFa=Fac,ˆFb=Fb,ˆFc=Fca.

    Let (x,F)=μa(x,F), then we have a=^Faa=b+1ac, b=b, c=c. From the definition of the LP mutation, the exchange polynomial Fa does not change. Since aFc, we have Fc=b+1 (or up to a unit). Since Fb depends on a, to compute Fb, we need to procedure the above three steps. By (i), we get Na=1ac and Gb=1ac+c. By (ii), we get Hb=Gb up to a unit in R. By (iii), M=ac and Fb=MHb=ac2+1. Thus the new seed can be chosen to be

    (x,F)={(a,b+1),(b,ac2+1),(c,b+1)}.

    It is not clear a priori that the LP mutation μk(x,F) of a LP seed (x,F) is still a LP seed because of the irreducibility requirement for the new exchange polynomials. But it can be seen from the following proposition that μk(x,F) is still a LP seed in F.

    Proposition 2.7. (Proposition 2.15 of [7])Let (x,F) be a LP seed in F, then μk(x,F) is also a LP seed in F.

    Proposition 2.8. (Proposition 2.16 of [7])If (x,F) is obtained from (x,F) by LP mutation at k, then (x,F) can be obtained from (x,F) by LP mutation at k. In this sense, LP mutation is an involution.

    Remark 2.9. It is important to note that because of (ii), Fi is defined up to a unit in R. And this is the motivation to consider LP seeds up to an equivalence relation.

    Definition 2.10. Let Σt1=(xt1,Ft1) and Σt2=(xt2,Ft2) be two LP seeds in F. Σt1 and Σt2 are equivalent if for each i[1,n], there exist ri,ri which are units in R such that xi;t2=rixi;t1 and Fi;t2=riFi;t1.

    Denote by [Σt] the equivalent class of Σt, that is, [Σt] is the set of LP seeds which are equivalent to Σt.

    Proposition 2.11. (Lemma 3.1 of [7])Let Σt1=(xt1,Ft1) and Σt2=(xt2,Ft2) be two LP seeds in F, and Σtu=μk(Σt2), Σtv=μk(Σt1).If [Σt1]=[Σt2], then [Σtv]=[Σtu].

    Let Σt=(xt,Ft) be a LP seed in F. By the above proposition, it is reasonable to define LP mutation of [Σt] at k given by μk([Σt]):=[μk(Σt)].

    Definition 2.12. A Laurent phenomenon (LP) pattern S in F is an assignment of each LP seed (xt,Ft) to a vertex t of the n-regular tree Tn, such that for any edge tk_t,(xt,Ft)=μk(xt,Ft).

    We always denote by xt={x1;t,,xn;t} and Ft={F1;t,,Fn;t}.

    Definition 2.13. Let S be a LP pattern, the Laurent phenomenon (LP) algebra A(S) (of rank n) associated with S is the R-subalgebra of F generated by all the cluster variables in the seeds of S.

    If Σ=(x,F) is any seed in F, we shall write A(Σ) to mean the LP algebra A(S) associated with S containing the seed Σ.

    Theorem 2.14. (Theorem 5.1 of [7], the Laurent phenomenon) Let A(S) be a LP algebra, and (xt0,Ft0) be a LP seed of A(S). Then any cluster variable xi;t of A(S) is in the Laurent polynomial ring R(t±10):=R[x±11;t0,,x±1n;t0].

    Definition 2.15. Let Σ=(x,F) be a LP seed of rank n and k[1,n]. A new seed Σ=(x,F) of rank n1 is defined as follows:

    1) let R=R[x±1k].

    2)x=x{xk}.

    3)let F={Fj=Fjxak | j[1,n]k, a is the power of xk in ˆFj/Fj}.

    The seed Σ is in fact a LP seed, then Σ is called the freezing of the LP seed Σ at xk. A(Σ)F=Frac(R[x1,,^xk,,xn]) is defined to be the subalgebra generated by all the cluster variables from LP seeds mutation-equivalent to Σ. Then A(Σ) is called the freezing of the LP algebra A(Σ) at xk.

    Example 2.16. Consider the LP seed Σ=(x,F)={(a,b+1),(b,a+c),(c,b+1)} over R=Z from Example 2.6. We produce the freezing of (x,F) at c as follows: first, remove (c,b+1); next, since the powers of c in ^Fa and ^Fb are -1 and 0 respectively, we have Fa=Fac1=b+1c and Fb=Fb. Then the LP seed Σ are {(a,b+1c),(b,a+c)} over Z[c±1].

    Proposition 2.17. (Proposition 3.7 of [7])The algebra A(Σ) is a LP algebra.

    Corollary 2.18. The freezing of the LP seed at xi is compatible with the mutation in direction j for ji.

    Recall that an integer matrix Bn×n=(bij) is called skew-symmetrizable if there is a positive integer diagonal matrix D such that DB is skew-symmetric, where D is said to be the skew-symmetrizer of B. Bn×n=(bij) is sign-skew-symmetric if bijbji<0 or bij=bji=0 for any i,j[1,n]. A sign-skew-symmetric B is totally sign-skew-symmetric if any matrix B obtained from B by a sequence of mutations is sign-skew-symmetric. It is known that skew-symmetrizable integer matrices are always totally sign-skew-symmetric.

    The diagram for a sign-skew-symmetric matrix Bn×n is the directed graph Γ(B) with the vertices 1,2,,n and the directed edges from i to j if bij>0. Bn×n is called acyclic if Γ(B) has no oriented cycles. As shown in [12], an acyclic sign-skew-symmetric integer matrix B is always totally sign-skew-symmetric.

    Let P be the coefficient group, its group ring ZP is a domain [1]. We take an ambient field F to be the field of rational functions in n independent variables with coefficients in ZP.

    Definition 2.19. A cluster seed in F is a triplet Σ=(x,y,B) such that

    (i) x={x1,,xn} is a transcendence basis for F over Frac(ZP). x is called the cluster of (x,y,B) and {x1,xn} are called cluster variables.

    (ii) y={y1,,yn} is a subset of P, where {y1,,yn} are called coefficients.

    (iii) B=(bij) is a n×n totally sign-skew-symmetric matrix, called an exchange matrix.

    Let (x,y,B) be a cluster seed in F, one can associate binomials {F1,,Fn} defined by

    Fj=yj1yjbij>0xbiji+yj1yjbij<0xbiji.

    {F1,,Fn} are called the exchange polynomials of (x,y,B).

    Note that the coefficients and the exchange matrices in a cluster algebra are used for providing the exchange polynomials and explaining how to produce new exchange polynomials when doing a mutation (see Definition 2.20) on a cluster seed.

    Definition 2.20. Let Σ=(x,y,B) be a cluster seed in F. Define the mutation of Σ in the direction k[1,n] as a new triple Σ=(x,y,B):=μk(Σ) in F, where

    xi={Fk/xki=kxiik.,yi={y1ki=kyiymax(bki,0)k(1yk)bkiik.,
     and bij={biji=k or j=kbij+sgn(bik)max(bikbkj,0)otherwise.

    It can be seen that μk(Σ) is also a cluster seed and the mutation of a cluster seed is an involution, that is, μk(μk(Σ))=Σ.

    Definition 2.21. A cluster pattern S is an assignment of a seed Σt=(xt,yt,Bt) to every vertex t of the n-regular tree Tn, such that for any edge tk_t,Σt=(xt,yt,Bt)=μk(Σt).

    We always denote by xt=(x1;t,,xn;t),yt=(y1;t,,yn;t),Bt=(btij).

    Definition 2.22. Let S be a cluster pattern, the cluster algebra A(S) (of rank n) associated with the given cluster pattern S is the ZP-subalgebra of the field F generated by all cluster variables of S.

    Theorem 2.23. (Theorem 3.1 of [1], the Laurent phenomenon)Let A(S) be a cluster algebra, and Σt0=(xt0,yt0,Bt0) be a cluster seed of A(S). Then any cluster variable xi;t of A(S) is in the Laurent polynomial ring ZP(t±10):=ZP[x±11;t0,,x±1n;t0].

    Example 2.24. Let B=(0330), then the exchange polynomials of the cluster seed (x,B) are the following two polynomials

    F1=x32+1=(x2+1)(x22x2+1),
    F2=x31+1=(x1+1)(x21x1+1).

    It is easy to see that the exchange polynomials F1,F2 of (x,B) are both reducible in the above example. Thus the cluster x and the exchange polynomial F of (x,B) can not define a LP seed. From [7], we know that sometimes a cluster algebra defines a LP algebra indeed.

    Theorem 2.25. (Theorem 4.5 of [7]) Every cluster algebra with principal coefficients is a Laurent phenomenon algebra.

    Theorem 3.1. Let A(S) be a LP algebra of rank n, and (xt1,Ft1),(xt2,Ft2) be two LP seeds of A(S).

    1) If there exists a permutation σ of [1,n] and a unit riR such that xi;t2=rixσ(i);t1 for i[1,n], then Fi;t2=riFσ(i);t1 as polynomials for a certain unit ri in R.

    2) each LP seed is uniquely defined by its cluster.

    Proof. For any fixed k[1,n], let (xu,Fu)=μk(xt2,Ft2) and (xv,Fv)=μσ(k)(xt1,Ft1), we consider the Laurent expansion of xk;u with respect to xv and the Laurent expansion of xσ(k);v with respect to xu.

    From the definition of the LP mutation, we know

    xi;u={xi;t2if ikˆFk;t2xk;t2if i=k and  xi;v={xi;t1if iσ(k)ˆFσ(k);t1xσ(k);t1if i=σ(k). (3.1)

    Since xi;t2=rixσ(i);t1 for i[1,n], we have xi;u=rixσ(i);v for ik. By (3.1), we get

    xk;u=ˆFk;t2(x1;t2,,ˆxk;t2,,xn;t2)/xk;t2=ˆFk;t2(r1xσ(1);t1,,ˆxσ(k);t1,,rnxσ(n);t1)/(rkxσ(k);t1)=ˆFk;t2(r1xσ(1);v,,ˆxσ(k);v,,rnxσ(n);v)/(rkxσ(k);t1);xσ(k);v=ˆFσ(k);t1(x1;t1,,ˆxσ(k);t1,,xn;t1)/xσ(k);t1=ˆFσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v)/xσ(k);t1.

    Thus xk;uxσ(k);v=ˆFk;t2(r1xσ(1);v,,ˆxσ(k);v,,rnxσ(n);v)rkˆFσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v) and we get that

    xk;u=xσ(k);vˆFk;t2(r1xσ(1);v,,ˆxσ(k);v,,rnxσ(n);v)rkˆFσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v). (3.2)

    From the definition of the exchange Laurent polynomial, we know the above equation has the form of

    xk;u=xσ(k);vFk;t2(r1xσ(1);v,,ˆxσ(k);v,,rnxσ(n);v)rkFσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v)M, (3.3)

    where the Laurent monomial M is of the form xm11;vxmσ(k)1σ(k)1;vxmσ(k)+1σ(k)+1;vxmnn;v and mj is integer for j[1,n]σ(k). Thus Eq (3.3) is the Laurent expansion of xk;u with respect to xv.

    Similarly, the following equation is the Laurent expansion of xσ(k);v with respect to xu.

    xσ(k);v=xk;uFσ(k);t1(r11xσ1(1);u,,ˆxk;u,,r1nxσ1(n);u)r1kFk;t2(x1;u,,ˆxk;u,,xn;u)M1, (3.4)

    where M1 is also a Laurent monomial in R[x±11;u,,ˆxk;u,,x±1n;u] since xi;u=rixσ(i);v for ik.

    We know that both Fσ(k);t1  (  r11xσ1(1);u   ,,   ˆxk;u   ,,   r1nxσ1(n);u  )  Fk;t2  (  x1;u   ,,   ˆxk;u   ,,   xn;u  )  =Fσ(k);t1  (  x1;v   ,,   ˆxσ(k);v   ,,   xn;v  )  Fk;t2  (  r1xσ(1);v   ,,   ˆxσ(k);v   ,,   rnxσ(n);v  )   and Fk;t2  (  r1xσ(1);v   ,,   ˆxσ(k);v   ,,   rnxσ(n);v  )  Fσ(k);t1  (  x1;v   ,,   ˆxσ(k);v   ,,   xn;v  )   are Laurent polynomials in

    R[x±11;v,,ˆxσ(k);v,,x±1n;v]=R[x±11;u,,ˆxk;u,,x±1n;u]

    by the Laurent phenomenon.

    Thus both Fk;t2  (  r1xσ(1);v   ,,  ˆxσ(k);v   ,,  rnxσ(n);v      )  Fσ(k);t1  (  x1;v   ,,  ˆxσ(k);v   ,,  xn;v  )   and Fσ(k);t1  (  x1;v   ,,  ˆxσ(k);v   ,,  xn;v  )  Fk;t2  (  r1xσ(1);v   ,,  ˆxσ(k);v   ,,  rnxσ(n);v      )   are units in R[x±11;v   ,,  ˆxσ(k);v   ,,  x±1n;v].

    Because both Fk;t2 and Fσ(k);t1 are irreducible and xj;t2Fk;t2, xj;t1Fσ(k);t1 for each j[1,n], so that both Fk;t2  (  r1xσ(1);v   ,,  ˆxσ(k);v   ,,  rnxσ(n);v      )  Fσ(k);t1  (  x1;v   ,,  ˆxσ(k);v   ,,  xn;v  )   and Fσ(k);t1  (  x1;v   ,,  ˆxσ(k);v   ,,  xn;v  )  Fk;t2  (  r1xσ(1);v   ,,  ˆxσ(k);v   ,,  rnxσ(n);v      )   are units in R.

    Hence

    Fk;t2(r1xσ(1);v,,ˆxσ(k);v,,rnxσ(n);v)=rkFσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v),

    for some unit rk in R, i.e., Fk;t2(x1;u,,ˆxk;u,,xn;u)=rkFσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v). Thus Fk;t2=rkFσ(k);t1 as polynomials, for each k[1,n].

    Remark 3.2. From the proof of the above theorem, we can see that the choice of the unit ri such that xi;t2=rixσ(i);t1 does not matter when proving Fi;t2/Fσ(i);t1 is a unit in R. Similarly, in the proof which is based on the Laurent phenomenon and need to use the ratio of two exchange polynomials (for example, the proof of Lemma 3.6), we can assume that ri=1.

    Next, we will give the proof of the conjecture for cluster algebras that each seed is uniquely defined by its cluster, and main points of proof that are different from the previous one.

    Theorem 3.3. Let A(S) be a cluster algebra, and Σtl=(xtl,ytl,Btl),l=1,2 be two cluster seeds of A(S).If there exists a permutation σ of [1,n] such that xi;t2=xσ(i);t1 for i[1,n], then

    (i) Either yk;t2=yσ(k);t1, bt2ik=bt1σ(i)σ(k) or yk;t2=y1σ(k);t1, bt2ik=bt1σ(i)σ(k) for i,k[1,n].

    (ii) In both cases, Fi;t2=Fσ(i);t1 as polynomials for i[1,n].

    Proof. By the same method with the proof of Theorem 3.1, the version of the equation (3.2) for the cluster algebra is just

    xk;u=xσ(k);vFk;t2(xσ(1);v,,ˆxσ(k);v,,xσ(n);v)Fσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v), (3.5)

    and note that xi;u=xσ(i);v for any ik, we also have

    xσ(k);v=xk;uFσ(k);t1(xσ1(1);u,,ˆxk;u,,xσ1(n);u)Fk;t2(x1;u,,ˆxk;u,,xn;u). (3.6)

    We know that Eq (3.5) is the Laurent expansion of xk;u with respect to xv and Eq (3.6) is the Laurent expansion of xσ(k);v with respect to xu. Then by the Laurent phenomenon, both Fk;t2  (  xσ(1);v   ,,   ˆxσ(k);v   ,,   xσ(n);v  )  Fσ(k);t1  (  x1;v   ,,   ˆxσ(k);v   ,,   xn;v  )   and Fσ(k);t1  (  xσ1(1);u   ,,   ˆxk;u   ,,   xσ1(n);u  )  Fk;t2  (  x1;u   ,,   ˆxk;u   ,,   xn;u  )   are Laurent polynomials, and this implies that Fk;t2  (  xσ(1);v   ,,   ˆxσ(k);v   ,,   xσ(n);v  )  Fσ(k);t1  (  x1;v   ,,   ˆxσ(k);v   ,,   xn;v  )   is a Laurent monomial in ZP[x±11;v   ,,   ˆxσ(k);v   ,,   x±1n;v]. We know that

    Fk;t2(xσ(1);v,,ˆxσ(k);v,,xσ(n);v)=yk;t21yk;t2bt2ik>0xbt2ikσ(i);v+11yk;t2bt2ik<0xbt2ikσ(i);v,
    Fσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v)=yσ(k);t11yσ(k);t1bt1iσ(k)>0xbt1iσ(k)i;v+11yσ(k);t1bt1iσ(k)<0xbt1iσ(k)i;v.

    Because Fk;t2(xσ(1);v,,ˆxσ(k);v,,xσ(n);v)Fσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v) is a Laurent monomial, we must have either yk;t2=yσ(k);t1, bt2ik=bt1σ(i)σ(k) or yk;t2=y1σ(k);t1, bt2ik=bt1σ(i)σ(k). In both cases, we have

    Fk;t2(xσ(1);v,,ˆxσ(k);v,,xσ(n);v)=Fσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v),

    i.e., Fk;t2(x1;u,,ˆxk;u,,xn;u)=Fσ(k);t1(x1;v,,ˆxσ(k);v,,xn;v). Thus Fk;t2=Fσ(k);t1 as polynomials.

    Lemma 3.4. Let A(S) be a skew-symmetrizable cluster algebra with skew-symmetrizer D, and(xt1,yt1,Bt1),(xt2,yt2,Bt2) be two cluster seeds of A(S).If there exists a permutation σ of [1,n] such that xi;t2=xσ(i);t1 for i[1,n], then bt2ik=dkdσ(k)bt1σ(i)σ(k).

    Proof. Let Pσ be the permutation matrix define by the permutation σ. By the cluster formula (see Theorem 3.5 of [2]), we have Pσ(Bt1D1)Pσ=Bt2D1. Then Bt2=(PσBt1Pσ)(PσD1Pσ)D. The result follows.

    By the proof of the first statement and the definition of equivalence for two cluster seeds, we conclude the second statement.

    From Theorem 3.3 and Lemma 3.4, we can affirm a conjecture for skew-symmetrizable cluster algebra proposed by Fomin and Zelevinsky in [13], which says every seed of a cluster algebra is uniquely determined by its cluster.

    Corollary 3.5. Let A(S) be a skew-symmetrizable cluster algebra with skew-symmetrizer D, and (xt1,yt1,Bt1),(xt2,yt2,Bt2) be two cluster seeds of A(S).If there exists a permutation σ of [1,n] such that xi;t2=xσ(i);t1 for i[1,n], then yk;t2=yσ(k);t1, bt2ik=bt1σ(i)σ(k), dk=dσ(k) for any i and k.

    The result (1) of Theorem 3.1 shows that when xi;t2=rixσ(i);t1 where i[1,n] and riR, then Fi;t2=riFσ(i);t1 where riR. In fact, the proof of result (1) also works for any generalized cluster algebra and any (totally sign-skew-symmetric) cluster algebra with coefficients. The reason is that the proof mainly relies on the Laurent phenomenon and is independent of the form of exchange polynomials. In the meantime, the unit ri such that xi;t2=rixσ(i);t1 can be chosen to be 1 (see Remark 3.2). Although LP algebras, cluster algebras and generalized cluster algebras have different forms of exchange polynomials, and they are not included in each other, they all have the Laurent phenomenon. Thus for cluster algebras or generalized cluster algebras, it is the same method in proving that two clusters up to a permutation imply their corresponding exchange polynomials to be the same up to a permutation.

    For cluster algebras or generalized cluster algebras, the equivalence for seeds is defined as two clusters and their corresponding exchange matrices up to a permutation. We know that exchange polynomials are defined by exchange matrices. And exchange polynomials up to a permutation can not imply exchange matrices to be equivalent up to a permutation. So in order to prove the conjecture (1) that each seed is uniquely defined by its cluster, we need to prove that the corresponding exchange matrices are equivalent up to a permutation. For cluster algebras, based on the result that exchange polynomials are the same up to a permutation, we give the proof in Corollary 3.5. For generalized cluster algebras, we will not discuss them more in this article, but will further discuss them in the next work.

    Let A(S) be a LP algebra, if there is a seed (xt0,Ft0) of A(S) such that the exchange polynomials in Ft0 are all nontrivial, we say that A(S) is a LP algebra having no trivial exchange relations.

    Note that if there is a trivial exchange polynomial in a LP seed (xt0,Ft0), from the definition of LP mutation, this trivial exchange polynomial remain invariant under any sequence of LP mutations. So if A(S) is a LP algebra having no trivial exchange relations, then each exchange polynomial of A(S) is a nontrivial polynomial.

    Lemma 3.6. Let A(S) be a LP algebra having no trivial exchange relations, and Σt=(xt,Ft), Σt0=(xt0,Ft0) be two LP seeds of A(S) with xi;t=rixi;t0, where ri is a unit in R for any ik. If xk;t=Mxk;t0 for some Laurent monomial M in R[x±11;t0,,ˆxk;t0,,x±1n;t0], then M is a unit in R, and [Σt]=[Σt0].

    Proof. Without loss of generality, we assume that ri=1 for ik. It does not make difference to the proof.

    Assume that M=rikxaii;t0=rikxaii;t, where r is a unit in R. If there exists some jk such that aj<0, then we consider the LP seed (xw,Fw)=μj(Σt0). From the definition of LP mutation, we know that xi;w=xi;t0 for ij and xj;wxj;t0=ˆFj;t0(x1;t0,,ˆxj;t0,,xn;t0). Then we have

    xk;t=(rikxaii;t0)xk;t0=(rij,kxaii;w)xajj;wˆFajj;t0(x1;w,,ˆxj;w,,xn;w)xk;w,

    which can be written as the following equation, from the definition of the exchange Laurent polynomial.

    xk;t=(rij,kxaii;w)xajj;wLFajj;t0(x1;w,,ˆxj;w,,xn;w)xk;w, (3.7)

    where L is a Laurent monomial in R[x±11;w,,ˆxj;w,,x±1n;w]. Thus Eq (3.7) is the expansion of xk;t with respect to xw. Because A(S) has no trivial exchange relations, Fj;t0 is a nontrivial polynomial. And we know that Fj;t0 is irreducible and xsFj;t0 for each s[1,n], thus Eq (3.7) will contradict the Laurent phenomenon. So each aj is nonnegative.

    Similarly, by considering that xk;t0=M1xk;t=(rikxaii;t)xk;t, we can get each aj is nonnegative. Thus each aj is 0, thus M=r is a unit in R. Then by Theorem 3.1, we have [Σt]=[Σt0].

    Theorem 3.7. Let A(S) be a LP algebra of rank n having no trivial exchange relations, and Σt1=(xt1,Ft1), Σt2=(xt2,Ft2) be two LP seeds of A(S). If xi;t1=rixi;t2 holds for any ik, where ri is a unit in R, then [Σt1]=[Σt2] or [Σt1]=μk[Σt2], that is, any two LP seeds with n>1 common cluster variables are connected with each other by one step of mutation.

    Proof. Without loss of generality, we assume that ri=1 for ik. It does not make difference to the proof.

    By the Laurent phenomenon, we assume that xk;t2=f(x1;t1,,xn;t1) and xk;t1=g(x1;t2,,xn;t2), where fR[x±11;t1,,x±1n;t1] and gR[x±11;t2,,x±1n;t2]. Since xi;t1=xi;t2 for any ik, we know that xk;t1 entries f with exponent 1 or 1; Thus xk;t2 has the form of xk;t2=L1x±1k;t1+L0, where L10 and L0 are Laurent polynomials in

    R[x±11;t1,,ˆxk;t1,,x±1n;t1]=R[x±11;t2,,ˆxk;t2,,x±1n;t2].

    Let (xu,Fu)=μk(Σt2) and (xv,Fv)=μk(Σt1). From the definition of the LP mutation, we know

    xi;u={xi;t2if ikˆFk;t2/xk;t2if i=k and xi;v={xi;t1if ikˆFk;t1/xk;t1if i=k.

    Thus xk;u=ˆFk;t2(x1;t2,,ˆxk;t2,,xn;t2)/xk;t2=ˆFk;t2(x1;t1,,ˆxk;t1,,xn;t1)L1x±1k;t1+L0. From the definition of the exchange Laurent polynomial, we know the above equation has the form of

    xk;u=Fk;t2(x1;t1,,ˆxk;t1,,xn;t1)L1x±1k;t1+L0M, (3.8)

    where M is a Laurent monomial in R[x±11;t1,,ˆxk;t1,,x±1n;t1]. The above equation is just the expansion of xk;u with respect to xt1. By the Laurent phenomenon, and the fact xk;t1Fk;t2(x1;t1,,ˆxk;t1,,xn;t1), we obtain that L0=0 and Fk;t2(x1;t1,,ˆxk;t1,,xn;t1)L1 is a Laurent polynomial in R[x±11;t1,,ˆxk;t1,,x±1n;t1]. Thus we have that xk;t2=L1x±1k;t1 and xk;u has the form of xk;u=˜Mx1k;t1, where ˜M is a Laurent polynomial in R[x±11;t1,,ˆxk;t1,,x±1n;t1].

    We claim that Fk;t2(x1;t1,,ˆxk;t1,,xn;t1)L1 is actually a Laurent monomial, i.e., ˜M is a Laurent monomial in R[x±11;t1,,ˆxk;t1,,x±1n;t1].

    Case (i): xk;t2=L1xk;t1. Then xk;t1=L11xk;t2, which is the expansion of xk;t1 with respect to xt2. By the Laurent phenomenon, we can get that L1 is a Luarent monomial in

    R[x±11;t1,,ˆxk;t1,,x±1n;t1].

    Then by Lemma 3.6, L1 is a unit in R and [Σt1]=[Σt2].

    Case (ii): xk;t2=L1x1k;t1, in this case, xk;u=˜Mxk;t1. By the same argument in case (i), we can get that ˜M is a Luarent monomial in R[x±11;t1,,ˆxk;t1,,x±1n;t1]. Then by Lemma 3.6, ˜M is a unit in R and [Σt1]=[(xu,Fu)]=[μk(Σt2)]=μk([Σt2]).

    Remark 3.8. The same method also works for cluster algebras and one can get the similar result.

    The following definitions are natural generalizations of the corresponding notions of cluster algebras in [4].

    For i[1,n], we define the adjacent cluster xi by xi=(x{xi}){xi} where the cluster variables xi and xi are related by the exchange Laurent polynomial ˆFi. Let R[x±1] be the ring of Laurent polynomials in x1,,xn with coefficients in R.

    Definition 4.1. The upper bound U(Σ) and lower bound L(Σ) associated with a LP seed Σ=(x,F) is defined by

    U(Σ)=R[x±1]R[x±11]R[x±1n], L(Σ)=R[x1,x1,,xn,xn]

    Thus, L(Σ) is the R-subalgebra of F generated by the union of n+1 clusters x±1,x±11,,x±1n. Note that L(Σ)A(Σ)U(Σ).

    Remark 4.2. The method of the proof of results for LP algebras in this section is a little similar to those for cluster algebras in [4]. The concepts of LP algebras and cluster algebras are essentially different, since LP algebras and cluster algebras are not included with each other. In general, the calculation for LP algebras is more complicated. Now, we give the following three points to explain the specific differences between LP algebras and cluster algebras.

    (1) For cluster algebras, the exchange polynomials are binomials. While for LP algebras, those are multinomials, so that the calculation using the exchange polynomials becomes complicated.

    (2) For cluster algebras, coprimeness is necessary for the proof of properties for the upper bound and lower bound, and it is easy to check that the coprimeness keeps under mutations for a certain seed. While for LP algebras, the concept of coprimeness is not yet defined. In order to obtain a LP seed with coprimeness, we assume that a LP seed satisfies the condition that ˆFk=Fk for any k. But it is not obvious whether the condition keeps under mutations (see Example 4.12 which shows that the condition does not keep under mutations), so that in the proof that involves mutations and requires that condition, we need to show that the condition keeps under mutations.

    (3) In cluster algebras, the acyclic seed has good property that the lexicographically first monomial of its any exchange polynomial Fj is a monomial in {xi|i>j}. While in LP algebras, the concept of the acyclic seed is not yet defined. In order to obtain a LP seed with such good property, we assume that the LP seed satisfies certain conditions, see Condition 1.2.

    For any LP seed Σ=(x,F), the following lemma and corollary hold parallel to the corresponding results in [4].

    Lemma 4.3.

    U(Σ)=nj=1R[x±11,,x±1j1,xj,xj,x±1j+1,,x±1n]. (4.1)

    Proof. It is sufficient to show that

    R[x±1]R[x±11]=R[x1,x1,x±12,,x±1n].

    The inclusion is clear, we only need to prove the converse inclusion.

    For any yR[x±1]R[x±11], y is of the form y=Nm=Mcmxm1, where M,NZ0 and cmR[x±12,,x±1n]. If M0, it is easy to see that

    yR[x1,x±12,,x±1n]R[x1,x1,x±12,,x±1n].

    If MN0, from the definition of LP seeds, x1ˆF1, then

    y|x1ˆF1x1=Nm=Mcm(ˆF1x1)m=Mm=1cmˆFm1xm1+Nm=0cmˆFm1xm1.

    Since yR[x±11], y can be written as Np=Mcpxp1 where cpR[x±12,,x±1n], then we have cmˆFm1R[x±12,,x±1n]. Thus,

    y=Mm=1cmˆFm1xm1+Nm=0cmxm1R[x1,x1,x±12,,x±1n].

    If N=0, by similar discussion, we have yR[x1,x±12,,x±1n]R[x1,x1,x±12,,x±1n].

    Corollary 4.4. For j[1,n], yR[x±11,,x±1j1,xj,xj,x±1j+1,,x±1n] if and only if y is of the form y=Nm=Mcmxmj where M,NZ0, cmR[x±11,,^xj±1,,x±1n] and cm is divisible by ˆFmj in R[x±11,,^xj±1,,x±1n] for m[1,M].

    Lemma 4.5. Suppose that ˆFj=Fj for j[1,2], then R[x1,x±12]R[x±11,x2,x2]=R[x1,x2,x2].

    Proof. The inclusion is clear, we only need to prove the converse inclusion. For yR[x1,x±12]R[x±11,x2,x2], y is of the form y=mZxm1(cm+cm(x2)+cm(x2)), where cmR, cm(x2) and cm(x2) are polynomials over R without constant terms.

    Let M be the smaller integer such that cM+cM(x2)+cM(x2)0. If M0, then it is easy to see that yR[x1,x2,x2].

    Otherwise, the Laurent expression of y is mZxm1(cm+cm(x2)+cm(F2x2)) by the assumption. Let r2 be the sum of monomials in F2 without x1. Then there are nonzero terms with smallest power of x1 in the Laurent expression of y, which are xM1(cM+cM(x2)+cM(r2x2))0, which contradicts the condition that yR[x1,x±12].

    Lemma 4.6. Suppose that ˆFj=Fj for j[1,2], then

    R[x1,x1,x±12]=R[x1,x1,x2,x2]+R[x1,x±12].

    Proof. The inclusion is clear, we only need to prove the converse inclusion. It is enough to show that M,N>0, xN1xM2R[x1,x1,x2,x2]+R[x1,x±12].

    By the assumption, we have x2x2=ˆF2=F2=g(x1)+r2, where g(x1)=mi=1gixi1, giR and r20R since F2 is not divisible by x1. If g(x1)=0, then x12=r12x2, which implies that xN1xM2R[x1,x1,x2,x2].

    Otherwise, let p(x1)=g(x1)r2R[x1], then x2x2=g(x1)+r2 can be written as

    x12=r12x2+p(x1)x12.

    Repeatedly substituting x12 in the RHS of the above equation by r12x2+p(x1)x12, we obtain x12=P(x1,x2)+pN(x1)x12, where P(x1,x2)=r12x2N1i=0pi(x1)R[x1,x2].

    Then we have

    xN1xM2=xN1PM(x1,x2)+xN1pMN(x1)xM2+xN1M1i=1(Mi)(P(x1,x2))Mi(p(x1)Nx12)i, (4.2)

    where the first term of (4.2) that is, xN1PM(x1,x2)R[x1,x1,x2].

    For p(x1)=1r2g(x1)=1r2mi=1gixi1, the smallest power of x1 in pN(x1) is N and the greatest is Nm. Thus we can rewrite pN(x1) in the form xN1(N(m1)i=0pixi1) where piR, implying that for any integer K>0, we have pNK(x1)xN1R[x1]. Since x1x1=ˆF1=F1R[x2], we have xN1pNK(x1)R[x1,x2].

    Then the middle term of (4.2) is obvious in R[x1,x±12], and the last term of (4.2) is equal to xN1M1i=1(Mi)(P(x1,F2x2))Mi(p(x1)Nx12)iR[x1,x±12].

    Thus we finish the proof.

    Proposition 4.7. Suppose that n2 and ˆFj=Fj for j[1,n], then

    U(Σ)=nj=2R[x1,x1,x±12,,x±1j1,xj,xj,x±1j+1,,x±1n]. (4.3)

    Proof. Comparing (4.1) with (4.3), it is sufficient to show that

    R[x1,x1,x2,x2,x±13,,x±1n]=R[x1,x1,x±12,,x±1n]R[x±11,x2,x2,x±13,,x±1n].

    Freeze the cluster variables x3,,xn and view R[x±13,,x±1n] as the new ground ring R, then the above equality reduces to

    R[x1,x1,x2,x2]=R[x1,x1,x±12]R[x±11,x2,x2]. (4.4)

    Suppose F1=f(x2)+r1, F2=g(x1)+r2, where r10,r20R and f(x2),g(x1) are polynomials over R without constant terms. It is easy to see that Lemma 4.5 and Lemma 4.6 hold for four cases which are: (C1) x2F1 and x1F2, that is, f(x2)=0 and g(x1)=0; (C2) x2F1 and x1F2; (C3) x2F1 but x1F2; (C4) x2F1 but x1F2. Combining Lemma 4.5 and Lemma 4.6 with the fact that R[x1,x1,x2,x2]R[x±11,x2,x2], we obtain:

    R[x1,x1,x±12]R[x±11,x2,x2]=(R[x1,x1,x2,x2]+R[x1,x±12])R[x±11,x2,x2]=R[x1,x1,x2,x2]+(R[x1,x±12]R[x±11,x2,x2])=R[x1,x1,x2,x2]

    Thus we have (4.4).

    Lemma 4.8. For a LP seed (x,F), let x2 and x2 be the cluster variables exchanged with x2 in the LP seeds μ2(x,F) and μ2μ1(x,F) respectively, then

    R[x1,x1,x2,x2,x±13,,x±1n]=R[x1,x1,x2,x2,x±13,,x±1n]. (4.5)

    Proof. We can freeze the cluster variables x3,,xn and view R[x±13,,x±1n] as the new ground ring R. Then we will prove the following equality can be reduced from (4.5):

    R[x1,x1,x2,x2]=R[x1,x1,x2,x2].

    We first show that x2R[x1,x1,x2,x2].

    In (C1), it is easy to see that x2=rx2 for certain rR, which implies x2R[x1,x1,x2,x2].

    In (C2), let (x,F)=μ1(x,F), then x2 is obtained by x2x2=ˆF2. Recall that x1x1=ˆF1=F1=f(x2)+r1 and x2x2=ˆF2=F2=g(x1)+r2, where g(x1)=mi=1gixi1, giR and r20R. Because F2 depends on x1, from the definition of LP mutations, we have:

    1) G2=F2|x1N2=g(r1x1)+r2.

    2) H2=G2/c, where c is the product of all common factors of giri1 for i[1,m] and r2.

    3) F2=MH2=xm1H2=h(x1)+r3,

    where r3=gmrm1c, h(x1)=mi=1hixi1, hj={gmirmi1/cj[1,m1],r2/cj=m.

    By Proposition 2.4, there exist x2 in F1=F1 in (x,F), so that there is no x1 in F2/ˆF2, thus we have ˆF2=F2. It follows that

    x2x2=1cr2xm1+m1j=1hjxj1+r3=1c(x2x2g(x1))xm1+m1j=1hjxj1+r3=x2(1cx2xm1)(1cg(x1)xm1(m1j=1hjxj1)r3),

    where 1cg(x1)xm1=(mi=1gicxi1)xm1=mi=1gic(x1x1)ixmi1=mi=1gic(f(x2)+r1)ixmi1. Recall that f(x2) is a polynomial in x2 without constant terms, then gmc(f(x2)+r1)m can be written as x2Pm+gmrm1c=x2Pm+r3, where Pm is a polynomial in x2.

    For i[1,m1], we have gic(f(x2)+r1)ixmi1=x2Pi+giri1cxmi1=x2Pi+hmixmi1, where Pi is a polynomial in x2.

    Then 1cg(x1)xm1(m1j=1hjxj1)r3=x2(mi=1Pi), which implies that

    x2x2=x2(1cx2xm1mi=1Pi).

    Thus x2R[x1,x1,x2,x2].

    For (C3) and (C4), it is enough to show for (C3) by symmetry. At this time, F2 is the same as that in (C2). Since f(x2)=0, (F1=F1)|x2F2/x2=r1 is not divisible by F2, so that ˆF2=F2. As a consequence, we have x2x2=x2(1cx2xm1). Thus x2R[x1,x1,x2,x2].

    On the other hand, we can prove similarly that x2R[x1,x1,x2,x2]. Then, (4.5) follows truely.

    Theorem 4.9. Assume that a LP seed Σ=(x,F) satisfied ˆFj=Fj for j[1,n] and Σ=(x,F) is the LP seed obtained from the LP seed Σ by mutation in direction k. Then the corresponding upper bounds coincide, that is, U(Σ)=U(Σ).

    Proof. Without loss of generality, we assume that k=1. Combining Proposition 4.7 and Lemma 4.8, we finish the proof.

    Proposition 4.10. If the exchange polynomials of a LP seed satisfy ˆFk=Fk for any k[1,n], then FiFk for any ik. Furthermore, any two of the exchange polynomials {Fk|k[1,n]}of a LP seed are coprime.

    Proof. We will prove by contradiction. If Fi=Fk, then

    ˆFi|xkFk/xk=Fi|xkFk/xk=Fk|xkFk/xk=Fk

    for xkFk, implying that Fk divides ˆFi|xkFk/xk, which contradicts the definition of exchange Laurent polynomials.

    Besides, since the irreducibility of exchange polynomials for LP seeds, we conclude that the exchange polynomials of a LP seed are coprime under the condition that ˆFk=Fk for any k[1,n].

    Remark 4.11. When a cluster seed is a LP seed, the coprimeness of the cluster seed is equivalent to the condition that ˆFk=Fk for any k[1,n].

    Example 4.12. Consider the LP seed (x,F)={(a,b+c),(b,a+c),(c,a+(a+1)b)} over R=Z, which satisfies the condition that ˆFk=Fk for any k{a,b,c}. Then the LP seed obtained by mutation at b is

    (x,F)={(a,1+d),(d,a+c),(c,a+d+1)}

    where d=a+Cb. It is easy to see that ^Fc=Fca, meaning that the condition that ˆFk=Fk for any k for a LP seed does not keep under mutations.

    Definition 4.13. Let Σ=(x,F) be a LP seed, the upper LP algebra ¯A(Σ) defined by Σ is the intersection of the subalgebras U(Σ) for all LP seeds Σ mutation-equivalent to Σ.

    Theorem 4.9 has the following direct implication.

    Corollary 4.14. Assume that all LP seeds mutation equivalent to a LP seed Σ=(x,F) satisfy the condition that ˆFj=Fj for j[1,n], then the upper bound U(Σ) is independent of the choice of LP seeds mutation-equivalent to Σ, and so is equal to the upper LP algebra ¯A(Σ).

    Definition 4.15. Let (x,F) be a LP seed. A standard monomial in {xi,xi|i[1,n]} is a monomial that contains no product of the form xixi.

    Let xa=xa11xann be a Laurent monomial where aZn. For a Laurent polynomial in x1,,xn, we order the each two terms xa and xa lexicographically as follows:

    aa if the first nonzero difference ajaj is positive. (4.6)

    We set the term with the smallest lexicographical order as the first term in a Laurent polynomial.

    Theorem 4.16. Assume that a LP seeds Σ=(x,F) satisfies

    1) ˆFj=Fj for j[1,n].

    2) in any Fj, the lexicographically first monomial is of the form

    xvj={xvj+1,jj+1xvn,jnj[1,n1]1j=n

    where vjZnj0 for j[1,n1].

    Then the standard monomials in x1,x1,,xn,xn form an R-basis for L(Σ).

    Proof. The proof is using the same technique as in [4]. We denote the standard monomials in x1,x1,,xn,xn by x(a)=x(a1)1x(an)n, where a=(a1,,an)Zn and

    x(ai)i={xaii,ai0xaii,ai<0.

    Note that x(a) is a Laurent polynomial in x1,,xn and for any i, we have x(1)i=xi=x1iˆFi=x1iFi. By the assumption for Fi, it follows that the lexicographically first monomial in x(1)i is x1ixvi, then the power of xi in x(ai)i is ai and there is no x1,,xi1 in x(ai)i. Then the lexicographically first monomial in x(a) is the product of xaii(ai>0) and xaii(xvi)ai(ai<0).

    We assume that aa such that ai=ai for i[1,k1] and ak<ak. Let P, M and Q be the lexicographically first monomial of k1j=1x(aj)j, x(ak)k and nj=k+1x(aj)j respectively. Then the lexicographically first monomial of x(a) is PMQ, similarly that of x(a) is PMQ.

    Since ai=ai for i[1,k1], we have P=P=nj=1xpjj.

    Since ak<ak and the power of xk in x(ak)k is ak and there is no xi (i[1,k1]) in x(ak)k, we obtain M=xakknj=k+1xmjj and M=xakknj=k+1xmjj.

    And Q=(nj=k+1,aj>0xajj)(nj=k+1,aj<0xajj(xvj)aj)=nj=k+1xqjj, similarly Q=nj=k+1xqjj.

    It follows that

    PMQ=(k1j=1xpjj)(xpk+akk)(nj=k+1xpj+mj+qjj), PMQ=(k1j=1xpjj)(xpk+akk)(nj=k+1xpj+mj+qjj).

    Thus PMQPMQ, implying that

    if aa, the lexicographically first monomial of x(a)that of x(a). (4.7)

    The linearly independence of standard monomials over R follows at once from (4.7). Since the product xixi for any i equals to ^Fi=Fi, which is the linear combination of standard monomials in x1,x1,,xn,xn. Thus they form a basis for L(Σ).

    In the following statements, we always assume that Σ=(x,F) is a LP seed of rank n satisfying Condition 1.2.

    Notation 4.17. We denote by the algebra homomorphism defined as the composition , where

    We denote by (resp. ) the -linear span (resp. -linear span) of the standard monomials in .

    Lemma 4.18. .

    Proof. For any , replace with , then . Thus we have . It follows that

    Similarly using the tool of the proof of Theorem 4.16, For , the lexicographically first monomial of is a Laurent monomial in whose the power of is , implying that if , then the lexicographically first monomial of precedes the one of .

    Then the restriction of to is injective.

    Notation 4.19. Given a Laurent polynomial , we denote by as the sum of all Laurent monomials with the smallest power of in the Laurent expansion of with nonzero coefficient.

    The following results parallel to Lemmas 6.4 and 6.5 in [4] can be obtained similarly.

    Lemma 4.20. Suppose that where and , then .

    Lemma 4.21. .

    Lemma 4.22. Im, where

    Proof. By Condition 1.2, we have Thus the inclusion is clear.

    Let be the set of indexes satisfying . We set . To prove the converse inclusion, it is enough to show that Im() for .

    For , let be a Laurent monomial in . Moreover, we set . Then we have any can be written as satisfying

    Define the multiplicative monoid . Then if and only if

    (4.8)

    By the equivalence condition (4.8) of , we obtain for , implying that it suffices to show that Im.

    For , we prove that Im by induction on the degree of . When , we have Im. Assume that and for any such that , then Im.

    Let , then we have by the equivalence condition (4.8) of . As a consequence, Im under the induction assumption. If , then Im so that Im.

    Otherwise, since , there exist such that , where implies that . Fix such an index . By (iv) of Condition 1.2, and , where is the number of terms of , and satisfying and .

    From the definition of LP mutations and Condition 1.2(i)(ii), we have . By multiplying both sides of that equation by , we have

    Since Im, we only need to show that for , Im.

    Define , where and for . Because , the equivalence condition (4.8) of can be written as

    (a) for , ;

    (b) for , .

    The inequalities of (a) are immediate from the definition of and the choice of . And for inequality (b), we discuss in several cases:

    if , (b) is equivalent to .

    if , we have , (b) is equivalent to .

    if , when , (b) is equivalent to , when , , then LHS of (b) is zero.

    Since and inequalities of (a) hold, we have inequality (b) holds for . Thus belongs to with , so that Im.

    Then we have

    The claim Im is a consequence of the statement that . Indeed, Im.

    The only variable with negative power (namely, ) in is , since

    where for . Then we have

    For is the fixed index such that , , then the power of is nonnegative.

    For , we have

    From the definition of , we obtain , and it is easy to see that and by the choice of and . Then . Thus the power of is nonnegative. Hence the power of any cluster variable is nonnegative. It follows that .

    By the same technique as in [4], we give the following theorem.

    Theorem 4.23. If a LP seed satisfying Condition 1.2, .

    Proof. We apply the induction on , that is, the rank of the LP seed. When , by Lemma 4.3, we have . Assume that and the statement holds for all algebras of rank 2 to . Then we consider about rank .

    By Lemma 4.3, we have

    For the seed obtained from by freezing at , by the induction assumption, we have , that is, . Then it is enough to show that

    (4.9)

    The inclusion is clear, we only need to prove the converse inclusion.

    For LHS of (4.9), let be the smallest power of in . Then can be written as where . By Lemma 4.20, we have

    If , by Lemma 4.21, we have the RHS of (4.9).

    Otherwise, we apply the induction on . Since , by Lemma 4.4 we have is divisible by , that is for certain .

    When , we have Im according to Lemma 4.22. Then Im.

    When , we consider the LP seed obtained from by freezing at and removing . In view of Lemma 4.22, we have Im. Besides, by the induction assumption, we have . Using Lemma 4.3, we obtain Im.

    For certain , can be written as , where . Since Im, by Corollary 4.4 is divisible by for . By Proposition 4.10, and are coprime, implying that is divisible by . Using Corollary 4.4 again, we have .

    By the arbitrariness of , we obtain Im.

    Then there exist such that . It implies that

    Then we have .

    Corollary 4.24. If a LP seed satisfied Condition 1.2, then the standard monomials in form an -basis of the LP algebra .

    Proof. It is immediately from Theorem 4.16 and Theorem 4.9.

    Example 4.25. Consider the LP seed , it is easy to see that Condition 1.2 hold. Since and , of Condition 1.2 holds. Besides, , Then it is clear that Im and Im. Thus by Theorem 4.9, we have .

    Note that this LP seed is not a cluster seed or a generalized cluster seed for since .

    The cluster seed is acyclic if and only if there exist a permutation such that for , . Renumbering if necessary the indexes of the initial acyclic cluster, we assume that for , . Then by the exchange polynomials for cluster algebras, we conclude that the cluster seed is acyclic if and only if for any , .

    Proposition 4.26. Condition 1.2 is equivalent to acyclicity and coprimeness of exchange polynomials for a cluster seed which is a also LP seed.

    Proof. When a cluster seed is a LP seed, recall that (i) in Condition 1.2 is equivalent to coprimeness of exchange polynomials by Remark 4.11.

    When a cluster seed satisfies the conditions (i) and (ii), for any , since is a binomial, we have . If for some , then for , which contradicts to the condition (ii). For , . From the definition of exchange polynomials for cluster algebras, we have is of the form . For , since for any , we have by the above discussion, so that we obtain . Then the cluster seed satisfied the conditions (i) and (ii) is acyclic. Besides, it is easy to see that when a cluster seed is acyclic, it satisfies the conditions (i) and (ii).Thus the conditions (i) and (ii) are equivalent to acyclicity of exchange polynomials.

    Under the conditions of acyclicity and coprimeness, the cluster seed in fact satisfies (iii) and (iv) in Condition 1.2.

    This project is supported by the National Natural Science Foundation of China (No.12071422 and No.12131015).

    The authors declare there is no conflicts of interest.



    [1] A. Childs, B. Chandrasekar, N. Balakrishnan, D. Kundu, Exact likelihood inference based on Type-Ⅰ and Type-Ⅱ hybrid censored samples from the exponential distribution, Ann. Inst. Stat. Math., 55 (2003), 319–330. https://doi.org/10.1007/BF02530502 doi: 10.1007/BF02530502
    [2] B. Chandrasekar, A. Childs, N. Balakrishnan, Exact likelihood inference for the exponential distribution under generalized Type-Ⅰ and Type-Ⅱ hybrid censoring, Nav. Res. Logis., 51(2004), 994–1004. https://doi.org/10.1002/nav.20038 doi: 10.1002/nav.20038
    [3] N. Balakrishnan, A. Rasouli, S. N. Farsipour, Exact likelihood inference based on an unified hybrid censored sample from the exponential distribution, J. Stat. Comput. Simul., 78 (2008), 475–488. https://doi.org/10.1080/00949650601158336 doi: 10.1080/00949650601158336
    [4] D. Kundu, H. Howlader, Bayesian inference and prediction of the inverse Weibull distribution for Type-Ⅱ censored data, Comput. Stat. Data Anal., 54 (2010), 1547–1558. https://doi.org/10.1016/j.csda.2010.01.003 doi: 10.1016/j.csda.2010.01.003
    [5] H. Panahi, Estimation of the Burr type Ⅲ distribution with application in unified hybrid censored sample of fracture toughness, J. Appl. Stat., 44 (2017), 2575–2592. https://doi.org/10.1080/02664763.2016.1258549 doi: 10.1080/02664763.2016.1258549
    [6] M. G. M. Ghazal, H. M. Hasaballah, Bayesian Prediction based on unified hybrid censored data from the exponentiated Rayleigh distribution, J. Stat. Appl. Probabil. Lett., 5 (2018), 103–118. https://doi.org/10.18576/jsapl/050301 doi: 10.18576/jsapl/050301
    [7] S. A. Lone, H. Panahi, Estimation procedures for partially accelerated life test model based on unified hybrid censored sample from the Gompertz distribution, Eksploat. i Niezawodn., 24 (2022), 427–436. https://doi.org/10.17531/ein.2022.3.4 doi: 10.17531/ein.2022.3.4
    [8] N. Balakrishnan, Progressive censoring methodology: An appraisal (with discussion), Test, 16 (2007), 211–296. https://doi.org/10.1007/s11749-007-0061-y doi: 10.1007/s11749-007-0061-y
    [9] T. Zhu, Z. Yan, X. Peng, A Weibull failure model to the study of the hierarchical Bayesian reliability, Eksploat. i Niezawodn., 18 (2016), 501–506. http://dx.doi.org/10.17531/ein.2016.4.4. doi: 10.17531/ein.2016.4.4
    [10] J. Gorny, E. Cramer, Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring, Metrika, 81 (2018), 173–210. https://doi.org/10.1007/s00184-017-0639-7 doi: 10.1007/s00184-017-0639-7
    [11] J. Kim, K. Lee, Estimation of the Weibull distribution under unified progressive hybrid censored data, J. Korean Data Anal. Soc., 20 (2018), 2189–2199. http://dx.doi.org/10.37727/jkdas.2018.20.5.2189 doi: 10.37727/jkdas.2018.20.5.2189
    [12] M. E. Ghitany, A compound Rayleigh survival model and its application to randomly censored data, Stat. Pap., 42 (2001), 437–450. https://doi.org/10.1007/s003620100072 doi: 10.1007/s003620100072
    [13] H. Panahi, N. Moradi, Estimation of the inverted exponentiated Rayleigh Distribution Based on Adaptive Type Ⅱ Progressive Hybrid Censored Sample, J. Comput. Appl. Math., 364 (2020), 112345. https://doi.org/10.1016/j.cam.2019.112345 doi: 10.1016/j.cam.2019.112345
    [14] W. K. Ghafi, T. J. Khraibet, A. A. Alwan, Maximum likelihood and Bayesian estimation of rayleigh with partly interval-censored case-Ⅰ data, NeuroQuantology, 18 (2020), 26–28. https://doi.org/10.14704/nq.2020.18.5.NQ20163 doi: 10.14704/nq.2020.18.5.NQ20163
    [15] S. Asadi, H. Panahi, C. Swarup, S. A. Lone, Inference on adaptive progressive hybrid censored accelerated life test for Gompertz distribution and its evaluation for virus-containing micro droplets data, Alex. Eng. J., 61 (2022), 10071–10084. https://doi.org/10.1016/j.aej.2022.02.061 doi: 10.1016/j.aej.2022.02.061
    [16] S. A. Lone, H. Panahi, I. Shah, Bayesian prediction interval for a constant-stress partially accelerated life test model under censored data, J. Taibah. Univ. Sci., 15 (2021), 1178–1187. https://doi.org/10.1080/16583655.2021.2023847 doi: 10.1080/16583655.2021.2023847
    [17] D. Kundu, B. Pradhan, Bayesian inference and life testing plans for generalized exponential distribution, Sci. China Ser. A.-Math., 52 (2009), 1373–1388. https://doi.org/10.1007/s11425-009-0085-8 doi: 10.1007/s11425-009-0085-8
    [18] L. L. Zhuang, A. Xu, X. L. Wang, A prognostic driven predictive maintenance framework based on Bayesian deep learning, Reliab. Eng. Syst. Safe., 234 (2023), 109181. https://doi.org/10.1016/j.ress.2023.10918 doi: 10.1016/j.ress.2023.10918
    [19] C. Luo, L. Shen, A. Xu, Modelling and estimation of system reliability under dynamic operating environments and lifetime ordering constraints, Reliab. Eng. Syst. Safe., 218 (2022), 108136. https://doi.org/10.1016/j.ress.2021.108136 doi: 10.1016/j.ress.2021.108136
    [20] S. Liu, W. Gui, Statistical inference for bathtub-shaped distribution based on generalized progressive hybrid censored data, Commun. Stat.-Theory Methods, 51 (2022), 8149–8172. https://doi.org/10.1080/03610926.2021.1889602 doi: 10.1080/03610926.2021.1889602
    [21] A. Xu, S. Zhou, Y. Tang, A unified model for system reliability evaluation under dynamic operating conditions, IEEE Trans. Reliab., 70 (2021), 65–72. https://doi.org/10.1109/TR.2019.2948173 doi: 10.1109/TR.2019.2948173
    [22] S. Park, N. Balakrishnan, S. W. Kim, Fisher information in progressive hybrid censoring schemes, Statistics, 45 (2011), 623–631. https://doi.org/10.1080/02331888.2010.504988 doi: 10.1080/02331888.2010.504988
    [23] A. P. Dempste, N. M. Laird, D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. B: Stat. Methodol., 39 (1977), 1–38. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x doi: 10.1111/j.2517-6161.1977.tb01600.x
    [24] T. R. Tsai, Y. Lio, W. C. Ting, EM Algorithm for Mixture Distributions Model with Type-Ⅰ Hybrid Censoring Scheme, Mathematics, 9 (2021), 2483. https://doi.org/10.3390/math9192483 doi: 10.3390/math9192483
    [25] L. A. Ferreira, J. L. Silva, Parameter estimation for Weibull distribution with right censored data using EM algorithm, Eksploat. i Niezawodn., 19 (2017), 310–315. https://doi.org/10.17531/ein.2017.2.20 doi: 10.17531/ein.2017.2.20
    [26] J. Diebolt, G. Celeux, Asymptotic properties of a stochastic EM algorithm for estimating mixing proportions, Commun. Stat. Stoch. Models, 9 (1993), 599–613. https://doi.org/10.1080/15326349308807283 doi: 10.1080/15326349308807283
    [27] M. Zhang, Z. Ye, M. Xie, A stochastic EM algorithm for progressively censored data analysis, Qual. Reliab. Eng. Int., 30 (2014), 711–722. https://doi.org/10.1002/qre.1522 doi: 10.1002/qre.1522
    [28] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller, Equations of state calculations by fast computing machines, J. Chem. Phys., 21 (1953), 1087–1092. https://doi.org/10.1063/1.1699114 doi: 10.1063/1.1699114
    [29] W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57 (1970), 97–109. https://doi.org/10.2307/2334940 doi: 10.2307/2334940
    [30] R. Di, P. Wang, C. He, Z. Guo, Constrained adjusted maximum a posteriori estimation of Bayesian network parameters, Entropy, 23 (2021), 1283. https://doi.org/10.3390/e23101283 doi: 10.3390/e23101283
    [31] D. N. P. Murthy, M. Xie, R. Jiang, Weibull models, Wiley, New York, 2003. http://dx.doi.org/10.1002/047147326X
    [32] P. Congdon, Applied Bayesian Modelling, John Wiley & Sons, Chichester, 2014.
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