D-finiteness: algorithms and applications

B. Salvy
{"title":"D-finiteness: algorithms and applications","authors":"B. Salvy","doi":"10.1145/1073884.1073886","DOIUrl":null,"url":null,"abstract":"Differentially finite series are solutions of linear differential equations with polynomial coefficients. P-recursive sequences are solutions of linear recurrences with polynomial coefficients. Corresponding notions are obtained by replacing classical differentiation or difference operators by their q-analogues. All these objects share numerous properties that are described in the framework of \"D-finiteness\". Our aim in this area is to enable computer algebra systems to deal in an algorithmic way with a large number of special functions and sequences. Indeed, it can be estimated that approximately 60% of the functions described in Abramowitz & Stegun's handbook [1] fall into this category, as well as 25% of the sequences in Sloane's encyclopedia [20,21]. In a way, D-finite sequences or series are non-commutative analogues of algebraic numbers: the role of the minimal polynomial is played by a linear operator.Ore [14] described a non-commutative version of Euclidean division and extended Euclid algorithm for these linear operators (known as Ore polynomials). In the same way as in the commutative case, these algorithms make several closure properties effective (see[22]). It follows that identities between these functions or sequences can be proved or computed automatically. Part of the success of the gfun package [17] comes from an implementation of these operations. Another part comes from the possibility of discovering such identities empirically, with Padé-Hermite approximants on power series [2] taking the place of the LLL algorithm on floating-point numbers. The discovery that a series is D-finite is also important from the complexity point of view: several operations can be performed on D-finite series at a lower cost than on arbitrary power series. This includes multiplication, but also evaluation at rational points by binary splitting [4]. A typical application is the numerical evaluation of π in computer algebra systems; we give another one in these proceedings [3]. Also, the local behaviour of solutions of linear differential equations in the neighbourhood of their singularities is well understood [9] and implementations of algorithms computing the corresponding expansions are available [24, 13]. This gives access to the asymptotics of numerous sequences or to analytic proofs that sequences or functions cannot satisfy such equations [10]Results of a more algebraic nature are obtained by differential Galois theory [18, 19], which naturally shares many subroutines with algorithms for D-finite series. The truly spectacular applications of D-finiteness come from the multivariate case: instead of series or sequences, one works with multivariate series or sequences, or with sequences of series or polynomials,.... They obey systems of linear operators that may be of differential, difference, q-difference or mixed types, with the extra constraint that a finite number of initial conditions are sufficient to specify the solution. This is a non-commutative analogue of polynomial systems with a finite number of solutions. It turns out that, as in the polynomial case, Gröbner bases give algorithmic answers to many decision questions, by providing normal forms in a finite dimensional vector space. This has been observed first in the differential case [11, 23] and then extended to the more general multivariate Ore case [8]. A crucial insight of Zeilberger [27, 15] is that elimination in this non-commutative setting computes definite integrals or sums. This is known as creative telescoping. In thehypergeometric setting (when the quotient is a vector space of dimension1), a fast algorithm for this operation is known as Zeilberger's fast algorithm [26]. In the more general case, Gröbner bases are of help in this elimination. This is true in the differential case [16, 25] and to a large extent in the more general multivariate case [8]. Also, Zeilberger's fast algorithm has been generalized to the multivariate Ore case by Chyzak [5, 6]. Still, various efficiency issues remain and phenomena of non-minimality of the eliminated operators are not completely understood. A further generalization of D-finite series is due to Gessel [12] who developed a theory of symmetric series. These series are such than when all but a finite number of their variables (in a certain basis) are specialized to0, the resulting series is D-finite in the previous sense. Closure properties under scalar product lead to proofs of D-finiteness (in the classical sense) for various combinatorial sequences. Again, algorithms based on Gröbner bases make these operations effective [7]. The talk will survey the nicest of these algorithms and their applications. I will also indicate where current work is in progress, or where more work is needed.","PeriodicalId":311546,"journal":{"name":"Proceedings of the 2005 international symposium on Symbolic and algebraic computation","volume":"424 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2005 international symposium on Symbolic and algebraic computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1073884.1073886","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

Abstract

Differentially finite series are solutions of linear differential equations with polynomial coefficients. P-recursive sequences are solutions of linear recurrences with polynomial coefficients. Corresponding notions are obtained by replacing classical differentiation or difference operators by their q-analogues. All these objects share numerous properties that are described in the framework of "D-finiteness". Our aim in this area is to enable computer algebra systems to deal in an algorithmic way with a large number of special functions and sequences. Indeed, it can be estimated that approximately 60% of the functions described in Abramowitz & Stegun's handbook [1] fall into this category, as well as 25% of the sequences in Sloane's encyclopedia [20,21]. In a way, D-finite sequences or series are non-commutative analogues of algebraic numbers: the role of the minimal polynomial is played by a linear operator.Ore [14] described a non-commutative version of Euclidean division and extended Euclid algorithm for these linear operators (known as Ore polynomials). In the same way as in the commutative case, these algorithms make several closure properties effective (see[22]). It follows that identities between these functions or sequences can be proved or computed automatically. Part of the success of the gfun package [17] comes from an implementation of these operations. Another part comes from the possibility of discovering such identities empirically, with Padé-Hermite approximants on power series [2] taking the place of the LLL algorithm on floating-point numbers. The discovery that a series is D-finite is also important from the complexity point of view: several operations can be performed on D-finite series at a lower cost than on arbitrary power series. This includes multiplication, but also evaluation at rational points by binary splitting [4]. A typical application is the numerical evaluation of π in computer algebra systems; we give another one in these proceedings [3]. Also, the local behaviour of solutions of linear differential equations in the neighbourhood of their singularities is well understood [9] and implementations of algorithms computing the corresponding expansions are available [24, 13]. This gives access to the asymptotics of numerous sequences or to analytic proofs that sequences or functions cannot satisfy such equations [10]Results of a more algebraic nature are obtained by differential Galois theory [18, 19], which naturally shares many subroutines with algorithms for D-finite series. The truly spectacular applications of D-finiteness come from the multivariate case: instead of series or sequences, one works with multivariate series or sequences, or with sequences of series or polynomials,.... They obey systems of linear operators that may be of differential, difference, q-difference or mixed types, with the extra constraint that a finite number of initial conditions are sufficient to specify the solution. This is a non-commutative analogue of polynomial systems with a finite number of solutions. It turns out that, as in the polynomial case, Gröbner bases give algorithmic answers to many decision questions, by providing normal forms in a finite dimensional vector space. This has been observed first in the differential case [11, 23] and then extended to the more general multivariate Ore case [8]. A crucial insight of Zeilberger [27, 15] is that elimination in this non-commutative setting computes definite integrals or sums. This is known as creative telescoping. In thehypergeometric setting (when the quotient is a vector space of dimension1), a fast algorithm for this operation is known as Zeilberger's fast algorithm [26]. In the more general case, Gröbner bases are of help in this elimination. This is true in the differential case [16, 25] and to a large extent in the more general multivariate case [8]. Also, Zeilberger's fast algorithm has been generalized to the multivariate Ore case by Chyzak [5, 6]. Still, various efficiency issues remain and phenomena of non-minimality of the eliminated operators are not completely understood. A further generalization of D-finite series is due to Gessel [12] who developed a theory of symmetric series. These series are such than when all but a finite number of their variables (in a certain basis) are specialized to0, the resulting series is D-finite in the previous sense. Closure properties under scalar product lead to proofs of D-finiteness (in the classical sense) for various combinatorial sequences. Again, algorithms based on Gröbner bases make these operations effective [7]. The talk will survey the nicest of these algorithms and their applications. I will also indicate where current work is in progress, or where more work is needed.
d -有限:算法和应用
微分有限级数是多项式系数线性微分方程的解。p递归序列是多项式系数线性递归的解。通过用它们的q类似物代替经典的微分或差分算子,得到了相应的概念。所有这些对象都具有在“d有限”框架中描述的许多属性。我们在这个领域的目标是使计算机代数系统能够以算法的方式处理大量的特殊函数和序列。事实上,可以估计,Abramowitz & Stegun的手册[1]中描述的大约60%的函数属于这一类,Sloane的百科全书[20,21]中描述的序列也有25%属于这一类。在某种程度上,d -有限序列或级数是代数数的非交换类似物:最小多项式的角色由线性算子扮演。Ore[14]描述了欧几里得除法的非交换版本,并扩展了这些线性算子(称为Ore多项式)的欧几里得算法。与交换情况相同,这些算法使几个闭包属性有效(参见[22])。由此可见,这些函数或序列之间的恒等式可以自动证明或计算。gfun包[17]的成功部分来自于这些操作的实现。另一部分来自于从经验上发现这种恒等式的可能性,幂级数[2]上的pad - hermite近似代替浮点数上的LLL算法。从复杂度的角度来看,级数是d有限的这一发现也很重要:在d有限级数上执行的一些操作比在任意幂级数上执行的操作成本更低。这包括乘法,还包括通过二进制分割[4]在有理点处求值。一个典型的应用是计算机代数系统中π的数值计算;我们在这个过程中再给一个。此外,线性微分方程的解在其奇点附近的局部行为已被很好地理解[24,13],并且计算相应展开式的算法也已实现[24,13]。这就得到了许多序列的渐近性,或者序列或函数不能满足这样的方程的解析证明[10]。微分伽罗瓦理论[18,19]得到了更具代数性质的结果,它与d有限序列的算法自然地共享了许多子程序。d有限的真正引人注目的应用来自于多变量情况:不是级数或序列,而是处理多变量级数或序列,或级数或多项式的序列,....它们服从可能是微分、差分、q-差分或混合类型的线性算子系统,附加约束是有限个数的初始条件足以确定解。这是一个具有有限个数解的多项式系统的非交换模拟。事实证明,在多项式情况下,Gröbner基通过在有限维向量空间中提供范式,为许多决策问题提供算法答案。这首先在微分情况下观察到[11,23],然后扩展到更一般的多变量矿石情况[8]。Zeilberger[27,15]的一个重要见解是,在这种非交换设置中的消去计算定积分或和。这就是所谓的创造性伸缩。在超几何设置中(当商是维数为1的向量空间时),此操作的快速算法称为Zeilberger快速算法[26]。在更一般的情况下,Gröbner碱基有助于这种消除。这在微分情况[16,25]中是正确的,在很大程度上在更一般的多元情况[8]中也是如此。此外,Zeilberger的快速算法已被Chyzak推广到多元的Ore情况[5,6]。然而,各种效率问题仍然存在,并且被淘汰的操作人员的非极小现象尚未完全理解。对d有限级数的进一步推广是由Gessel[12]提出的,他提出了对称级数理论。这些级数是这样的,当除了有限数量的变量(在一定的基础上)都专门化到0时,得到的级数在前面的意义上是d有限的。标量积下的闭包性质导致了各种组合序列的d有限性(经典意义上的)证明。同样,基于Gröbner基数的算法使这些操作变得有效。这次演讲将探讨这些算法中最好的一些及其应用。我还将指出当前工作正在进行的地方,或者需要更多工作的地方。
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