Abstract
In this paper, we generalize the study of finite sequences of orthogonal polynomials from one to two variables. In doing so, twenty three new classes of bivariate finite orthogonal polynomials are presented, obtained from the product of a finite and an infinite family of univariate orthogonal polynomials. For these new classes of bivariate finite orthogonal polynomials, we present a bivariate weight function, the domain of orthogonality, the orthogonality relation, the recurrence relations, the second-order partial differential equations, the generating functions, as well as the parameter derivatives. The limit relations among these families are also presented in Labelle’s flavor.
Keywords:
orthogonal polynomial; weight function; differential equation; recurrence relation; generating function MSC:
33C50
1. Introduction
Let us consider a second-order linear differential equation of the form:
where a, b, c, d, and e are all real parameters and n is a nonnegative integer. The problem of finding all linear second-order differential equations of the Sturm–Liouville type with polynomial coefficients having orthogonal polynomial solutions goes back to Bochner [1] in 1929. Under some assumptions about the parameters a, b, c, d, and e, for each n, the differential equation can have orthogonal polynomial solutions [2,3]. If we also impose that the weight function for the orthogonality is positive, then we obtain the three very classical orthogonal polynomial sequences of Jacobi, orthogonal with respect to the beta weight function, Laguerre, orthogonal with respect to the gamma weight function, and Hermite, orthogonal with respect to the normal weight function. These three families are infinite sequences in the sense that, for each nonnegative integer n, there exists one element of the family that is a polynomial of degree n. Recently, Masjed-Jamei [4] studied three other finite classes of hypergeometric orthogonal polynomials, which are special solutions to (1). These three families, denoted by , , and , are finitely orthogonal with respect to the F sampling distribution, inverse Gamma distribution, and T sampling distribution.
Very classical families of univariate orthogonal polynomials have been enlarged to classical univariate orthogonal polynomials [2,3]. Following [5] (p. 189), we recall the following definition of univariate classical orthogonal polynomials [6]: “An orthogonal polynomial sequence is classical if it is a special case or limiting case of the polynomials given by the q-Racah or the Askey-Wilson polynomials”. We refer to [2] for the required notations, as well as the definitions of q-Racah and Askey–Wilson polynomials. The limit transitions among all univariate families known as the “Askey scheme”—or Tableau d’Askey—was nicely designed by J. Labelle [7], and it appears, e.g., in [2].
In [8], Güldoğan et al. introduced some new finite classes of two-variable orthogonal polynomials derived from two finite orthogonal polynomials in one variable by means of Koornwinder’s method [9], giving some properties of these families. In [10], Fourier transforms of the finite sets defined in [8] were studied, and some new orthogonal functions were obtained via Parseval’s identity, presenting some limit relationships between finite and infinite sequences of orthogonal polynomials in two variables.
In this paper, we constructed some new classes of two-variable finite orthogonal polynomials obtained from the product of a finite and an infinite family of univariate orthogonal polynomials. Furthermore, some transitions are given, and some new families are defined by taking the limit of the bivariate orthogonal polynomials. For the 23 new families introduced, recurrence relations, generating functions, and second-order partial differential equations are presented. Since we obtained a large number of finite families of bivariate orthogonal polynomials, we shall just give the details of the proofs for the first family in Section 3.1. The other results can be obtained mutatis mutandis, and they deserve also to be presented in Section 3.2, Section 3.3, Section 3.4, Section 3.5, Section 3.6, Section 3.7, Section 3.8, Section 3.9, Section 3.10, Section 3.11, Section 3.12, Section 3.13, Section 3.14, Section 3.15, Section 3.16, Section 3.17, Section 3.18, Section 3.19, Section 3.20, Section 3.21, Section 3.22 and Section 3.23. The results were checked with the help of Mathematica [11].
The main aims of this study were to increase and extend the number of families of finite orthogonal polynomials and to provide tools that can serve as inspiration for future studies.
The work is organized as follows. In Section 2, we recall the basic properties of univariate orthogonal polynomials, both infinite and finite situations. In Section 3, we introduce 23 new families of finite bivariate orthogonal polynomials. For each family, we present the polynomials as the product of a finite and an infinite family of univariate orthogonal polynomials described in Section 2. The orthogonality weight function, as well as the domain and orthogonality relation are explicitly given. Next, recurrence relations, second-order partial differential equations, and generating functions are derived. Furthermore, parameter derivatives are also investigated. Finally, the limit relations in Labelle’s flavor [7] are given. As already mentioned, the sketch of the proofs will just be given for the first family.
2. Infinite and Finite Univariate Families of Orthogonal Polynomials
Let us recall the general properties of the infinite sequences of the Jacobi, Laguerre, and Hermite polynomials and the sets of finite orthogonal polynomials , , and .
2.1. The Jacobi Polynomials
The Jacobi polynomial is defined by the explicit series [2] (p. 216):
where denotes the Gamma function. These polynomials are orthogonal on the interval with respect to the (beta) weight function . Jacobi polynomials satisfy the orthogonality relation:
where , , and is the Kronecker delta [12]. The set of polynomials has the generating functions (see, e.g., [13] (p. 82, Equations (1) and (3)), as well as [14] for the use of generating functions in discrete mathematics):
where and
where
Jacobi polynomials satisfy the following four recurrence relations [15,16]:
where stands for the Pochhammer symbol defined by for and .
Furthermore, we have the following limit relations between the Jacobi and Laguerre polynomials introduced in the next section (see, e.g., [2] (Equation (9.8.16)) or [16,17]):
and
Furthermore, for , we can compute the parameter derivatives of Jacobi polynomials with respect to or , giving rise to (see [18] (p. 9, Equation (4.7)) or [19])
and
2.2. The Generalized Laguerre Polynomials
The generalized Laguerre polynomials can be introduced by the series representation [2] (p. 241):
and the corresponding orthogonality relation with respect to the gamma weight function is explicitly given as [12]
where , . These polynomials have the generating functions (see, e.g., [12] (p. 202, Equation (4)), [13] (p. 84, Equation (16)) or [20]):
and
Furthermore, for , we have the following derivative with respect to the unique parameter (see [19] or [21] (p. 80)):
2.3. The Hermite Polynomials
The Hermite polynomials defined by [12] (p. 187, Equation (2)):
have the orthogonality relation in the form [13] (p. 73, Equation (13)):
The set of the polynomials is generated by [2] (Equation (9.15.10))
2.4. First Class of Finite Classical Orthogonal Polynomials
The polynomials , which are defined by the Rodrigues formula:
are polynomial solutions of the equation:
The finite set is orthogonal with respect to the weight function on if and only if , . That is,
for , , . The polynomials satisfy the following recurrence relations:
as well as
for .
Furthermore, the set of the polynomials has a generating function of the form:
Formally, the polynomials are related to the Jacobi polynomials defined in Equation (2) by
Furthermore [22],
2.5. Second Class of Finite Classical Orthogonal Polynomials
The polynomials are also defined through a Rodrigues formula:
and they satisfy the second-order differential equation:
The finite set is orthogonal with respect to the weight function on if and only if , and
is satisfied for , . The polynomials satisfy the following recurrence relations:
and
for . Moreover, the set of the polynomials is generated by:
The relationship:
between the polynomials and the Laguerre polynomials holds true.
2.6. Third Class of Finite Classical Orthogonal Polynomials
The polynomials are defined as follows:
These polynomials are solutions to the equation:
and are orthogonal with respect to the weight function in the interval if and only if . The orthogonality relation corresponding to these polynomials is given by
for , .
For , the following recurrence relations hold:
as well as
Furthermore, the set of polynomials has a generating function of the form:
and the relation:
holds true between the polynomials and the ultraspherical polynomials defined in terms of Jacobi polynomials by [2] (p. 222):
Furthermore, we have the relation [13] (p. 125, Equation (6)):
between the ultraspherical polynomials and the Jacobi polynomials. In [22], the following limit relation:
is given.
Now, we recall Koornwinder’s method to derive orthogonal polynomials in two variables from two orthogonal polynomials in one variable [9,23].
Theorem 1
([9]). Assume that and are positive weight functions on the interval and respectively. Let be a positive function on and satisfy one of the following assumptions:
Case 1: is a polynomial of degree .
Case 2: is a polynomial of degree ; is a symmetric interval; is an even function.
For each integer , let , denote a sequence of orthogonal polynomials in one variable with respect to the weight function . Let be a sequence of orthogonal polynomials with respect to . Then, polynomials in two variables can be defined by
These polynomials are orthogonal with respect to the weight function:
over the domain .
In the present paper, we define 23 sets of finite orthogonal polynomials in two variables in terms of the finite univariate orthogonal polynomials , , and very classical orthogonal polynomials , and by using Koornwinder’s method. We present a number of properties for each family such as the orthogonality relation, the recurrence relations, the partial differential equation, the generating function, as well as the parameter derivatives of these polynomials.
3. The Finite Sets of the Bivariate Orthogonal Polynomials Obtained by the Product of a Finite and an Infinite Univariate Orthogonal Polynomials
By means of the polynomials given by (2), (11), (14), (16), (18), and (22), we define the following 23 sets of bivariate finite orthogonal polynomials in the next subsections.
3.1. The Set of Polynomials
Definition 1.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for and . Indeed, the following relation holds:
for , , .
Theorem 2.
The polynomials satisfy the recurrence relations:
and the three-term recurrence relation of the form:
where and the coefficients are
Proof.
Theorem 3.
The polynomials (25) satisfy the partial differential equation:
Proof.
Let us consider , , and in (19) and multiply the equation by . Let us use the first partial derivative with respect to the y variable:
and the second partial derivative with respect to the y variable:
Theorem 4.
The set of polynomials is generated by
where and with .
Theorem 5.
Proof.
From (10), we have
□
3.2. The Set of Polynomials
Definition 2.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for . In fact, the corresponding orthogonality relation is
for , .
Theorem 7.
The polynomials satisfy the partial differential equations:
and
Theorem 8.
3.3. The Set of Polynomials
Definition 3.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . Indeed, the orthogonality relation corresponding to these polynomials is:
for , , and .
Theorem 9.
Theorem 10.
The polynomials satisfy the partial differential equation:
Theorem 11.
Lemma 2.
Theorem 12.
The parameter derivative of the polynomials is given by:
for , and .
3.4. The Set of Polynomials
Definition 4.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for . The orthogonality relation corresponding to these polynomials is given by:
for , , .
Theorem 13.
The polynomials satisfy the recurrence relation (28), where , , , , as well as .
Theorem 14.
The polynomials satisfy the second-order partial differential equation:
Theorem 15.
For the polynomials , we have the generating function:
where we denoted , with , , as well as .
Lemma 3.
If we substitute and take the limit as in Definition (32), then
3.5. The Set of Polynomials
Definition 5.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for . The orthogonality relation reads as
for .
Theorem 16.
Theorem 17.
The polynomials satisfy the second-order partial differential equations:
and
Theorem 18.
For the polynomials , the generating function:
holds where , , and , .
3.6. The Set of Polynomials
Definition 6.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . The corresponding orthogonality relation takes the form:
for , , , .
Theorem 19.
Theorem 20.
The polynomials satisfy the partial differential equations:
and
Theorem 21.
The polynomials are generated by
Lemma 4.
If we substitute and and take the limit as in Definition (34), we obtain
where we used the notation introduced in [22].
Remark 1.
The bivariate orthogonal polynomials are the product of two infinite families of univariate orthogonal polynomials.
3.7. The Set of Polynomials
Definition 7.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . It follows from the orthogonality relation:
for , , .
Theorem 22.
The polynomials satisfy the following recurrence relations:
for , , the differential relation:
as well as the relation (28) by substituting , and the coefficients are explicitly given by , , , and .
Theorem 23.
The polynomials satisfy the partial differential equations:
and
Theorem 24.
The set is generated by
Lemma 5.
If we substitute and take the limit as in Definition (35), we obtain
which gives the product of the Laguerre and Hermite polynomials [23,24,25].
3.8. The Set of Polynomials
Definition 8.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . The orthogonality relation corresponding to these polynomials is given by
for , , .
Theorem 25.
Theorem 26.
The polynomials satisfy the partial differential equation:
Theorem 27.
Theorem 28.
Lemma 6.
If we substitute and and take the limit as in Definition (36), we obtain
where defined in [23,24,25] is the product of two infinite sequences of univariate orthogonal polynomials.
From a different viewpoint, we give the following limit case.
Lemma 7.
If we substitute and and take the limit as in Definition (36), we obtain
3.9. The Set of Polynomials
Definition 9.
Let us introduce
The set is orthogonal with respect to the weight function:
over the domain for . That is, the corresponding orthogonality relation:
is satisfied for , .
Theorem 30.
The polynomials satisfy the partial differential equations:
and
Theorem 31.
The set of the polynomials is generated by
where and .
3.10. The Set of Polynomials
Definition 10.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . That is,
for , , .
Theorem 32.
Theorem 33.
The polynomials satisfy the partial differential equation:
Theorem 34.
The polynomials have the generating function:
where and , .
Lemma 8.
If we substitute and and take the limit as in Definition (39), we obtain
From a different viewpoint, we give the following limit case.
Lemma 9.
If we substitute and and take the limit as in Definition (38), we obtain
Theorem 35.
The parameter derivative of the polynomials is given by
for , and .
3.11. The Set of Polynomials
Definition 11.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for . In fact,
for , , .
Theorem 36.
Theorem 37.
The polynomials are solutions to the partial differential equation:
Theorem 38.
The polynomials have the generating function:
where the function .
Theorem 39.
Lemma 10.
If we substitute and and take the limit as in Definition (39), we obtain
where is defined in [22], and moreover,
3.12. The Set of Polynomials
Definition 12.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for . The orthogonality relation corresponding to these polynomials is
for .
Theorem 40.
The polynomials defined in (40) can also be computed by the Rodrigues representation:
Theorem 41.
Theorem 42.
Theorem 43.
The polynomials have the generating function:
3.13. The Set of Polynomials
Definition 13.
Let us define
for .
The set is orthogonal with respect to the weight function:
over the domain for , . That is,
holds true for , , .
Theorem 44.
Theorem 45.
The polynomials (41) satisfy the partial differential equation:
Theorem 46.
The polynomials have the generating function:
where , , and .
Theorem 47.
The parameter derivative of the polynomials is given by
for , , and .
Lemma 11.
If we substitute and and take the limit as in Definition (41), we obtain
3.14. The Set of Polynomials
Definition 14.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , and
for , .
Theorem 48.
Theorem 49.
The polynomials are solutions of the partial differential equations:
and
Theorem 50.
3.15. The Set of Polynomials
Definition 15.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , , and
for , .
Theorem 51.
The polynomials satisfy the relation (28) for , with coefficients , , , as well as .
Theorem 52.
The set of the polynomials satisfies the partial differential equations:
and
Theorem 53.
The polynomials are generated by
where and , .
Lemma 12.
If we substitute and and take the limit as in Definition (43), we obtain
where are actually the Laguerre–Laguerre–Koornwinder polynomials [26].
3.16. The Set of Polynomials
Definition 16.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . The corresponding orthogonality relation is
for , , .
Theorem 54.
Theorem 55.
The polynomials satisfy the partial differential equations:
and
Theorem 56.
The set of polynomials has the generating function:
where .
Lemma 13.
If we substitute and take the limit as in Definition (44), we obtain
where the polynomials are defined in [23,24,25].
3.17. The Set of Polynomials
Definition 17.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . The corresponding orthogonality relation takes the form:
for , , .
Theorem 57.
Theorem 58.
The polynomials satisfy the partial differential equations:
and
Theorem 59.
The polynomials are generated by
where and .
Lemma 14.
3.18. The Set of Polynomials
Definition 18.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . The corresponding orthogonality relation is
for , , .
Theorem 60.
Theorem 61.
The polynomials satisfy the partial differential equation:
Theorem 62.
The set of the polynomials has the generating function:
where we denoted , , as well as .
Lemma 15.
If we substitute and and take the limit as in Definition (46), we obtain
where is defined in [23,24,25].
3.19. The Set of Polynomials
Definition 19.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . That is,
for , , .
Theorem 64.
The polynomials satisfy the partial differential equation:
Theorem 65.
The polynomials have the generating function
where and , .
Lemma 16.
3.20. The Set of Polynomials
Definition 20.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . The corresponding orthogonality relation takes the form:
for , , .
Theorem 67.
The polynomials satisfy the partial differential equation:
Theorem 68.
The set of the polynomials has the generating function:
where , , and .
Lemma 17.
If we substitute and and take the limit as in Definition (48), we obtain
where are defined in [23,24,25] by replacing .
3.21. The Set of Polynomials
Definition 21.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for , . Indeed,
for , , .
Theorem 70.
The polynomials satisfy the partial differential equation:
Theorem 71.
The polynomials are generated by
where , , and .
Lemma 18.
3.22. The Set of Polynomials
Definition 22.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for . The orthogonality relation is given by
for , .
Theorem 72.
Theorem 73.
Theorem 74.
For the polynomials , we have the generating function:
where , .
3.23. The Set of Polynomials
Definition 23.
Let us define
The set is orthogonal with respect to the weight function:
over the domain for . The orthogonality relation corresponding to these polynomials is:
for , .
Theorem 75.
Theorem 76.
Theorem 77.
For the polynomials , we have the following generating function:
Lemma 19.
If we substitute and take the limit as in Definition (51), we obtain
where are the Hermite-Hermite polynomials defined in [23,24,25].
4. Conclusions
Classical univariate orthogonal polynomials with respect to a positive weight function have been deeply analyzed since the works of Laplace in 1810. They include the Jacobi, Laguerre, and Hermite polynomials. As for the latter family, they were studied by Chebyshev in 1959 and by Hermite in 1964. Hermite introduced in 1865 for the first time a sequence of multidimensional orthogonal polynomials. Later, a number of properties and characterizations have been considered, which enlarged the univariate families to the Bessel polynomials if we consider definite weights. Very recently, new families emerged under the name of exceptional families, which provide a vast extension of the first mentioned families in many areas of mathematics, in particular the “time-and-band limiting” commutative property found and exploited by D. Slepian, H. Landau, and H. Pollak at Bell Labs in the 1960s [27]. On the other hand, finite families of orthogonal polynomials in the univariate case have been considered since the works of Romanovski connected with the analysis of probability distribution functions in statistics [28].
As for the finite bivariate case, up to now, there were only 15 classes defined by Güldoğan et al., and these were obtained from the product of two finite univariate polynomials. In this paper, 23 finite bivariate orthogonal polynomials were obtained from the product of a finite and an infinite univariate orthogonal polynomials. Therefore, the study fills a gap in the literature, providing a way to generalize to other dimensions. Once we have these new finite families, Fourier transforms can be calculated or q-analogues can be studied, which shall be considered in future works.
Author Contributions
Both authors equally contributed to this work. All authors have read and agreed to the published version of the manuscript.
Funding
The work of I.A. was partially supported by MCIN/AEI/10.13039/501100011033 Grant PID2020-113275GB-I00 and by the European Union. The work of E.G.L. was partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) (Grant Number 2218-122C240).
Data Availability Statement
Not applicable.
Acknowledgments
The authors thank the three reviewers for helpful suggestions and comments, which improved a preliminary version of this work.
Conflicts of Interest
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
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