Orthogonal polynomials

In

.

The most widely used orthogonal polynomials are the classical orthogonal polynomials, consisting of the Hermite polynomials, the Laguerre polynomials and the Jacobi polynomials. The Gegenbauer polynomials form the most important class of Jacobi polynomials; they include the Chebyshev polynomials, and the Legendre polynomials as special cases. These are frequently given by the Rodrigues' formula.

The field of orthogonal polynomials developed in the late 19th century from a study of

.

Definition for 1-variable case for a real measure

Given any non-decreasing function α on the real numbers, we can define the

Lebesgue–Stieltjes integral

$${\displaystyle \int f(x)\,d\alpha (x)}$$ of a function f. If this integral is finite for all polynomials f, we can define an inner product on pairs of polynomials f and g by $${\displaystyle \langle f,g\rangle =\int f(x)g(x)\,d\alpha (x).}$$

This operation is a positive semidefinite inner product on the vector space of all polynomials, and is positive definite if the function α has an infinite number of points of growth. It induces a notion of orthogonality in the usual way, namely that two polynomials are orthogonal if their inner product is zero.

Then the sequence (P_n)^∞
_n=0 of orthogonal polynomials is defined by the relations $${\displaystyle \deg P_{n}=n~,\quad \langle P_{m},\,P_{n}\rangle =0\quad {\text{for}}\quad m\neq n~.}$$

In other words, the sequence is obtained from the sequence of monomials 1, x, x², … by the Gram–Schmidt process with respect to this inner product.

Usually the sequence is required to be

, namely, $${\displaystyle \langle P_{n},P_{n}\rangle =1,}$$ however, other normalisations are sometimes used.

Sometimes we have $${\displaystyle d\alpha (x)=W(x)\,dx}$$ where $${\displaystyle W:[x_{1},x_{2}]\to \mathbb {R} }$$ is a non-negative function with support on some interval [x₁, x₂] in the real line (where x₁ = −∞ and x₂ = ∞ are allowed). Such a W is called a weight function.^[1] Then the inner product is given by $${\displaystyle \langle f,g\rangle =\int _{x_{1}}^{x_{2}}f(x)g(x)W(x)\,dx.}$$ However, there are many examples of orthogonal polynomials where the measure dα(x) has points with non-zero measure where the function α is discontinuous, so cannot be given by a weight function W as above.

The most commonly used orthogonal polynomials are orthogonal for a measure with support in a real interval. This includes:

• The classical orthogonal polynomials (Jacobi polynomials, Laguerre polynomials, Hermite polynomials, and their special cases Gegenbauer polynomials, Chebyshev polynomials and Legendre polynomials).
• The Wilson polynomials, which generalize the Jacobi polynomials. They include many orthogonal polynomials as special cases, such as the Meixner–Pollaczek polynomials, the continuous Hahn polynomials, the continuous dual Hahn polynomials, and the classical polynomials, described by the Askey scheme
• The Askey–Wilson polynomials introduce an extra parameter q into the Wilson polynomials.

.

Meixner classified all the orthogonal Sheffer sequences: there are only Hermite, Laguerre, Charlier, Meixner, and Meixner–Pollaczek. In some sense Krawtchouk should be on this list too, but they are a finite sequence. These six families correspond to the NEF-QVFs and are martingale polynomials for certain Lévy processes.

Sieved orthogonal polynomials, such as the sieved ultraspherical polynomials, sieved Jacobi polynomials, and sieved Pollaczek polynomials, have modified recurrence relations.

One can also consider orthogonal polynomials for some curve in the complex plane. The most important case (other than real intervals) is when the curve is the unit circle, giving orthogonal polynomials on the unit circle, such as the Rogers–Szegő polynomials.

There are some families of orthogonal polynomials that are orthogonal on plane regions such as triangles or disks. They can sometimes be written in terms of Jacobi polynomials. For example, Zernike polynomials are orthogonal on the unit disk.

The advantage of orthogonality between different orders of Hermite polynomials is applied to Generalized frequency division multiplexing (GFDM) structure. More than one symbol can be carried in each grid of time-frequency lattice.^[2]

Orthogonal polynomials of one variable defined by a non-negative measure on the real line have the following properties.

The orthogonal polynomials P_n can be expressed in terms of the moments

${\displaystyle m_{n}=\int x^{n}\,d\alpha (x)}$

as follows:

${\displaystyle P_{n}(x)=c_{n}\,\det {\begin{bmatrix}m_{0}&m_{1}&m_{2}&\cdots &m_{n}\\m_{1}&m_{2}&m_{3}&\cdots &m_{n+1}\\\vdots &\vdots &\vdots &\ddots &\vdots \\m_{n-1}&m_{n}&m_{n+1}&\cdots &m_{2n-1}\\1&x&x^{2}&\cdots &x^{n}\end{bmatrix}}~,}$

where the constants c_n are arbitrary (depend on the normalization of P_n).

This comes directly from applying the Gram–Schmidt process to the monomials, imposing each polynomial to be orthogonal with respect to the previous ones. For example, orthogonality with ${\displaystyle P_{0}}$ prescribes that ${\displaystyle P_{1}}$ must have the form$${\displaystyle P_{1}(x)=c_{1}\left(x-{\frac {\langle P_{0},x\rangle P_{0}}{\langle P_{0},P_{0}\rangle }}\right)=c_{1}(x-m_{1}),}$$which can be seen to be consistent with the previously given expression with the determinant.

The polynomials P_n satisfy a recurrence relation of the form

${\displaystyle P_{n}(x)=(A_{n}x+B_{n})P_{n-1}(x)+C_{n}P_{n-2}(x)}$

where A_n is not 0. The converse is also true; see Favard's theorem.

If the measure dα is supported on an interval [a, b], all the zeros of P_n lie in [a, b]. Moreover, the zeros have the following interlacing property: if m < n, there is a zero of P_n between any two zeros of P_m.

]

Combinatorial interpretation

From the 1980s, with the work of X. G. Viennot, J. Labelle, Y.-N. Yeh, D. Foata, and others, combinatorial interpretations were found for all the classical orthogonal polynomials. [3]

The

are the special case of Macdonald polynomials for a certain non-reduced root system of rank 1.

Multiple orthogonal polynomials

Multiple orthogonal polynomials are polynomials in one variable that are orthogonal with respect to a finite family of measures.

These are orthogonal polynomials with respect to a Sobolev inner product, i.e. an inner product with derivatives. Including derivatives has big consequences for the polynomials, in general they no longer share some of the nice features of the classical orthogonal polynomials.

Orthogonal polynomials with matrices have either coefficients that are matrices or the indeterminate is a matrix.

There are two popular examples: either the coefficients ${\displaystyle \{a_{i}\}}$ are matrices or ${\displaystyle x}$:

• Variante 1: ${\displaystyle P(x)=A_{n}x^{n}+A_{n-1}x^{n-1}+\cdots +A_{1}x+A_{0}}$, where ${\displaystyle \{A_{i}\}}$ are ${\displaystyle p\times p}$ matrices.
• Variante 2: ${\displaystyle P(X)=a_{n}X^{n}+a_{n-1}X^{n-1}+\cdots +a_{1}X+a_{0}I_{p}}$ where ${\displaystyle X}$ is a ${\displaystyle p\times p}$-matrix and ${\displaystyle I_{p}}$ is the identity matrix.

Quantum polynomials or q-polynomials are the q-analogs of orthogonal polynomials.

• Appell sequence
• Askey scheme of hypergeometric orthogonal polynomials
• Favard's theorem
• Polynomial sequences of binomial type
• Biorthogonal polynomials
• Generalized Fourier series
• Pseudo Jacobi polynomials
  • Romanovski polynomials
• Secondary measure
• Sheffer sequence
• Sturm–Liouville theory
• Umbral calculus
• Plancherel–Rotach asymptotics

• LCCN 65-12253
  .
• Chihara, Theodore Seio (1978). An Introduction to Orthogonal Polynomials. Gordon and Breach, New York.
  ISBN 0-677-04150-0
  .
• Chihara, Theodore Seio (2001). "45 years of orthogonal polynomials: a view from the wings". Proceedings of the Fifth International Symposium on Orthogonal Polynomials, Special Functions and their Applications (Patras, 1999). Journal of Computational and Applied Mathematics. 133 (1): 13–21.
  MR 1858267
  .
• Foncannon, J. J.; Foncannon, J. J.; Pekonen, Osmo (2008). "Review of Classical and quantum orthogonal polynomials in one variable by Mourad Ismail".
  S2CID 118133026
  .
• Ismail, Mourad E. H. (2005). Classical and Quantum Orthogonal Polynomials in One Variable. Cambridge: Cambridge Univ. Press.
  ISBN 0-521-78201-5
  .
• Jackson, Dunham (2004) [1941]. Fourier Series and Orthogonal Polynomials. New York: Dover.
  ISBN 0-486-43808-2
  .
• Koornwinder, Tom H.; Wong, Roderick S. C.; Koekoek, Roelof; Swarttouw, René F. (2010), "Orthogonal Polynomials", in
  MR 2723248
  .
• "Orthogonal polynomials", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
• Szegő, Gábor (1939). Orthogonal Polynomials. Colloquium Publications. Vol. XXIII. American Mathematical Society.
  MR 0372517. {{cite book}}: ISBN / Date incompatibility (help
  )
• arXiv:math.CA/0512424
  .
• C. Chan, A. Mironov, A. Morozov, A. Sleptsov,
  arXiv:1712.03155
  .
• Herbert Stahl and Vilmos Totik: General Orthogonal Polynomials, Cambridge Univ. Press, ISBN 978-0-521-41534-7 (1992).
• G. Sansone: Orthogonal Functions, (Revised English Edition), Dover, ISBN 978-0-486-77730-0 (1991).