Fox–Wright function

{{Short description|Generalisation of the generalised hypergeometric function pFq(z)}}

In mathematics, the Fox–Wright function (also known as Fox–Wright Psi function, not to be confused with Wright Omega function) is a generalisation of the generalised hypergeometric function pFq(z) based on ideas of {{harvs|txt|authorlink=Charles Fox (mathematician)|first=Charles|last=Fox|year=1928}} and {{harvs|txt|authorlink=E.M. Wright|first=E. Maitland|last=Wright|year=1935}}:

{}_p\Psi_q \left[\begin{matrix}

( a_1 , A_1 ) & ( a_2 , A_2 ) & \ldots & ( a_p , A_p ) \\

( b_1 , B_1 ) & ( b_2 , B_2 ) & \ldots & ( b_q , B_q ) \end{matrix}

; z \right]

=

\sum_{n=0}^\infty \frac{\Gamma( a_1 + A_1 n )\cdots\Gamma( a_p + A_p n )}{\Gamma( b_1 + B_1 n )\cdots\Gamma( b_q + B_q n )} \, \frac {z^n} {n!}.

Upon changing the normalisation

{}_p\Psi^*_q \left[\begin{matrix}

( a_1 , A_1 ) & ( a_2 , A_2 ) & \ldots & ( a_p , A_p ) \\

( b_1 , B_1 ) & ( b_2 , B_2 ) & \ldots & ( b_q , B_q ) \end{matrix}

; z \right]

=

\frac{ \Gamma(b_1) \cdots \Gamma(b_q) }{ \Gamma(a_1) \cdots \Gamma(a_p) }

\sum_{n=0}^\infty \frac{\Gamma( a_1 + A_1 n )\cdots\Gamma( a_p + A_p n )}{\Gamma( b_1 + B_1 n )\cdots\Gamma( b_q + B_q n )} \, \frac {z^n} {n!}

it becomes pFq(z) for A1...p = B1...q = 1.

The Fox–Wright function is a special case of the Fox H-function {{harv|Srivastava|Manocha|1984|p=50}}:

{}_p\Psi_q \left[\begin{matrix}

( a_1 , A_1 ) & ( a_2 , A_2 ) & \ldots & ( a_p , A_p ) \\

( b_1 , B_1 ) & ( b_2 , B_2 ) & \ldots & ( b_q , B_q ) \end{matrix}

; z \right]

=

H^{1,p}_{p,q+1} \left[ -z \left| \begin{matrix}

( 1-a_1 , A_1 ) & ( 1-a_2 , A_2 ) & \ldots & ( 1-a_p , A_p ) \\

(0,1) & (1- b_1 , B_1 ) & ( 1-b_2 , B_2 ) & \ldots & ( 1-b_q , B_q ) \end{matrix} \right. \right].

A special case of Fox–Wright function appears as a part of the normalizing constant of the modified half-normal distribution{{cite journal |last1=Sun |first1=Jingchao |last2=Kong |first2=Maiying |last3=Pal |first3=Subhadip |title=The Modified-Half-Normal distribution: Properties and an efficient sampling scheme |journal=Communications in Statistics – Theory and Methods |date=22 June 2021 |volume=52 |issue=5 |pages=1591–1613 |doi=10.1080/03610926.2021.1934700 |s2cid=237919587 |url=https://www.tandfonline.com/doi/abs/10.1080/03610926.2021.1934700?journalCode=lsta20 |issn=0361-0926}} with the pdf on (0, \infty) is given as f(x)= \frac{2\beta^{\frac{\alpha}{2}} x^{\alpha-1} \exp(-\beta x^2+ \gamma x )}{\Psi{\left(\frac{\alpha}{2}, \frac{ \gamma}{\sqrt{\beta}}\right)}}, where \Psi(\alpha,z)={}_1\Psi_1\left(\begin{matrix}\left(\alpha,\frac{1}{2}\right)\\(1,0)\end{matrix};z \right) denotes the Fox–Wright Psi function.

Wright function

The entire function W_{\lambda,\mu}(z) is often called the Wright function.{{cite web |url=https://mathworld.wolfram.com/WrightFunction.html |last=Weisstein |first=Eric W. |title=Wright Function |website=From MathWorld--A Wolfram Web Resource |access-date=2022-12-03}} It is the special case of {}_0\Psi_1 \left[\ldots \right] of the Fox–Wright function. Its series representation is

W_{\lambda,\mu}(z) = \sum_{n=0}^\infty

\frac{z^n}{n!\,\Gamma(\lambda n+\mu)}, \lambda > -1.

This function is used extensively in fractional calculus and the stable count distribution. Recall that \lim\limits_{\lambda \to 0} W_{\lambda,\mu}(z) = e^{z} / \Gamma(\mu). Hence, a non-zero \lambda with zero \mu is the simplest nontrivial extension of the exponential function in such context.

Three properties were stated in Theorem 1 of Wright (1933){{cite journal |last=Wright |first=E. |date=1933 |title=On the Coefficients of Power Series Having Exponential Singularities |journal=Journal of the London Mathematical Society |series=Second Series |pages=71–79 |doi=10.1112/JLMS/S1-8.1.71 |s2cid=122652898 |language=en}} and 18.1(30–32) of Erdelyi, Bateman Project, Vol 3 (1955){{Cite book |last=Erdelyi |first=A |title=The Bateman Project, Volume 3 |publisher=California Institute of Technology |year=1955}} (p. 212)

\begin{align}

\lambda z W_{\lambda,\mu+\lambda}(z) & = W_{\lambda,\mu -1}(z) + (1-\mu) W_{\lambda,\mu}(z)

& (a) \\[6pt]

{d \over dz} W_{\lambda,\mu }(z) & = W_{\lambda,\mu +\lambda}(z)

& (b) \\[6pt]

\lambda z {d \over dz} W_{\lambda,\mu }(z) & = W_{\lambda,\mu -1}(z) + (1-\mu) W_{\lambda,\mu}(z)

& (c)

\end{align}

Equation (a) is a recurrence formula. (b) and (c) provide two paths to reduce a derivative. And (c) can be derived from (a) and (b).

A special case of (c) is \lambda = -c\alpha, \mu = 0. Replacing z with -x^\alpha, we have

\begin{array}{lcl}

x {d \over dx} W_{-c\alpha,0 }(-x^\alpha) & = &

-\frac{1}{c}

\left[ W_{-c\alpha,-1}(-x^\alpha) + W_{-c\alpha,0}(-x^\alpha) \right]

\end{array}

A special case of (a) is \lambda = -\alpha, \mu = 1. Replacing z with -z, we have

\alpha z W_{-\alpha,1-\alpha}(-z) = W_{-\alpha,0}(-z)

Two notations, M_{\alpha}(z) and F_{\alpha}(z), were used extensively in the literatures:

\begin{align}

M_{\alpha}(z) & = W_{-\alpha,1-\alpha}(-z), \\ [1ex]

\implies

F_{\alpha}(z) & = W_{-\alpha,0}(-z) = \alpha z M_{\alpha}(z).

\end{align}

= M-Wright function =

M_\alpha(z) is known as the M-Wright function, entering as a probability density in a relevant class of self-similar stochastic processes, generally referred to as time-fractional diffusion processes.

Its properties were surveyed in Mainardi et al (2010).{{cite book |last1=Mainardi |first1=Francesco |last2=Mura |first2=Antonio |last3=Pagnini |first3=Gianni |date=2010-04-17 |title=The M-Wright function in time-fractional diffusion processes: a tutorial survey |arxiv=1004.2950}}

Through the stable count distribution, \alpha is connected to Lévy's stability index (0 < \alpha \leq 1).

Its asymptotic expansion of M_{\alpha}(z) for \alpha > 0 is

M_\alpha \left ( \frac{r}{\alpha} \right ) =

A(\alpha) \, r^{(\alpha -1/2)/(1-\alpha)}

\, e^{-B(\alpha) \, r^{1/(1-\alpha)}}, \,\, r\rightarrow \infty,

where

A(\alpha) = \frac{1}{\sqrt{2\pi (1-\alpha)}},

B(\alpha) = \frac{1-\alpha}{\alpha}.

See also

{{Portal|Mathematics}}

References

{{reflist}}

  • {{cite journal | last= Fox | first= C. | title= The asymptotic expansion of integral functions defined by generalized hypergeometric series | journal= Proc. London Math. Soc. | year= 1928 | volume= 27 | issue= 1 | pages= 389–400 | doi=10.1112/plms/s2-27.1.389 }}
  • {{cite journal | last= Wright | first= E. M. | title= The asymptotic expansion of the generalized hypergeometric function | journal= J. London Math. Soc. | year= 1935 | volume= 10 | issue= 4 | pages= 286–293 | doi=10.1112/jlms/s1-10.40.286 }}
  • {{cite journal | last= Wright | first= E. M. | title= The asymptotic expansion of the generalized hypergeometric function | journal= Proc. London Math. Soc. | year= 1940 | volume= 46 | issue= 2 | pages= 389–408 | doi=10.1112/plms/s2-46.1.389}}
  • {{cite journal | last= Wright | first= E. M. | title= Erratum to "The asymptotic expansion of the generalized hypergeometric function" | journal= J. London Math. Soc. | year= 1952 | volume= 27 | pages= 254 | doi= 10.1112/plms/s2-54.3.254-s | doi-access= free }}
  • {{cite book | last1= Srivastava | first1= H.M. | last2=Manocha | first2= H.L. | title= A treatise on generating functions | year= 1984 | publisher= E. Horwood | isbn= 0-470-20010-3 }}
  • {{cite journal | last1= Miller | first1= A. R. | last2= Moskowitz | first2= I.S. | title= Reduction of a Class of Fox–Wright Psi Functions for Certain Rational Parameters | journal= Computers Math. Applic. | year= 1995 | volume= 30 | issue= 11 | pages= 73–82 | doi=10.1016/0898-1221(95)00165-u| doi-access= free }}
  • {{cite journal |last1=Sun |first1=Jingchao |last2=Kong |first2=Maiying |last3=Pal |first3=Subhadip |title=The Modified-Half-Normal distribution: Properties and an efficient sampling scheme |journal=Communications in Statistics – Theory and Methods |date=22 June 2021 |volume=52 |issue=5 |pages=1591–1613 |doi=10.1080/03610926.2021.1934700 |s2cid=237919587 |url=https://www.tandfonline.com/doi/abs/10.1080/03610926.2021.1934700?journalCode=lsta20 |issn=0361-0926}}