# q-derivative

In mathematics, in the area of combinatorics and quantum calculus, the q-derivative, or Jackson derivative, is a q-analog of the ordinary derivative, introduced by Frank Hilton Jackson. It is the inverse of Jackson's q-integration. For other forms of q-derivative, see Chung et al. (1994).

## Definition

The q-derivative of a function f(x) is defined as[1][2][3]

${\displaystyle \left({\frac {d}{dx}}\right)_{q}f(x)={\frac {f(qx)-f(x)}{qx-x}}.}$

It is also often written as ${\displaystyle D_{q}f(x)}$. The q-derivative is also known as the Jackson derivative.

Formally, in terms of Lagrange's shift operator in logarithmic variables, it amounts to the operator

${\displaystyle D_{q}={\frac {1}{x}}~{\frac {q^{d~~~ \over d(\ln x)}-1}{q-1}}~,}$

which goes to the plain derivative ${\displaystyle \to {\frac {d}{dx}}}$ as ${\displaystyle q\to 1}$.

It is manifestly linear,

${\displaystyle \displaystyle D_{q}(f(x)+g(x))=D_{q}f(x)+D_{q}g(x)~.}$

It has a product rule analogous to the ordinary derivative product rule, with two equivalent forms

${\displaystyle \displaystyle D_{q}(f(x)g(x))=g(x)D_{q}f(x)+f(qx)D_{q}g(x)=g(qx)D_{q}f(x)+f(x)D_{q}g(x).}$

Similarly, it satisfies a quotient rule,

${\displaystyle \displaystyle D_{q}(f(x)/g(x))={\frac {g(x)D_{q}f(x)-f(x)D_{q}g(x)}{g(qx)g(x)}},\quad g(x)g(qx)\neq 0.}$

There is also a rule similar to the chain rule for ordinary derivatives. Let ${\displaystyle g(x)=cx^{k}}$. Then

${\displaystyle \displaystyle D_{q}f(g(x))=D_{q^{k}}(f)(g(x))D_{q}(g)(x).}$

The eigenfunction of the q-derivative is the q-exponential eq(x).

## Relationship to ordinary derivatives

Q-differentiation resembles ordinary differentiation, with curious differences. For example, the q-derivative of the monomial is:[2]

${\displaystyle \left({\frac {d}{dz}}\right)_{q}z^{n}={\frac {1-q^{n}}{1-q}}z^{n-1}=[n]_{q}z^{n-1}}$

where ${\displaystyle [n]_{q}}$ is the q-bracket of n. Note that ${\displaystyle \lim _{q\to 1}[n]_{q}=n}$ so the ordinary derivative is regained in this limit.

The n-th q-derivative of a function may be given as:[3]

${\displaystyle (D_{q}^{n}f)(0)={\frac {f^{(n)}(0)}{n!}}{\frac {(q;q)_{n}}{(1-q)^{n}}}={\frac {f^{(n)}(0)}{n!}}[n]!_{q}}$

provided that the ordinary n-th derivative of f exists at x = 0. Here, ${\displaystyle (q;q)_{n}}$ is the q-Pochhammer symbol, and ${\displaystyle [n]!_{q}}$ is the q-factorial. If ${\displaystyle f(x)}$ is analytic we can apply the Taylor formula to the definition of ${\displaystyle D_{q}(f(x))}$ to get

${\displaystyle \displaystyle D_{q}(f(x))=\sum _{k=0}^{\infty }{\frac {(q-1)^{k}}{(k+1)!}}x^{k}f^{(k+1)}(x).}$

A q-analog of the Taylor expansion of a function about zero follows:[2]

${\displaystyle f(z)=\sum _{n=0}^{\infty }f^{(n)}(0)\,{\frac {z^{n}}{n!}}=\sum _{n=0}^{\infty }(D_{q}^{n}f)(0)\,{\frac {z^{n}}{[n]!_{q}}}.}$

## Higher order q-derivatives

Th following representation for higher order ${\displaystyle q}$-derivatives is known:[4][5]

${\displaystyle D_{q}^{n}f(x)={\frac {1}{(1-q)^{n}x^{n}}}\sum _{k=0}^{n}(-1)^{k}{\binom {n}{k}}_{q}q^{{\binom {k}{2}}-(n-1)k}f(q^{k}x).}$

${\displaystyle {\binom {n}{k}}_{q}}$ is the ${\displaystyle q}$-binomial coefficient. By changing the order of summation as ${\displaystyle r=n-k}$, we obtain the next formula:[4][6]

${\displaystyle D_{q}^{n}f(x)={\frac {(-1)^{n}q^{-{\binom {n}{2}}}}{(1-q)^{n}x^{n}}}\sum _{r=0}^{n}(-1)^{r}{\binom {n}{r}}_{q}q^{\binom {r}{2}}f(q^{n-r}x).}$

Higher order ${\displaystyle q}$-derivatives are used to ${\displaystyle q}$-Taylor formula and the ${\displaystyle q}$-Rodrigues' formula (the formula used to construct ${\displaystyle q}$-orthogonal polynomials[4]).

## Generalizations

### Post Quantum Calculus

Post quantum calculus is a generalization of the theory of quantum calculus, and it uses the following operator:[7][8]

${\displaystyle D_{p,q}f(x):={\frac {f(px)-f(qx)}{(p-q)x}},\quad x\neq 0.}$

### Hahn difference

Wolfgang Hahn introduced the following operator (Hahn difference):[9][10]

${\displaystyle D_{q,\omega }f(x):={\frac {f(qx+\omega )-f(x)}{(q-1)x+\omega }},\quad 00.}$

When ${\displaystyle \omega \to 0}$ this operator reduces to ${\displaystyle q}$-derivative, and when ${\displaystyle q\to 1}$ it reduces to forward difference. This is a successful tool for constructing families of orthogonal polynomials and investigating some approximation problems.[11][12][13]

### β-derivative

${\displaystyle \beta }$-derivative is an operator defined as follows:[14][15]

${\displaystyle D_{\beta }f(t):={\frac {f(\beta (t))-f(t)}{\beta (t)-t}},\quad \beta \neq t,\quad \beta :I\to I.}$

In the definition, ${\displaystyle I}$ is a given interval, and ${\displaystyle \beta (t)}$ is any continuous function that strictly monotonically increases (i.e. ${\displaystyle t>s\rightarrow \beta (t)>\beta (s)}$). When ${\displaystyle \beta (t)=qt}$ then this operator is ${\displaystyle q}$-derivative, and when ${\displaystyle \beta (t)=qt+\omega }$ this operator is Hahn difference.

## Applications

The q-calculus has been used in machine learning for designing stochastic activation functions.[16]

## Citations

1. ^ Jackson 1908, pp. 253–281.
2. ^ a b c
3. ^ a b
4. ^ a b c
5. ^ Koepf, Rajković & Marinković 2007, pp. 621–638.
6. ^ Annaby & Mansour 2008, pp. 472–483.
7. ^ Gupta V., Rassias T.M., Agrawal P.N., Acu A.M. (2018) Basics of Post-Quantum Calculus. In: Recent Advances in Constructive Approximation Theory. SpringerOptimization and Its Applications, vol 138. Springer.
8. ^
9. ^ Hahn, W. (1949). Math. Nachr. 2: 4-34.
10. ^ Hahn, W. (1983) Monatshefte Math. 95: 19-24.
11. ^
12. ^ Kwon, K.; Lee, D.; Park, S.; Yoo, B.: Kyungpook Math. J. 38, 259-281 (1998).
13. ^ Alvarez-Nodarse, R.: J. Comput. Appl. Math. 196, 320-337 (2006).
14. ^ Auch, T. (2013): Development and Application of Difference and Fractional Calculus on Discrete Time Scales. PhD thesis, University of Nebraska-Lincoln.
15. ^ Hamza et al. 2015, p. 182.
16. ^ Nielsen & Sun 2021, pp. 2782–2789.

## Bibliography

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• Chung, K. S.; Chung, W. S.; Nam, S. T.; Kang, H. J. (1994). "New q-derivative and q-logarithm". International Journal of Theoretical Physics. 33 (10): 2019–2029. doi:10.1007/BF00675167. S2CID 117685233.
• Duran, U. (2016). Post Quantum Calculus (M.Sc. thesis). Department of Mathematics, University of Gaziantep Graduate School of Natural & Applied Sciences. Retrieved 9 March 2022 – via ResearchGate.
• Ernst, T. (2012). A comprehensive treatment of q-calculus. Springer Science & Business Media. ISBN 978-303480430-1.
• Ernst, Thomas (2001). "The History of q-Calculus and a new method" (PDF). Archived from the original (PDF) on 28 November 2009. Retrieved 9 March 2022.
• Exton, H. (1983). q-Hypergeometric Functions and Applications. New York: Halstead Press. ISBN 978-047027453-8.
• Foupouagnigni, M. (1998). Laguerre-Hahn orthogonal polynomials with respect to the Hahn operator: fourth-order difference equation for the rth associated and the Laguerre-Freud equations for the recurrence coefficients (Ph.D. thesis). Université Nationale du Bénin.
• Hamza, A.; Sarhan, A.; Shehata, E.; Aldwoah, K. (2015). "A General Quantum Difference Calculus". Advances in Difference Equations. 1: 182. doi:10.1186/s13662-015-0518-3. S2CID 54790288.
• Jackson, F. H. (1908). "On q-functions and a certain difference operator". Trans. R. Soc. Edinb. 46 (2): 253–281. doi:10.1017/S0080456800002751.
• Kac, Victor; Pokman Cheung (2002). Quantum Calculus. Springer-Verlag. ISBN 0-387-95341-8.
• Koekoek, J.; Koekoek, R. (1999). "A note on the q-derivative operator". J. Math. Anal. Appl. 176 (2): 627–634. arXiv:math/9908140. doi:10.1006/jmaa.1993.1237. S2CID 329394.
• Koepf, W.; Rajković, P. M.; Marinković, S. D. (July 2007). "Properties of q-holonomic functions". Journal of Difference Equations and Applications. 13 (7): 621–638. doi:10.1080/10236190701264925. S2CID 123079843.
• Koepf, Wolfram (2014). Hypergeometric Summation. An Algorithmic Approach to Summation and Special Function Identities. Springer. ISBN 978-1-4471-6464-7.
• Nielsen, Frank; Sun, Ke (2021). "q-Neurons: Neuron Activations Based on Stochastic Jackson's Derivative Operators". IEEE Trans. Neural Networks Learn. Syst. 32 (6): 2782–2789. arXiv:1806.00149. doi:10.1109/TNNLS.2020.3005167. PMID 32886614. S2CID 44143912.