在数学中,梅林变换是一种以幂函数为核的积分变换,与双边拉普拉斯变换有密切关联。梅林变换定义式如下: { M f } ( s ) = φ ( s ) = ∫ 0 ∞ x s − 1 f ( x ) d x . {\displaystyle \left\{{\mathcal {M}}f\right\}(s)=\varphi (s)=\int _{0}^{\infty }x^{s-1}f(x)dx.} 此条目可参照英语维基百科相应条目来扩充。 (2020年4月28日) 而其逆变换为 { M − 1 φ } ( x ) = f ( x ) = 1 2 π i ∫ c − i ∞ c + i ∞ x − s φ ( s ) d s . {\displaystyle \left\{{\mathcal {M}}^{-1}\varphi \right\}(x)=f(x)={\frac {1}{2\pi i}}\int _{c-i\infty }^{c+i\infty }x^{-s}\varphi (s)\,ds.} 梅林变换有许多应用。出于它与狄利克雷级数的联系,它也被用以证明黎曼ζ函数与素数计数函数有关的的函数方程;进一步地,它也与解析数论有关,如在佩龙公式中。 同时,它与伽马函数密切相关,很多常见函数的梅林变换中都需要用到伽马函数或它衍生出的贝塔函数;这使得它被运用在梅林-巴恩斯积分和超几何函数的理论中,衍生出了在计算机代数系统中使用的,可以快速计算大量定积分的Meijer_G-函数。 与其他变换之关系 之所以伽马函数与积分变换的理论联系密切,是因为伽马函数同时是指数函数的拉普拉斯变换和幂函数的梅林变换,这也展示了两种积分变换之间的联系。 双边拉普拉斯变换 双边拉普拉斯变换可以用梅林变换来表示,如下式 { B f } ( s ) = { M f ( − ln x ) } ( s ) {\displaystyle \left\{{\mathcal {B}}f\right\}(s)=\left\{{\mathcal {M}}f(-\ln x)\right\}(s)} 梅林变换也可以用双边拉普拉斯变换来表示,如下式 { M f } ( s ) = { B f ( e − x ) } ( s ) {\displaystyle \left\{{\mathcal {M}}f\right\}(s)=\left\{{\mathcal {B}}f(e^{-x})\right\}(s)} 傅立叶变换 傅立叶变换可以用梅林变换来表示,如下式 { F f } ( − s ) = { B f } ( − i s ) = { M f ( − ln x ) } ( − i s ) {\displaystyle \left\{{\mathcal {F}}f\right\}(-s)=\left\{{\mathcal {B}}f\right\}(-is)=\left\{{\mathcal {M}}f(-\ln x)\right\}(-is)\ } 梅林变换变换也可以用傅立叶来表示,如下式 { M f } ( s ) = { B f ( e − x ) } ( s ) = { F f ( e − x ) } ( − i s ) {\displaystyle \left\{{\mathcal {M}}f\right\}(s)=\left\{{\mathcal {B}}f(e^{-x})\right\}(s)=\left\{{\mathcal {F}}f(e^{-x})\right\}(-is)\ } 范例 Cahen–Mellin 积分 对于 c > 0 {\displaystyle c>0} , ℜ ( y ) > 0 {\displaystyle \Re (y)>0} ,且 y − s {\displaystyle y^{-s}} 在主要分支(principal branch)上,我们有 e − y = 1 2 π i ∫ c − i ∞ c + i ∞ Γ ( s ) y − s d s {\displaystyle e^{-y}={\frac {1}{2\pi i}}\int _{c-i\infty }^{c+i\infty }\Gamma (s)y^{-s}\;ds} 其中 Γ ( s ) {\displaystyle \Gamma (s)} 为 Γ函数。 数论 假设 ℜ ( s + a ) < 0 {\displaystyle \Re (s+a)<0} 我们有 f ( x ) = { 0 x < 1 x a x > 1 {\displaystyle f(x)={\begin{cases}0&x<1\\x^{a}&x>1\end{cases}}} 其中 M f ( s ) = − 1 s + a {\displaystyle {\mathcal {M}}f(s)=-{\frac {1}{s+a}}} 圆柱坐标系下的拉普拉斯算子 在任何维度的圆柱坐标系中,拉普拉斯算子总是会包含下式 1 r ∂ ∂ r ( r ∂ f ∂ r ) {\displaystyle {\frac {1}{r}}{\frac {\partial }{\partial r}}\left(r{\frac {\partial f}{\partial r}}\right)} 例如,拉普拉斯算子在二维空间的极坐标表示法 ∇ 2 f = 1 r ∂ ∂ r ( r ∂ f ∂ r ) + 1 r 2 ∂ 2 f ∂ θ 2 {\displaystyle \nabla ^{2}f={\frac {1}{r}}{\frac {\partial }{\partial r}}\left(r{\frac {\partial f}{\partial r}}\right)+{\frac {1}{r^{2}}}{\frac {\partial ^{2}f}{\partial \theta ^{2}}}} 或是在三维空间的柱坐标表示法 ∇ 2 f = 1 r ∂ ∂ r ( r ∂ f ∂ r ) + 1 r 2 ∂ 2 f ∂ φ 2 + ∂ 2 f ∂ z 2 {\displaystyle \nabla ^{2}f={\frac {1}{r}}{\frac {\partial }{\partial r}}\left(r{\frac {\partial f}{\partial r}}\right)+{\frac {1}{r^{2}}}{\frac {\partial ^{2}f}{\partial \varphi ^{2}}}+{\frac {\partial ^{2}f}{\partial z^{2}}}} 而利用梅林变换可以很简单的处理此项 1 r ∂ ∂ r ( r ∂ f ∂ r ) = f r r + f r r {\displaystyle {\frac {1}{r}}{\frac {\partial }{\partial r}}\left(r{\frac {\partial f}{\partial r}}\right)=f_{rr}+{\frac {f_{r}}{r}}} M ( r 2 f r r + r f r , r → s ) = s 2 M ( f , r → s ) = s 2 F {\displaystyle {\mathcal {M}}\left(r^{2}f_{rr}+rf_{r},r\to s\right)=s^{2}{\mathcal {M}}\left(f,r\to s\right)=s^{2}F} 举例来说,二维拉普拉斯方程的极坐标表示法具有以下形式 r 2 f r r + r f r + f θ θ = 0 {\displaystyle r^{2}f_{rr}+rf_{r}+f_{\theta \theta }=0} 或是 1 r ∂ ∂ r ( r ∂ f ∂ r ) + 1 r 2 ∂ 2 f ∂ θ 2 = 0 {\displaystyle {\frac {1}{r}}{\frac {\partial }{\partial r}}\left(r{\frac {\partial f}{\partial r}}\right)+{\frac {1}{r^{2}}}{\frac {\partial ^{2}f}{\partial \theta ^{2}}}=0} 利用梅林变换,可以转换成一个简谐振子的形式 F θ θ + s 2 F = 0 {\displaystyle F_{\theta \theta }+s^{2}F=0} 通解为 F ( s , θ ) = C 1 ( s ) cos ( s θ ) + C 2 ( s ) sin ( s θ ) {\displaystyle F(s,\theta )=C_{1}(s)\cos(s\theta )+C_{2}(s)\sin(s\theta )} 若给定边界条件 f ( r , − θ 0 ) = a ( r ) , f ( r , θ 0 ) = b ( r ) {\displaystyle f(r,-\theta _{0})=a(r),\quad f(r,\theta _{0})=b(r)} 其梅林变换为 F ( s , − θ 0 ) = A ( s ) , F ( s , θ 0 ) = B ( s ) {\displaystyle F(s,-\theta _{0})=A(s),\quad F(s,\theta _{0})=B(s)} 则通解可以写成 F ( s , θ ) = A ( s ) sin ( s ( θ 0 − θ ) ) sin ( 2 θ 0 s ) + B ( s ) sin ( s ( θ 0 + θ ) ) sin ( 2 θ 0 s ) {\displaystyle F(s,\theta )=A(s){\frac {\sin(s(\theta _{0}-\theta ))}{\sin(2\theta _{0}s)}}+B(s){\frac {\sin(s(\theta _{0}+\theta ))}{\sin(2\theta _{0}s)}}} 最后利用逆变换以及卷积定理 M − 1 ( sin ( s φ ) sin ( 2 θ 0 s ) ; s → r ) = 1 2 θ 0 r m sin ( m φ ) 1 + 2 r m cos ( m φ ) + r 2 m {\displaystyle {\mathcal {M}}^{-1}\left({\frac {\sin(s\varphi )}{\sin(2\theta _{0}s)}};s\to r\right)={\frac {1}{2\theta _{0}}}{\frac {r^{m}\sin(m\varphi )}{1+2r^{m}\cos(m\varphi )+r^{2m}}}} 其中 m = π 2 θ 0 {\displaystyle m={\frac {\pi }{2\theta _{0}}}} 可以得到 f ( r , θ ) = r m cos ( m θ ) 2 θ 0 ∫ 0 ∞ { a ( x ) x 2 m + 2 r m x m sin ( m θ ) + r 2 m + b ( x ) x 2 m − 2 r m x m sin ( m θ ) + r 2 m } x m − 1 d x {\displaystyle f(r,\theta )={\frac {r^{m}\cos(m\theta )}{2\theta _{0}}}\int _{0}^{\infty }\left\{{\frac {a(x)}{x^{2m}+2r^{m}x^{m}\sin(m\theta )+r^{2m}}}+{\frac {b(x)}{x^{2m}-2r^{m}x^{m}\sin(m\theta )+r^{2m}}}\right\}x^{m-1}\,dx} 应用 因为具有尺度不变性(英语:Scale_invariance),梅林变换广泛应用于计算机科学的算法分析。[1]对于纯虚输入,原函数函数的梅林变换与对其进行尺度伸缩后函数的梅林变换幅度相同。尺度不变性类似于傅里叶变换的时移不变性,即原函数与对其进行时移的函数的傅里叶变换幅度相同。这一性质对图像识别非常有用:当物体与摄像机的距离发生变化时,图像尺度会发生变化。 在量子力学(特别是量子场论)中,由于动量和位置之间存在傅里叶变化的关系,傅里叶空间被广泛应用。2011年,A. Liam Fitzpatrick、Jared Kaplan、João Penedones(英语:João_Penedones)、Suvrat Raju(英语:Suvrat_Raju)和Balt C. van Rees证明了梅林空间在AdS/CFT对偶中具有类似的效用。[2][3][4] 梅林变换表 下表展示了部分函数的梅林变化结果,在Bracewell (2000) harvtxt error: no target: CITEREFBracewell2000 (help)与Erdélyi (1954) harvtxt error: no target: CITEREFErdélyi1954 (help)中可以找到。 More information 函数 ... 梅林变化表 函数 f ( x ) {\displaystyle f(x)} 梅林变换 f ~ ( s ) = M { f } ( s ) {\displaystyle {\tilde {f}}(s)={\mathcal {M}}\{f\}(s)} 收敛域 注释 e − x {\displaystyle e^{-x}} Γ ( s ) {\displaystyle \Gamma (s)} 0 < ℜ s < ∞ {\displaystyle 0<\Re s<\infty } e − x − 1 {\displaystyle e^{-x}-1} Γ ( s ) {\displaystyle \Gamma (s)} − 1 < ℜ s < 0 {\displaystyle -1<\Re s<0} e − x − 1 + x {\displaystyle e^{-x}-1+x} Γ ( s ) {\displaystyle \Gamma (s)} − 2 < ℜ s < − 1 {\displaystyle -2<\Re s<-1} 一般来说, Γ ( s ) {\displaystyle \Gamma (s)} 是 e − x − ∑ n = 0 N − 1 ( − 1 ) n n ! x n , for − N < ℜ s < − N + 1 {\displaystyle e^{-x}-\sum _{n=0}^{N-1}{\frac {(-1)^{n}}{n!}}x^{n},{\text{ for }}-N<\Re s<-N+1} 的梅林变换。[5] e − x 2 {\displaystyle e^{-x^{2}}} 1 2 Γ ( 1 2 s ) {\displaystyle {\tfrac {1}{2}}\Gamma ({\tfrac {1}{2}}s)} 0 < ℜ s < ∞ {\displaystyle 0<\Re s<\infty } e r f c ( x ) {\displaystyle \mathrm {erfc} (x)} Γ ( 1 2 ( 1 + s ) ) π s {\displaystyle {\frac {\Gamma ({\tfrac {1}{2}}(1+s))}{{\sqrt {\pi }}\;s}}} 0 < ℜ s < ∞ {\displaystyle 0<\Re s<\infty } e − ( ln x ) 2 {\displaystyle e^{-(\ln x)^{2}}} π e 1 4 s 2 {\displaystyle {\sqrt {\pi }}\,e^{{\tfrac {1}{4}}s^{2}}} − ∞ < ℜ s < ∞ {\displaystyle -\infty <\Re s<\infty } δ ( x − a ) {\displaystyle \delta (x-a)} a s − 1 {\displaystyle a^{s-1}} − ∞ < ℜ s < ∞ {\displaystyle -\infty <\Re s<\infty } a > 0 , δ ( x ) {\displaystyle a>0,\;\delta (x)} 是狄拉克函数。 u ( 1 − x ) = { 1 if 0 < x < 1 0 if 1 < x < ∞ {\displaystyle u(1-x)=\left\{{\begin{aligned}&1&&\;{\text{if}}\;0<x<1&\\&0&&\;{\text{if}}\;1<x<\infty &\end{aligned}}\right.} 1 s {\displaystyle {\frac {1}{s}}} 0 < ℜ s < ∞ {\displaystyle 0<\Re s<\infty } u ( x ) {\displaystyle u(x)} 是单位阶跃函数。 − u ( x − 1 ) = { 0 if 0 < x < 1 − 1 if 1 < x < ∞ {\displaystyle -u(x-1)=\left\{{\begin{aligned}&0&&\;{\text{if}}\;0<x<1&\\&-1&&\;{\text{if}}\;1<x<\infty &\end{aligned}}\right.} 1 s {\displaystyle {\frac {1}{s}}} − ∞ < ℜ s < 0 {\displaystyle -\infty <\Re s<0} u ( 1 − x ) x a = { x a if 0 < x < 1 0 if 1 < x < ∞ {\displaystyle u(1-x)\,x^{a}=\left\{{\begin{aligned}&x^{a}&&\;{\text{if}}\;0<x<1&\\&0&&\;{\text{if}}\;1<x<\infty &\end{aligned}}\right.} 1 s + a {\displaystyle {\frac {1}{s+a}}} − ℜ a < ℜ s < ∞ {\displaystyle -\Re a<\Re s<\infty } − u ( x − 1 ) x a = { 0 if 0 < x < 1 − x a if 1 < x < ∞ {\displaystyle -u(x-1)\,x^{a}=\left\{{\begin{aligned}&0&&\;{\text{if}}\;0<x<1&\\&-x^{a}&&\;{\text{if}}\;1<x<\infty &\end{aligned}}\right.} 1 s + a {\displaystyle {\frac {1}{s+a}}} − ∞ < ℜ s < − ℜ a {\displaystyle -\infty <\Re s<-\Re a} u ( 1 − x ) x a ln x = { x a ln x if 0 < x < 1 0 if 1 < x < ∞ {\displaystyle u(1-x)\,x^{a}\ln x=\left\{{\begin{aligned}&x^{a}\ln x&&\;{\text{if}}\;0<x<1&\\&0&&\;{\text{if}}\;1<x<\infty &\end{aligned}}\right.} 1 ( s + a ) 2 {\displaystyle {\frac {1}{(s+a)^{2}}}} − ℜ a < ℜ s < ∞ {\displaystyle -\Re a<\Re s<\infty } − u ( x − 1 ) x a ln x = { 0 if 0 < x < 1 − x a ln x if 1 < x < ∞ {\displaystyle -u(x-1)\,x^{a}\ln x=\left\{{\begin{aligned}&0&&\;{\text{if}}\;0<x<1&\\&-x^{a}\ln x&&\;{\text{if}}\;1<x<\infty &\end{aligned}}\right.} 1 ( s + a ) 2 {\displaystyle {\frac {1}{(s+a)^{2}}}} − ∞ < ℜ s < − ℜ a {\displaystyle -\infty <\Re s<-\Re a} 1 1 + x {\displaystyle {\frac {1}{1+x}}} π sin ( π s ) {\displaystyle {\frac {\pi }{\sin(\pi s)}}} 0 < ℜ s < 1 {\displaystyle 0<\Re s<1} 1 1 − x {\displaystyle {\frac {1}{1-x}}} π tan ( π s ) {\displaystyle {\frac {\pi }{\tan(\pi s)}}} 0 < ℜ s < 1 {\displaystyle 0<\Re s<1} 1 1 + x 2 {\displaystyle {\frac {1}{1+x^{2}}}} π 2 sin ( 1 2 π s ) {\displaystyle {\frac {\pi }{2\sin({\tfrac {1}{2}}\pi s)}}} 0 < ℜ s < 2 {\displaystyle 0<\Re s<2} ln ( 1 + x ) {\displaystyle \ln(1+x)} π s sin ( π s ) {\displaystyle {\frac {\pi }{s\,\sin(\pi s)}}} − 1 < ℜ s < 0 {\displaystyle -1<\Re s<0} sin ( x ) {\displaystyle \sin(x)} sin ( 1 2 π s ) Γ ( s ) {\displaystyle \sin({\tfrac {1}{2}}\pi s)\,\Gamma (s)} − 1 < ℜ s < 1 {\displaystyle -1<\Re s<1} cos ( x ) {\displaystyle \cos(x)} cos ( 1 2 π s ) Γ ( s ) {\displaystyle \cos({\tfrac {1}{2}}\pi s)\,\Gamma (s)} 0 < ℜ s < 1 {\displaystyle 0<\Re s<1} e i x {\displaystyle e^{ix}} e i π s / 2 Γ ( s ) {\displaystyle e^{i\pi s/2}\,\Gamma (s)} 0 < ℜ s < 1 {\displaystyle 0<\Re s<1} J 0 ( x ) {\displaystyle J_{0}(x)} 2 s − 1 π sin ( π s / 2 ) [ Γ ( s / 2 ) ] 2 {\displaystyle {\frac {2^{s-1}}{\pi }}\,\sin(\pi s/2)\,\left[\Gamma (s/2)\right]^{2}} 0 < ℜ s < 3 2 {\displaystyle 0<\Re s<{\tfrac {3}{2}}} J 0 ( x ) {\displaystyle J_{0}(x)} 是第一类贝塞尔函数。 Y 0 ( x ) {\displaystyle Y_{0}(x)} − 2 s − 1 π cos ( π s / 2 ) [ Γ ( s / 2 ) ] 2 {\displaystyle -{\frac {2^{s-1}}{\pi }}\,\cos(\pi s/2)\,\left[\Gamma (s/2)\right]^{2}} 0 < ℜ s < 3 2 {\displaystyle 0<\Re s<{\tfrac {3}{2}}} Y 0 ( x ) {\displaystyle Y_{0}(x)} 是第二类贝塞尔函数。 K 0 ( x ) {\displaystyle K_{0}(x)} 2 s − 2 [ Γ ( s / 2 ) ] 2 {\displaystyle 2^{s-2}\,\left[\Gamma (s/2)\right]^{2}} 0 < ℜ s < ∞ {\displaystyle 0<\Re s<\infty } K 0 ( x ) {\displaystyle K_{0}(x)} 是第二类修正贝塞尔函数。 Close 参考文献 Galambos, Janos; Simonelli, Italo. Products of random variables: applications to problems of physics and to arithmetical functions. Marcel Dekker, Inc. 2004. ISBN 0-8247-5402-6. [1]Philippe Flajolet and Robert Sedgewick. The Average Case Analysis of Algorithms: Mellin Transform Asymptotics. Research Report 2956. 93 pages. Institut National de Recherche en Informatique et en Automatique (INRIA), 1996. [2]Fitzpatrick, A. Liam; Kaplan, Jared; Penedones, Joao; Raju, Suvrat; van Rees, Balt C. A Natural Language for AdS/CFT Correlators. Journal of High Energy Physics. 2011-11, 2011 (11) [2015-11-27]. ISSN 1029-8479. doi:10.1007/JHEP11(2011)095. (原始内容存档于2022-12-25). [3]Fitzpatrick, A. Liam; Kaplan, Jared. Unitarity and the Holographic S-Matrix. Journal of High Energy Physics. 2012-10, 2012 (10) [2015-11-27]. ISSN 1029-8479. doi:10.1007/JHEP10(2012)032. (原始内容存档于2023-11-16). [4]A. Liam Fitzpatrick. "AdS/CFT and the Holographic S-Matrix" (页面存档备份,存于互联网档案馆), video lecture. [5]Jacqueline Bertrand, Pierre Bertrand, Jean-Philippe Ovarlez. The Mellin Transform. The Transforms and Applications Handbook, 1995, 978-1420066524. ffhal-03152634f Wikiwand in your browser!Seamless Wikipedia browsing. On steroids.Every time you click a link to Wikipedia, Wiktionary or Wikiquote in your browser's search results, it will show the modern Wikiwand interface.Wikiwand extension is a five stars, simple, with minimum permission required to keep your browsing private, safe and transparent.Wikiwand for ChromeWikiwand for EdgeWikiwand for Firefox
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