随机矩阵
在概率論和數學物理中,隨機矩陣(英語:Random matrix)是一個矩陣值的随机变量,也就是说,一个矩阵中的所有元素都是随机变量。[1]
應用
物理
- 原子核物理学[2][3],量子場論
- 量子混沌(quantum chaos)Bohigas–Giannoni–Schmit(BGS)猜想[4]
- 量子光學[5][6]
- 楊-米爾斯理論(量子色動力學)[7]
- 兩維的量子引力,AdS/CFT对偶,[8]
- 介观物理学,[9]
- 自旋转移矩,[10]
- 小数量子霍爾效果,[11]
- 安德森的本地化(Anderson localization)[12]
- 量子点,[13]
- 超导現象[14]
其他(AI、数学、统计)
- 数论,黎曼ζ函數和其他L函数的零分布,希尔伯特–波利亚猜想,黎曼猜想[15]
- 多元变量统计[16][17]
- 數值分析[18][19]
- 最优控制[20][21][22]
- 神经科学理论,混沌理论[22][23][24][25][26]
- 人工智能,机器学习,深度学习,深度神经网络[27][28][29]
随机矩阵模型
设是的矩阵,有下面的概率测度:
例子,高斯模型:。
- GUE (Gaussian Unitary Ensemble):H是埃尔米特矩阵。通过1/N展开,維格納半圓分布描述H的大N特征值的機率密度函數。[1]
- GOE (Orthogonal):H是对称矩阵
- GSE (Symplectic):H是四元数的矩阵(Quaternion matrix)
參見
- 維格納半圓分布
- 弗里曼·戴森气体模型(Dyson gas model)
- 1/N展开
- 普遍性 (物理学)(Universality)
- Spectral Theory
- 非古典机率(Free probability)
阅读
- 陶哲轩的Topics in random matrix theory (https://terrytao.files.wordpress.com/2011/02/matrix-book.pdf (页面存档备份,存于互联网档案馆))
- 其他书:[30][31][32]
- 文章:[33][34][35][36]
- 原始文章:[37][38][39]
- Voiculescu, Free Probability Theory and Operator Algebras
- Speicher, Free Probability Theory (https://arxiv.org/pdf/0911.0087.pdf (页面存档备份,存于互联网档案馆))
- 徐一鴻的https://en.wikipedia.org/wiki/Quantum_Field_Theory_in_a_Nutshell (页面存档备份,存于互联网档案馆) (Large N expansion)
參考文獻
- ^ 1.0 1.1 Terence Tao 陶哲轩. Topics in random matrix theory (PDF). (原始内容 (PDF)存档于2021-05-06) (英语).
- ^ Wigner, E. Characteristic vectors of bordered matrices with infinite dimensions. Annals of Mathematics. 1955, 62 (3): 548–564. JSTOR 1970079. doi:10.2307/1970079.
- ^ Mehta, M.L. Random Matrices. Amsterdam: Elsevier/Academic Press. 2004. ISBN 0-12-088409-7.
- ^ Bohigas, O.; Giannoni, M.J.; Schmit, Schmit. Characterization of Chaotic Quantum Spectra and Universality of Level Fluctuation Laws. Phys. Rev. Lett. 1984, 52 (1): 1–4. Bibcode:1984PhRvL..52....1B. doi:10.1103/PhysRevLett.52.1.
- ^ Aaronson, Scott; Arkhipov, Alex. The computational complexity of linear optics. Theory of Computing. 2013, 9: 143–252. doi:10.4086/toc.2013.v009a004.
- ^ Russell, Nicholas; Chakhmakhchyan, Levon; O'Brien, Jeremy; Laing, Anthony. Direct dialling of Haar random unitary matrices. New J. Phys. 2017, 19 (3): 033007. Bibcode:2017NJPh...19c3007R. arXiv:1506.06220 . doi:10.1088/1367-2630/aa60ed.
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- ^ Tropp, J. User-Friendly Tail Bounds for Sums of Random Matrices. Foundations of Computational Mathematics. 2011, 12 (4): 389–434. arXiv:1004.4389 . doi:10.1007/s10208-011-9099-z.
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- ^ Anderson, G.W.; Guionnet, A.; Zeitouni, O. An introduction to random matrices.. Cambridge: Cambridge University Press. 2010. ISBN 978-0-521-19452-5.
- ^ Akemann, G.; Baik, J.; Di Francesco, P. The Oxford Handbook of Random Matrix Theory.. Oxford: Oxford University Press. 2011. ISBN 978-0-19-957400-1.
- ^ Edelman, A.; Rao, N.R. Random matrix theory. Acta Numerica. 2005, 14: 233–297. Bibcode:2005AcNum..14..233E. doi:10.1017/S0962492904000236.
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- ^ Wigner, E. Characteristic vectors of bordered matrices with infinite dimensions. Annals of Mathematics. 1955, 62 (3): 548–564. JSTOR 1970079. doi:10.2307/1970079.
- ^ Wishart, J. Generalized product moment distribution in samples. Biometrika. 1928, 20A (1–2): 32–52. doi:10.1093/biomet/20a.1-2.32.
- ^ von Neumann, J.; Goldstine, H.H. Numerical inverting of matrices of high order. Bull. Amer. Math. Soc. 1947, 53 (11): 1021–1099. doi:10.1090/S0002-9904-1947-08909-6.