Monte Carlo Integration Using Elliptic Curves

被引:0
|
作者
Mok, Chung Pang [1 ]
Zheng, Huimin [2 ,3 ,4 ]
机构
[1] Shanghai Inst Math & Interdisciplinary Sci, Shanghai 200438, Peoples R China
[2] Nanjing Univ, Dept Math, Nanjing 210093, Peoples R China
[3] Jiangsu Natl Ctr Appl Math, Nanjing 210023, Peoples R China
[4] Anhui Sci & Technol Univ, Coll Informat & Network Engn, Bengbu 233030, Anhui, Peoples R China
关键词
Pseudorandom vectors; Elliptic curves; Finite fields; Monte Carlo integration; Feynman-Kac formulas;
D O I
10.1007/s11401-025-0013-4
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The authors carry out numerical experiments with regard to the Monte Carlo integration method, using as input the pseudorandom vectors that are generated by the algorithm proposed in [Mok, C. P., Pseudorandom Vector Generation Using Elliptic Curves and Applications to Wiener Processes, Finite Fields and Their Applications, 85, 2023, 102129], which is based on the arithmetic theory of elliptic curves over finite fields. They consider integration in the following two cases: The case of Lebesgue measure on the unit hypercube [0, 1]d, and as well as the case of Wiener measure. In the case of Wiener measure, the construction gives discrete time simulation of an independent sequence of standard Wiener processes, which is then used for the numerical evaluation of Feynman-Kac formulas.
引用
收藏
页码:241 / 260
页数:20
相关论文
共 50 条
  • [1] Monte Carlo Integration Using Elliptic Curves
    Chung Pang MOK
    Huimin ZHENG
    Chinese Annals of Mathematics,Series B, 2025, (02) : 241 - 260
  • [2] A Primer in Monte Carlo Integration Using Mathcad
    Hoyer, Chad E.
    Kegerreis, Jeb S.
    JOURNAL OF CHEMICAL EDUCATION, 2013, 90 (09) : 1186 - 1190
  • [3] Bayesian Reliability Modeling Using Monte Carlo Integration
    Camara, Vincent A. R.
    Tsokos, Chris P.
    JOURNAL OF MODERN APPLIED STATISTICAL METHODS, 2005, 4 (01) : 172 - 186
  • [4] On the use of stochastic approximation Monte Carlo for Monte Carlo integration
    Liang, Faming
    STATISTICS & PROBABILITY LETTERS, 2009, 79 (05) : 581 - 587
  • [5] On Accelerating Monte Carlo Integration Using Orthogonal Projections
    Huei-Wen Teng
    Ming-Hsuan Kang
    Methodology and Computing in Applied Probability, 2022, 24 : 1143 - 1168
  • [6] On Accelerating Monte Carlo Integration Using Orthogonal Projections
    Teng, Huei-Wen
    Kang, Ming-Hsuan
    METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY, 2022, 24 (02) : 1143 - 1168
  • [7] Monte Carlo integration on GPU
    Kanzaki, J.
    EUROPEAN PHYSICAL JOURNAL C, 2011, 71 (02):
  • [8] Monte Carlo integration with subtraction
    Arthur, Rudy
    Kennedy, A. D.
    COMPUTER PHYSICS COMMUNICATIONS, 2013, 184 (12) : 2794 - 2802
  • [9] Amortized Monte Carlo Integration
    Golinski, Adam
    Wood, Frank
    Rainforth, Tom
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97
  • [10] Monte Carlo integration on GPU
    J. Kanzaki
    The European Physical Journal C, 2011, 71