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[DEPRECATED] Statistics & Numerical algorithms implemented in Julia.

License: GNU General Public License v3.0

Julia 100.00%
numerical-methods numerical-analysis numerical-optimization numerical-integration mathematical-modelling mathematics applied-mathematics numerical-computation simulation julia julia-language julialang scientific-computing monte-carlo statistics algorithms

numericalalgorithms.jl's Introduction

DEPRECATED: I'm giving up with Julia and I'll implement similar algorithms in another language. If you want to develop further, please fork this repository.

Why I gave up with Julia:

  1. Lack of OOP
  2. Lack of static type checking: most errors were encountered in runtime
  3. Unreadable library source codes (due to lack of OOP and type system)
  4. ...

NumericalAlgorithms.jl

No Maintenance Intended CI codecov GitHub license

Numerical algorithms implemented in Julia.

Installation

Install the package with add https://github.com/mrtkp9993/NumericalAlgorithms.jl in package mode (]).

Algorithms

Currently implemented:

  • Root finding algorithms
    • Secant method
    • Broyden's method
  • Differentation
    • Automatic differentiation via dual numbers
  • Integration
    • Composite Simpson - One dim.
    • Double Simpson - Two dim.
    • Monte Carlo Integration - Arbitrary dimension
  • Random Number Generators (RNGs)
    • Pseudo-random numbers
      • Lagged Fibonacci generator
      • RANMAR
    • Quasi-random numbers
      • van der Corput sequences
      • Halton sequences
      • Faure sequences
      • Sobol sequences
  • Markov Chain Monte Carlo (MCMC) for sampling
  • Statistical Tests
    • Wald–Wolfowitz runs test

License

Distributed under the GPL License. See LICENSE for more information.

Contact

Murat Koptur, LinkedIn

Email: [email protected]

References

  • Press, William H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical Recipes 3rd Edition: The Art of Scientific Computing (3rd ed.). Cambridge, England: Cambridge University Press.
  • Kochenderfer, M. J., & Wheeler, T. A. (2019). Algorithms for Optimization (The MIT Press) (Illustrated ed.). The MIT Press.
  • Burden, R. L., & Faires, D. J. (2010). Numerical Analysis (9th ed.). Cengage Learning.
  • Zwillinger, D. (2018). CRC Standard Mathematical Tables and Formulas, 33rd Edition. Amsterdam University Press.
  • Stoop, R., Hardy, A., Hardy, Y., & Steeb, W. (2004). Problems and Solutions in Scientific Computing with C++ and Java Simulations. World Scientific Publishing Company.
  • Weinzierl, S. (2000, June 23). Introduction to Monte Carlo methods. ArXiv.Org. https://arxiv.org/abs/hep-ph/0006269.
  • Lists of small primes. (2020). The PrimePages: Prime Number Research & Records. https://primes.utm.edu/lists/small/.

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