Julia: A fast dynamic language for technical computing. Dynamic languages have become popular for scientific computing. They are generally considered highly productive, but lacking in performance. This paper presents Julia, a new dynamic language for technical computing, designed for performance from the beginning by adapting and extending modern programming language techniques. A design based on generic functions and a rich type system simultaneously enables an expressive programming model and successful type inference, leading to good performance for a wide range of programs. This makes it possible for much of the Julia library to be written in Julia itself, while also incorporating best-of-breed C and Fortran libraries.

References in zbMATH (referenced in 49 articles , 1 standard article )

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  1. Crean, Jared; Hicken, Jason E.; Del Rey Fernández, David C.; Zingg, David W.; Carpenter, Mark H.: Entropy-stable summation-by-parts discretization of the Euler equations on general curved elements (2018)
  2. Fasi, Massimiliano; Higham, Nicholas J.: Multiprecision algorithms for computing the matrix logarithm (2018)
  3. Fasiolo, Matteo; de Melo, Flávio Eler; Maskell, Simon: Langevin incremental mixture importance sampling (2018)
  4. Mescheder, L. M.; Lorenz, D. A.: An extended Perona-Malik model based on probabilistic models (2018)
  5. Art B. Owen: A randomized Halton algorithm in R (2017) arXiv
  6. Bertsimas, Dimitris; Dunn, Jack: Optimal classification trees (2017)
  7. Bertsimas, Dimitris; Mišić, Velibor V.: Robust product line design (2017)
  8. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  9. Cancès, Eric; Cazeaux, Paul; Luskin, Mitchell: Generalized Kubo formulas for the transport properties of incommensurate 2D atomic heterostructures (2017)
  10. Claus Fieker, William Hart, Tommy Hofmann, Fredrik Johansson: Nemo/Hecke: Computer Algebra and Number Theory Packages for the Julia Programming Language (2017) arXiv
  11. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  12. Egorov, Maxim; Sunberg, Zachary N.; Balaban, Edward; Wheeler, Tim A.; Gupta, Jayesh K.; Kochenderfer, Mykel J.: POMDPs.jl: a framework for sequential decision making under uncertainty (2017)
  13. Fox, Alyson; Manteuffel, Thomas; Sanders, Geoffrey: Numerical methods for Gremban’s expansion of signed graphs (2017)
  14. Francesco Furiani, Giulio Martella, Alberto Paoluzzi: Geometric Computing with Chain Complexes: Design and Features of a Julia Package (2017) arXiv
  15. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  16. Gaudreau, Philippe; Safouhi, Hassan: Double exponential sinc-collocation method for solving the energy eigenvalues of harmonic oscillators perturbed by a rational function (2017)
  17. Kressner, Daniel; Periša, Lana: Recompression of Hadamard products of tensors in Tucker format (2017)
  18. Moore, M.Nicholas J.: A fast Chebyshev method for simulating flexible-wing propulsion (2017)
  19. Paul Breiding, Sascha Timme: HomotopyContinuation.jl - a package for solving systems of polynomial equations in Julia (2017) arXiv
  20. Rathijit Sen, Jianqiao Zhu, Jignesh M. Patel, Somesh Jha: ROSA: R Optimizations with Static Analysis (2017) arXiv

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Further publications can be found at: http://julialang.org/publications/