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 41 articles , 1 standard article )

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  1. Art B. Owen: A randomized Halton algorithm in R (2017) arXiv
  2. Bertsimas, Dimitris; Mišić, Velibor V.: Robust product line design (2017)
  3. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  4. Cancès, Eric; Cazeaux, Paul; Luskin, Mitchell: Generalized Kubo formulas for the transport properties of incommensurate 2D atomic heterostructures (2017)
  5. Claus Fieker, William Hart, Tommy Hofmann, Fredrik Johansson: Nemo/Hecke: Computer Algebra and Number Theory Packages for the Julia Programming Language (2017) arXiv
  6. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  7. 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)
  8. Francesco Furiani, Giulio Martella, Alberto Paoluzzi: Geometric Computing with Chain Complexes: Design and Features of a Julia Package (2017) arXiv
  9. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  10. Kressner, Daniel; Periša, Lana: Recompression of Hadamard products of tensors in Tucker format (2017)
  11. Moore, M.Nicholas J.: A fast Chebyshev method for simulating flexible-wing propulsion (2017)
  12. Paul Breiding, Sascha Timme: HomotopyContinuation.jl - a package for solving systems of polynomial equations in Julia (2017) arXiv
  13. Rathijit Sen, Jianqiao Zhu, Jignesh M. Patel, Somesh Jha: ROSA: R Optimizations with Static Analysis (2017) arXiv
  14. Ruthotto, Lars; Treister, Eran; Haber, Eldad: jInv -- a flexible Julia package for PDE parameter estimation (2017)
  15. Slevinsky, Richard Mikael; Olver, Sheehan: A fast and well-conditioned spectral method for singular integral equations (2017)
  16. Vielma, Juan Pablo; Dunning, Iain; Huchette, Joey; Lubin, Miles: Extended formulations in mixed integer conic quadratic programming (2017)
  17. Anderes, Ethan; Borgwardt, Steffen; Miller, Jacob: Discrete Wasserstein barycenters: optimal transport for discrete data (2016)
  18. Auzinger, Winfried; Hofstätter, Harald; Koch, Othmar: Symbolic manipulation of flows of nonlinear evolution equations, with application in the analysis of split-step time integrators (2016)
  19. Bertsimas, Dimitris; de Ruiter, Frans J.C.T.: Duality in two-stage adaptive linear optimization: faster computation and stronger bounds (2016)
  20. Bertsimas, Dimitris; Dunning, Iain: Multistage robust mixed-integer optimization with adaptive partitions (2016)

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