Julia

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 37 articles )

Showing results 1 to 20 of 37.
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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. Friedlander, Michael P.; Goh, Gabriel: Efficient evaluation of scaled proximal operators (2017)
  9. Kressner, Daniel; Periša, Lana: Recompression of Hadamard products of tensors in Tucker format (2017)
  10. Paul Breiding, Sascha Timme: HomotopyContinuation.jl - a package for solving systems of polynomial equations in Julia (2017) arXiv
  11. Rathijit Sen, Jianqiao Zhu, Jignesh M. Patel, Somesh Jha: ROSA: R Optimizations with Static Analysis (2017) arXiv
  12. Ruthotto, Lars; Treister, Eran; Haber, Eldad: jInv -- a flexible Julia package for PDE parameter estimation (2017)
  13. Anderes, Ethan; Borgwardt, Steffen; Miller, Jacob: Discrete Wasserstein barycenters: optimal transport for discrete data (2016)
  14. 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)
  15. Bertsimas, Dimitris; de Ruiter, Frans J.C.T.: Duality in two-stage adaptive linear optimization: faster computation and stronger bounds (2016)
  16. Bertsimas, Dimitris; Dunning, Iain: Multistage robust mixed-integer optimization with adaptive partitions (2016)
  17. Bertsimas, Dimitris; King, Angela: OR forum: An algorithmic approach to linear regression (2016)
  18. Chen, Huajie; Ortner, Christoph: QM/MM methods for crystalline defects. I: Locality of the tight binding model (2016)
  19. Duncan, Andrew; Erban, Radek; Zygalakis, Konstantinos: Hybrid framework for the simulation of stochastic chemical kinetics (2016)
  20. Edelman, Alan; Guionnet, A.; Péché, S.: Beyond universality in random matrix theory (2016)

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