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 17 articles )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. Anderes, Ethan; Borgwardt, Steffen; Miller, Jacob: Discrete Wasserstein barycenters: optimal transport for discrete data (2016)
  2. 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)
  3. Bertsimas, Dimitris; de Ruiter, Frans J.C.T.: Duality in two-stage adaptive linear optimization: faster computation and stronger bounds (2016)
  4. Bertsimas, Dimitris; Dunning, Iain: Multistage robust mixed-integer optimization with adaptive partitions (2016)
  5. Bertsimas, Dimitris; King, Angela: OR forum: An algorithmic approach to linear regression (2016)
  6. Chen, Huajie; Ortner, Christoph: QM/MM methods for crystalline defects. I: Locality of the tight binding model (2016)
  7. Gaudreau, P.; Slevinsky, R.; Safouhi, H.: The double exponential sinc collocation method for singular Sturm-Liouville problems (2016)
  8. Geiersbach, Caroline; Heitzinger, Clemens; Tulzer, Gerhard: Optimal approximation of the first-order corrector in multiscale stochastic elliptic PDE (2016)
  9. Gose, Alexander H.; Denton, Brian T.: Sequential bounding methods for two-stage stochastic programs (2016)
  10. Higham, Nicholas J.; Noferini, Vanni: An algorithm to compute the polar decomposition of a $3 \times 3$ matrix (2016)
  11. Kraemer, Atahualpa S.; Kryukov, Nikolay; Sanders, David P.: Efficient algorithms for general periodic Lorentz gases in two and three dimensions (2016)
  12. Knopp, T.; Weber, A.: Local system matrix compression for efficient reconstruction in magnetic particle imaging (2015)
  13. Lubin, Miles; Dunning, Iain: Computing in operations research using Julia (2015)
  14. Plumb, Gregory; Pachauri, Deepti; Kondor, Risi; Singh, Vikas: $\Bbb S_n$FFT: a Julia toolkit for Fourier analysis of functions over permutations (2015)
  15. Romano, Paul K.: An algorithm for generating random variates from the Madland-Nix fission energy spectrum (2015)
  16. Slevinsky, Richard Mikael; Olver, Sheehan: On the use of conformal maps for the acceleration of convergence of the trapezoidal rule and sinc numerical methods (2015)
  17. Townsend, Alex; Olver, Sheehan: The automatic solution of partial differential equations using a global spectral method (2015)


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