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 227 articles , 1 standard article )

Showing results 1 to 20 of 227.
Sorted by year (citations)

1 2 3 ... 10 11 12 next

  1. af Klinteberg, Ludvig; Askham, Travis; Kropinski, Mary Catherine: A fast integral equation method for the two-dimensional Navier-Stokes equations (2020)
  2. Ali Ramadhan; Gregory L. Wagner; Chris Hill; Jean-Michel Campin; Valentin Churavy; Tim Besard; Andre Souza; Alan Edelman; Raffaele Ferrari; John Marshall: Oceananigans.jl: Fast and friendly geophysical uid dynamics on GPUs (2020) not zbMATH
  3. Andy Nowacki: SeisModels.jl: A Julia package for models of the Earth’s interior (2020) not zbMATH
  4. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  5. Behling, Roger; Bello-Cruz, J.-Yunier; Santos, Luiz-Rafael: The block-wise circumcentered-reflection method (2020)
  6. Bertsimas, Dimitris; Van Parys, Bart: Sparse hierarchical regression with polynomials (2020)
  7. Bierkens, Joris; van der Meulen, Frank; Schauer, Moritz: Simulation of elliptic and hypo-elliptic conditional diffusions (2020)
  8. Bognanni, Mark; Zito, John: Sequential Bayesian inference for vector autoregressions with stochastic volatility (2020)
  9. Borggaard, Jeff; Glatt-Holtz, Nathan; Krometis, Justin: A Bayesian approach to estimating background flows from a passive scalar (2020)
  10. Breuer, T.; Héthelyi, L.; Horváth, E.; Külshammer, B.: The Loewy structure of certain fixpoint algebras. I (2020)
  11. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  12. Cazeaux, Paul; Luskin, Mitchell; Massatt, Daniel: Energy minimization of two dimensional incommensurate heterostructures (2020)
  13. Cea, Sebastián; Durán, Guillermo; Guajardo, Mario; Sauré, Denis; Siebert, Joaquín; Zamorano, Gonzalo: An analytics approach to the FIFA ranking procedure and the world cup final draw (2020)
  14. Chirre, Andrés; Gonçalves, Felipe; de Laat, David: Pair correlation estimates for the zeros of the zeta function via semidefinite programming (2020)
  15. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  16. de Laat, David: Moment methods in energy minimization: new bounds for Riesz minimal energy problems (2020)
  17. Downward, Anthony; Dowson, Oscar; Baucke, Regan: Stochastic dual dynamic programming with stagewise-dependent objective uncertainty (2020)
  18. Dravins, Ivo; Neytcheva, Maya: PDE-constrained optimization: matrix structures and preconditioners (2020)
  19. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  20. Eliasof, Moshe; Sharf, Andrei; Treister, Eran: Multimodal 3D shape reconstruction under calibration uncertainty using parametric level set methods (2020)

1 2 3 ... 10 11 12 next

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