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 218 articles , 1 standard article )

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  1. af Klinteberg, Ludvig; Askham, Travis; Kropinski, Mary Catherine: A fast integral equation method for the two-dimensional Navier-Stokes equations (2020)
  2. Andy Nowacki: SeisModels.jl: A Julia package for models of the Earth’s interior (2020) not zbMATH
  3. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  4. Behling, Roger; Bello-Cruz, J.-Yunier; Santos, Luiz-Rafael: The block-wise circumcentered-reflection method (2020)
  5. Bertsimas, Dimitris; Van Parys, Bart: Sparse hierarchical regression with polynomials (2020)
  6. Bierkens, Joris; van der Meulen, Frank; Schauer, Moritz: Simulation of elliptic and hypo-elliptic conditional diffusions (2020)
  7. Bognanni, Mark; Zito, John: Sequential Bayesian inference for vector autoregressions with stochastic volatility (2020)
  8. Breuer, T.; Héthelyi, L.; Horváth, E.; Külshammer, B.: The Loewy structure of certain fixpoint algebras. I (2020)
  9. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  10. Cazeaux, Paul; Luskin, Mitchell; Massatt, Daniel: Energy minimization of two dimensional incommensurate heterostructures (2020)
  11. 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)
  12. Chirre, Andrés; Gonçalves, Felipe; de Laat, David: Pair correlation estimates for the zeros of the zeta function via semidefinite programming (2020)
  13. de Laat, David: Moment methods in energy minimization: new bounds for Riesz minimal energy problems (2020)
  14. Downward, Anthony; Dowson, Oscar; Baucke, Regan: Stochastic dual dynamic programming with stagewise-dependent objective uncertainty (2020)
  15. Dravins, Ivo; Neytcheva, Maya: PDE-constrained optimization: matrix structures and preconditioners (2020)
  16. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  17. Eliasof, Moshe; Sharf, Andrei; Treister, Eran: Multimodal 3D shape reconstruction under calibration uncertainty using parametric level set methods (2020)
  18. Essaouini, M.; Abouzaid, B.; Gaudreau, P.; Safouhi, H.: Computation of energy eigenvalues of the anharmonic Coulombic potential with irregular singularities (2020)
  19. Garzella, Jack J.; Baranowski, Marek; He, Shaobo; Rakamarić, Zvonimir: Leveraging compiler intermediate representation for multi- and cross-language verification (2020)
  20. Gugushvili, Shota; van der Meulen, Frank; Schauer, Moritz; Spreij, Peter: Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient (2020)

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