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 202 articles , 1 standard article )

Showing results 1 to 20 of 202.
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  1. Andy Nowacki: SeisModels.jl: A Julia package for models of the Earth’s interior (2020) not zbMATH
  2. Behling, Roger; Bello-Cruz, J.-Yunier; Santos, Luiz-Rafael: The block-wise circumcentered-reflection method (2020)
  3. Bognanni, Mark; Zito, John: Sequential Bayesian inference for vector autoregressions with stochastic volatility (2020)
  4. Breuer, T.; Héthelyi, L.; Horváth, E.; Külshammer, B.: The Loewy structure of certain fixpoint algebras. I (2020)
  5. Bueno, Luís Felipe; Haeser, Gabriel; Santos, Luiz-Rafael: Towards an efficient augmented Lagrangian method for convex quadratic programming (2020)
  6. Cazeaux, Paul; Luskin, Mitchell; Massatt, Daniel: Energy minimization of two dimensional incommensurate heterostructures (2020)
  7. 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)
  8. Chirre, Andrés; Gonçalves, Felipe; de Laat, David: Pair correlation estimates for the zeros of the zeta function via semidefinite programming (2020)
  9. de Laat, David: Moment methods in energy minimization: new bounds for Riesz minimal energy problems (2020)
  10. Downward, Anthony; Dowson, Oscar; Baucke, Regan: Stochastic dual dynamic programming with stagewise-dependent objective uncertainty (2020)
  11. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  12. Eliasof, Moshe; Sharf, Andrei; Treister, Eran: Multimodal 3D shape reconstruction under calibration uncertainty using parametric level set methods (2020)
  13. Haller, George; Karrasch, Daniel; Kogelbauer, Florian: Barriers to the transport of diffusive scalars in compressible flows (2020)
  14. Hicken, Jason E.: Entropy-stable, high-order summation-by-parts discretizations without interface penalties (2020)
  15. Hudson, Thomas; Legoll, Frédéric; Lelièvre, Tony: Stochastic homogenization of a scalar viscoelastic model exhibiting stress-strain hysteresis (2020)
  16. Isensee, Jonas; Datseris, George; Parlitz, Ulrich: Predicting spatio-temporal time series using dimension reduced local states (2020)
  17. Jeon, Jong-June; Kim, Yongdai; Won, Sungho; Choi, Hosik: Primal path algorithm for compositional data analysis (2020)
  18. J L Kaplan, A Bonfanti, A Kabla: RHEOS.jl-A Julia Package for Rheology Data Analysis (2020) arXiv
  19. Justin Angevaare, Zeny Feng, Rob Deardon: Infectious Disease Transmission Network Modelling with Julia (2020) arXiv
  20. Karrasch, Daniel; Schilling, Nathanael: Fast and robust computation of coherent Lagrangian vortices on very large two-dimensional domains (2020)

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