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 128 articles , 1 standard article )

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  1. Berk, Lauren; Bertsimas, Dimitris; Weinstein, Alexander M.; Yan, Julia: Prescriptive analytics for human resource planning in the professional services industry (2019)
  2. Bertsimas, Dimitris; Jaillet, Patrick; Korolko, Nikita: The (K)-server problem via a modern optimization Lens (2019)
  3. Bogomolov, Sergiy; Forets, Marcelo; Frehse, Goran; Potomkin, Kostiantyn; Schilling, Christian: JuliaReach: a toolbox for set-based reachability (2019)
  4. Caraiani, Petre: Introduction to quantitative macroeconomics using Julia. From basic to state-of-the-art computational techniques (2019)
  5. Chris Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit: DiffEqFlux.jl - A Julia Library for Neural Differential Equations (2019) arXiv
  6. Contardo, Claudio; Iori, Manuel; Kramer, Raphael: A scalable exact algorithm for the vertex (p)-center problem (2019)
  7. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  8. Damle, Anil; Levitt, Antoine; Lin, Lin: Variational formulation for Wannier functions with entangled band structure (2019)
  9. Dowson, Oscar; Philpott, Andy; Mason, Andrew; Downward, Anthony: A multi-stage stochastic optimization model of a pastoral dairy farm (2019)
  10. Emerson V. Castelani; Ronaldo Lopes; Wesley V. I. Shirabayashi; Francisco N. C. Sobral: RAFF.jl: Robust Algebraic Fitting Function in Julia (2019) not zbMATH
  11. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  12. Fung, Samy Wu; Ruthotto, Lars: A multiscale method for model order reduction in PDE parameter estimation (2019)
  13. Gevorkyan, Migran N.; Korolkova, Anna V.; Kulyabov, Dmitry S.; Lovetskiy, Konstantin P.: Statistically significant comparative performance testing of Julia and Fortran languages in case of Runge-Kutta methods (2019)
  14. Hesaraki, Alireza F.; Dellaert, Nico P.; de Kok, Ton: Generating outpatient chemotherapy appointment templates with balanced flowtime and makespan (2019)
  15. Higham, Nicholas J.; Pranesh, Srikara: Simulating low precision floating-point arithmetic (2019)
  16. Jared M. Noynaert: PressureDrop.jl: Pressure traverses and gas lift analysis for oil & gas wells (2019) not zbMATH
  17. Kristoffer Carlsson; Fredrik Ekre: Tensors.jl - Tensor Computations in Julia (2019) not zbMATH
  18. Mathieu Besançon: A Julia package for bilevel optimization problems (2019) not zbMATH
  19. Raphael Saavedra, Guilherme Bodin, Mario Souto: StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework (2019) arXiv
  20. Sambit Kumar Dash: PDFIO: PDF Reader Library for native Julia (2019) not zbMATH

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