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 152 articles , 1 standard article )

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  1. Poirion, Pierre-Louis; Toubaline, Sonia; D’Ambrosio, Claudia; Liberti, Leo: Algorithms and applications for a class of bilevel MILPs (2020)
  2. Agrawal, Akshay; Diamond, Steven; Boyd, Stephen: Disciplined geometric programming (2019)
  3. Andrade, Tomás; Emparan, Roberto; Licht, David; Luna, Raimon: Cosmic censorship violation in black hole collisions in higher dimensions (2019)
  4. Berk, Lauren; Bertsimas, Dimitris; Weinstein, Alexander M.; Yan, Julia: Prescriptive analytics for human resource planning in the professional services industry (2019)
  5. Bertsimas, Dimitris; Jaillet, Patrick; Korolko, Nikita: The (K)-server problem via a modern optimization Lens (2019)
  6. Bertsimas, Dimitris; Ng, Yeesian: Robust and stochastic formulations for ambulance deployment and dispatch (2019)
  7. Bogomolov, Sergiy; Forets, Marcelo; Frehse, Goran; Potomkin, Kostiantyn; Schilling, Christian: JuliaReach: a toolbox for set-based reachability (2019)
  8. Breiding, Paul; Kozhasov, Khazhgali; Lerario, Antonio: Random spectrahedra (2019)
  9. Camargo, Valter S.; Castelani, Emerson V.; Fernandes, Leandro A. F.; Fidalgo, Felipe: Geometric algebra to describe the exact discretizable molecular distance geometry problem for an arbitrary dimension (2019)
  10. Cambier, Léopold; Darve, Eric: Fast low-rank kernel matrix factorization using skeletonized interpolation (2019)
  11. Caraiani, Petre: Introduction to quantitative macroeconomics using Julia. From basic to state-of-the-art computational techniques (2019)
  12. Chris Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit: DiffEqFlux.jl - A Julia Library for Neural Differential Equations (2019) arXiv
  13. Contardo, Claudio; Iori, Manuel; Kramer, Raphael: A scalable exact algorithm for the vertex (p)-center problem (2019)
  14. Contreras, Carlos; Carrero, Gustavo; de Vries, Gerda: A mathematical model for the effect of low-dose radiation on the G2/M transition (2019)
  15. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  16. Damle, Anil; Levitt, Antoine; Lin, Lin: Variational formulation for Wannier functions with entangled band structure (2019)
  17. Domino, Krzysztof; Gawron, Piotr: An algorithm for arbitrary-order cumulant tensor calculation in a sliding window of data streams (2019)
  18. Douven, Igor: Optimizing group learning: an evolutionary computing approach (2019)
  19. Dowson, Oscar; Philpott, Andy; Mason, Andrew; Downward, Anthony: A multi-stage stochastic optimization model of a pastoral dairy farm (2019)
  20. Emerson V. Castelani; Ronaldo Lopes; Wesley V. I. Shirabayashi; Francisco N. C. Sobral: RAFF.jl: Robust Algebraic Fitting Function in Julia (2019) not zbMATH

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