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 450 articles , 1 standard article )

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  1. Ahmed, Shabbir; Cabral, Filipe Goulart; Freitas Paulo da Costa, Bernardo: Stochastic Lipschitz dynamic programming (2022)
  2. Al-Mohy, Awad H.; Higham, Nicholas J.; Liu, Xiaobo: Arbitrary precision algorithms for computing the matrix cosine and its Fréchet derivative (2022)
  3. Antoñana, Mikel; Chartier, Philippe; Murua, Ander: Majorant series for the (N)-body problem (2022)
  4. Becher, Simon; Matthies, Gunar: Unified analysis for variational time discretizations of higher order and higher regularity applied to non-stiff ODEs (2022)
  5. Behague, Natalie C.; Marbach, Trent G.; Prałat, Paweł: Tight bounds on probabilistic zero forcing on hypercubes and grids (2022)
  6. Bena, Iosif; Blåbäck, Johan; Graña, Mariana; Lüst, Severin: Algorithmically solving the tadpole problem (2022)
  7. Berger, Mathias; Radu, David; Dubois, Antoine; Pandžić, Hrvoje; Dvorkin, Yury; Louveaux, Quentin; Ernst, Damien: Siting renewable power generation assets with combinatorial optimisation (2022)
  8. Bertsimas, Dimitris; Dunn, Jack; Kapelevich, Lea; Zhang, Rebecca: Sparse regression over clusters: SparClur (2022)
  9. Borisov, Lev; Buch, Anders; Fatighenti, Enrico: A journey from the octonionic (\mathbbP^2) to a fake (\mathbbP^2) (2022)
  10. Bou-Rabee, Ahmed: Dynamic dimensional reduction in the abelian sandpile (2022)
  11. Ciaran Welsh, Jin Xu, Lucian Smith, Matthias König, Kiri Choi, Herbert M. Sauro: libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library (2022) arXiv
  12. Ekre, Fredrik; Larsson, Fredrik; Runesson, Kenneth; Jänicke, Ralf: Combining spectral and POD modes to improve error estimation of numerical model reduction for porous media (2022)
  13. Ekre, Fredrik; Larsson, Fredrik; Runesson, Kenneth; Jänicke, Ralf: Numerical model reduction with error estimation for computational homogenization of non-linear consolidation (2022)
  14. Ekström, Sven-Erik; Vassalos, Paris: A matrix-less method to approximate the spectrum and the spectral function of Toeplitz matrices with real eigenvalues (2022)
  15. Gao, Pu; Kamiński, Bogumił; MacRury, Calum; Prałat, Paweł: Hamilton cycles in the semi-random graph process (2022)
  16. James Gardner, Oscar A. Douglas-Gallardo, Wojciech G. Stark, Julia Westermayr, Svenja M. Janke, Scott Habershon, Reinhard J. Maurer: NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase (2022) arXiv
  17. Jan Košata, Javier del Pino, Toni L. Heugel, Oded Zilberberg: HarmonicBalance.jl: A Julia suite for nonlinear dynamics using harmonic balance (2022) arXiv
  18. Jorgensen, Jeremy J.; Christensen, John E.; Jarvis, Tyler J.; Hart, Gus L. W.: A general algorithm for calculating irreducible Brillouin zones (2022)
  19. Kugler, Benoit; Forbes, Florence; Douté, Sylvain: Fast Bayesian inversion for high dimensional inverse problems (2022)
  20. Law, Yann-Meing; Nave, Jean-Christophe: High-order FDTD schemes for Maxwell’s interface problems with discontinuous coefficients and complex interfaces based on the correction function method (2022)

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