Lua

Lua is a powerful, fast, lightweight, embeddable scripting language. Lua combines simple procedural syntax with powerful data description constructs based on associative arrays and extensible semantics. Lua is dynamically typed, runs by interpreting bytecode for a register-based virtual machine, and has automatic memory management with incremental garbage collection, making it ideal for configuration, scripting, and rapid prototyping.


References in zbMATH (referenced in 40 articles )

Showing results 1 to 20 of 40.
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  1. Bogaerts, Bart; Gamba, Emilio; Guns, Tias: A framework for step-wise explaining how to solve constraint satisfaction problems (2021)
  2. Tao, Yong; Ren, Fan; Chen, Youdong; Wang, Tianmiao; Zou, Yu; Chen, Chaoyong; Jiang, Shan: A method for robotic grasping based on improved Gaussian mixture model (2020)
  3. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  4. Kepner, Jeremy; Jananthan, Hayden: Mathematics of big data. Spreadsheets, databases, matrices, and graphs. With a foreword by Charles E. Leiserson (2018)
  5. Richard Beare; Bradley Lowekamp; Ziv Yaniv: Image Segmentation, Registration and Characterization in R with SimpleITK (2018) not zbMATH
  6. Sébastien Brochet; Christophe Delaere; Brieuc François; Vincent Lemaître; Alexandre Mertens; Alessia Saggio; Miguel Vidal Marono; Sébastien Wertz: MoMEMta, a modular toolkit for the Matrix Element Method at the LHC (2018) arXiv
  7. Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
  8. Skambath, Malte; Tantau, Till: Offline drawing of dynamic trees: algorithmics and document integration (2016)
  9. Bruynooghe, Maurice; Blockeel, Hendrik; Bogaerts, Bart; De Cat, Broes; De Pooter, Stef; Jansen, Joachim; Labarre, Anthony; Ramon, Jan; Denecker, Marc; Verwer, Sicco: Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3 (2015)
  10. Krause, Dorian; Dickopf, Thomas; Potse, Mark; Krause, Rolf: Towards a large-scale scalable adaptive heart model using shallow tree meshes (2015)
  11. Rudolf, Florian; Rupp, Karl; Weinbub, Josef; Morhammer, Andreas; Selberherr, Siegfried: Transformation invariant local element size specification (2015)
  12. Kindermann, Philipp; Lipp, Fabian; Wolff, Alexander: Luatodonotes: boundary labeling for annotations in texts (2014) ioport
  13. Makni, Zaatar; Demersseman, Richard: A coupled analytical-numerical approach for optimal sizing of power inductors (2014)
  14. Clerici, Silvia; Zoltan, Cristina; Prestigiacomo, Guillermo: Graphical and incremental type inference. A graph transformation approach (2013)
  15. Heinimäki, Teemu J.; Vanhatupa, Juha-Matti: Implementing artificial intelligence: a generic approach with software support (2013) ioport
  16. Muhammad, Hisham; Mascarenhas, Fabio; Ierusalimschy, Roberto: Luarocks -- a declarative and extensible package management system for Lua (2013) ioport
  17. Vogel, Andreas; Reiter, Sebastian; Rupp, Martin; Nägel, Arne; Wittum, Gabriel: \textitUG4: a novel flexible software system for simulating PDE based models on high performance computers (2013)
  18. Biggar, Paul; De Vries, Edsko; Gregg, David: A practical solution for achieving language compatibility in scripting language compilers (2012) ioport
  19. Klöckner, Andreas; Pinto, Nicolas; Lee, Yunsup; Catanzaro, Bryan; Ivanov, Paul; Fasih, Ahmed: PyCUDA and PyOpenCL: a scripting-based approach to GPU run-time code generation (2012) ioport
  20. Liu, Victor; Fan, Shanhui: (S^4): a free electromagnetic solver for layered periodic structures (2012)

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