Armadillo

Armadillo is a C++ linear algebra library (matrix maths) aiming towards a good balance between speed and ease of use. Integer, floating point, and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. A delayed evaluation approach, based on template meta-programming, is used (during compile time) to combine several operations into one and reduce or eliminate the need for temporaries. (Source: http://freecode.com/)

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References in zbMATH (referenced in 46 articles )

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  1. Hartigan, Luke: Alternative HAC covariance matrix estimators with improved finite sample properties (2018)
  2. Leopoldo Catania; Nima Nonejad: Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package (2018)
  3. Sanderson, Conrad; Curtin, Ryan: A user-friendly hybrid sparse matrix class in C++ (2018)
  4. Venelin Mitov; Krzysztof Bartoszek; Georgios Asimomitis; Tanja Stadler: Fast likelihood evaluation for multivariate phylogenetic comparative methods: the PCMBase R package (2018) arXiv
  5. Walker, Shawn W.: FELICITY: a Matlab/C++ toolbox for developing finite element methods and simulation modeling (2018)
  6. Anthony Ebert, Paul Wu, Kerrie Mengersen, Fabrizio Ruggeri: Computationally Efficient Simulation of Queues: The R Package queuecomputer (2017) arXiv
  7. Barber, Xavier; Conesa, David; López-Quílez, Antonio; Mayoral, Asunción; Morales, Javier; Barber, Antoni: Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices (2017)
  8. Brault, Vincent; Chiquet, Julien; Lévy-Leduc, Céline: Efficient block boundaries estimation in block-wise constant matrices: an application to HiC data (2017)
  9. Hafstein, Sigurdur F.; Li, Huijuan: Computation of Lyapunov functions for nonautonomous systems on finite time-intervals by linear programming (2017)
  10. Hunt, Alexander; Surulescu, Christina: A multiscale modeling approach to glioma invasion with therapy (2017)
  11. Lyonnet, F.; Schienbein, I.: PyR@TE 2: a Python tool for computing RGEs at two-loop (2017)
  12. Pierre Fernique, Christophe Pradal: AutoWIG: Automatic Generation of Python Bindings for C++ Libraries (2017) arXiv
  13. Ryan R. Curtin, Shikhar Bhardwaj, Marcus Edel, Yannis Mentekidis: A generic and fast C++ optimization framework (2017) arXiv
  14. Avanzini, Francesco; Fresch, Barbara; Moro, Giorgio J.: Pilot-wave quantum theory with a single Bohm’s trajectory (2016)
  15. Klibanov, Michael V.; Nguyen, Loc H.; Pan, Kejia: Nanostructures imaging via numerical solution of a 3-D inverse scattering problem without the phase information (2016)
  16. Klibanov, Michael V.; Nguyen, Loc H.; Sullivan, Anders; Nguyen, Lam: A globally convergent numerical method for a 1-d inverse medium problem with experimental data (2016)
  17. Pastorino, Roland; Cosco, Francesco; Naets, Frank; Desmet, Wim; Cuadrado, Javier: Hard real-time multibody simulations using ARM-based embedded systems (2016)
  18. Philip Rinn, Pedro G. Lind, Matthias Waechter, Joachim Peinke: The Langevin Approach: An R Package for Modeling Markov Processes (2016) arXiv
  19. Rupp, Karl; Tillet, Philippe; Rudolf, Florian; Weinbub, Josef; Morhammer, Andreas; Grasser, Tibor; Jüngel, Ansgar; Selberherr, Siegfried: ViennaCL-linear algebra library for multi- and many-core architectures (2016)
  20. Trinath, G.; Babu, V.: On the solution of the Neumann Poisson problem arising from a compact differencing scheme using the full multi-grid method (2016)

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