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* Fast C++ library for linear algebra (matrix maths) and scientific computing
* Easy to use functions and syntax, deliberately similar to Matlab / Octave
* Uses template meta-programming techniques to increase efficiency
* Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries
* Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc.
* Downloads: http://arma.sourceforge.net/download.html
* Documentation: http://arma.sourceforge.net/docs.html
* Bug reports: http://arma.sourceforge.net/faq.html
* Git repo: https://gitlab.com/conradsnicta/armadillo-code
Features
- Easy to use - has many MATLAB like functions
- Useful for prototyping directly in C++
- Useful for conversion of research code into production environments
- Permissively licensed - can be used in proprietary software and products
- Used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc
- Efficient classes for vectors, matrices, cubes (1st, 2nd, 3rd order tensors)
- Supports dense and sparse matrices
- Fast singular value decomposition (SVD), eigen decomposition, QR, LU, Cholesky, FFT
- Clustering using k-means and Gaussian Mixture Models (GMM)
- Automatic vectorisation of expressions (SIMD)
- Contiguous and non-contiguous submatrices
- Automatically combines several operations into one to increase speed and efficiency
- Read/write data in CSV files
- Automatically uses OpenMP for acceleration via multi-threading
- Used for speeding up NumPy / Python via CARMA: https://github.com/RUrlus/carma
- Used for machine learning and pattern recognition by MLPACK: https://mlpack.org/
- Used for numerical optimisation by Ensmallen: https://ensmallen.org/
