GeezerVison

* 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: https://arma.sourceforge.net/download.html
* Documentation: https://arma.sourceforge.net/docs.html
* Bug reports: https://arma.sourceforge.net/faq.html
* Git repo: https://gitlab.com/armadillo-lib/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/

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