Armadillo - C++ library for linear algebra & scientific computing

 

• Download latest version

• Documentation (with code examples)

• Bug reports and frequently asked questions

• Source code repo (GitLab)

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Overview

• Fast C++ matrix library with easy to use functions and syntax, deliberately similar to Matlab. Uses template meta-programming techniques.

• Also provides efficient wrappers for LAPACK, BLAS and ATLAS libraries, including high-performance versions such as Intel MKL, AMD ACML and OpenBLAS.

• Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc.

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Features

• Easy to use
• Many MATLAB like functions
• Efficient classes for vectors, matrices, cubes (3rd order tensors) and fields
• Fast singular value decomposition (SVD), eigen decomposition, QR, LU, Cholesky, FFT
• Statistical modelling using Gaussian Mixture Models (GMM)
• Clustering using K-means and Expectation Maximisation
• Automatic vectorisation of expressions (SIMD)
• Contiguous and non-contiguous submatrices
• Automatically combines several operations into one
• Useful for prototyping directly in C++
• Useful for conversion of research code into production environments

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Developers

• Conrad Sanderson
• Ryan Curtin

NOTE: please see the Questions page before contacting the developers

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Related Projects

• ensmallen - fast non-linear numerical optimisation library
• mlpack - extensive library of machine learning algorithms
• PyArmadillo - linear algebra library for Python with Matlab-like syntax
• CARMA - bidirectional interface between Python and Armadillo