Armadillo: a template-based C++ library for linear algebra

Abstract

The C++ language is often used for implementing functionality that is performance\nand/or resource sensitive. While the standard C++ library provides many useful algorithms (such as sorting), in its current form it does not provide direct handling of\nlinear algebra (matrix maths).\nArmadillo is an open source linear algebra library for the C++ language, aiming towards\na good balance between speed and ease of use. Its high-level Application Programming\nInterface (API) is deliberately similar to the widely Matlab and Octave languages (Eaton\net al. 2015), so that mathematical operations can be expressed in a familiar and natural\nmanner. The library is useful for algorithm development directly in C++, or relatively\nquick conversion of research code into production environments.\nArmadillo provides efficient objects for vectors, matrices and cubes (third order tensors),\nas well as over 200 associated functions for manipulating data stored in the objects. Integer, floating point and complex numbers are supported, as well as dense and sparse\nstorage formats. Various matrix factorisations are provided through integration with LAPACK (Demmel 1997), or one of its high performance drop-in replacements such as Intel\nMKL (Intel 2016) or OpenBLAS (Xianyi, Qian, and Saar 2016). It is also possible to use\nArmadillo in conjunction with NVBLAS to obtain GPU-accelerated matrix multiplication\n(NVIDIA 2015).\nArmadillo is used as a base for other open source projects, such as MLPACK, a C++\nlibrary for machine learning and pattern recognition (Curtin et al. 2013), and RcppArmadillo, a bridge between the R language and C++ in order to speed up computations\n(Eddelbuettel and Sanderson 2014).\nArmadillo internally employs an expression evaluator based on template metaprogramming techniques (Abrahams and Gurtovoy 2004), to automatically combine\nseveral operations in order to increase speed and efficiency. An overview of the internal\narchitecture is given in (Sanderson 2010).