Arroyo is an international consortium providing support for high performance computing solutions that aim to bridge the gap between research and development.

Our focus is on scalable software and hardware components for machine learning and artificial intelligence, which have wide applicability in industrial and consumer areas.

Supported Projects

MLPACK is a scalable machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. Algorithms are provided as simple command-line programs and C++ classes which can then be integrated into larger-scale machine learning solutions.

Armadillo is a high quality linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use. It provides high-level syntax deliberately similar to Matlab, making it useful for algorithm development directly in C++, or quick conversion of research code into production environments.

Bandicoot is a C++ linear algebra library exploiting GPU based processing. It has two use cases: (1) addon accelerator for the Armadillo library, offloading intensive computations to the GPU when possible, and (2) as a dedicated library for GPU matrix programming with a subset of Armadillo-like functions.

ensmallen is flexible C++ library for efficient mathematical optimization. It provides a simple set of abstractions for writing an objective function to optimize. It also provides a large set of standard and cutting-edge optimizers that can be used for virtually any mathematical optimization task. These include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization.

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