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Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data redu... Read more

A library to define abstract linear operators, and associated algebra and matrix-free algorithms, that works with pyTorch Tensors.

Quickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see... Read more

MATOPS is a menu-driven C program that implements core matrix operations used in linear algebra. It allows users to perform matrix multiplication, determinant and inverse calculation, characteristic equation generation, and computation of eigenvalues and eigenvectors for supported square matrices... Read more

📐 Linear Algebra with Matrix Operations in JavaScript. Used in vectorization implementations in machine learning algorithms.

🧮 QuatIca: Advanced Quaternion Linear Algebra Library for Python A comprehensive numerical library for quaternion matrix operations, featuring Q-SVD, Q-GMRES with LU preconditioning, eigenvalue decomposition, and signal processing applications. Perfect for 3D rotations, color image processing, a... Read more

Sparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are differe... Read more

A matrix based approach to epidemics on temporal networks. We use methods from linear algebra to simulate infection processes on time-varying topologies. For more information see reference

Blaz: a library for frugal matrix computations. Blaz provides compression/uncompression functions for matrices of floating-point numbers and makes it possible to perform basic linear algebra on the compressed matrices, without uncompressing them.