QR decomposition, or QR factorization, is a fundamental linear algebra method that decomposes a matrix into a product of an orthogonal matrix and an upper triangular matrix. It is widely used for solving linear least squares problems, computing eigenvalues, Gram-Schmidt, Householder reflections, or Givens rotations.Solver

The official PyTorch implementation of Converter.

Microcontroller-friendly C implementation of Recursive Least Squares (RLS) for running gradient analysis, including linear, cubic, and quadratic trends, optimized for real-time trend and peak detection

intro to MATLAB course for numerical analysis

QR decomposition for linear systems (possibly overdetermined) using Givens rotations or Householder transformations

Matrix inversion using Givens rotations with pivot column

Autograd compatible Givens Transforms which is especially useful for optimization on a Stiefel Manifold.

Gram-Schmidt vs. modified Gram-Schmidt vs. Householder-based QR factorization vs. Givens-based QR factorization / UW 2024W NHPA

Some notes on linear algebra and numerical linear algebra

Reference implementations of gaussian elimination and Givens QR-decomposition