Introduction to Linear Algebra, Gilbert Strang, 2023 (Wellesley-Cambridge Press) - Covers fundamental concepts of QR decomposition, orthogonal matrices, Gram-Schmidt, and applications in least squares.
Matrix Computations, Gene H. Golub and Charles F. Van Loan, 2013 (Johns Hopkins University Press)DOI: 10.1137/1.9781421407944 - Authoritative text on numerical linear algebra, detailing algorithms like Householder reflections and Givens rotations for QR decomposition and their numerical stability.
Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020 (Cambridge University Press)DOI: 10.1017/9781108679989 - Provides a machine learning context for QR decomposition, explaining its role and advantages in solving least-squares problems from a practical perspective.