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

🧮Implementations of numerical linear algebra algorithms including Cholesky, LU Decomposition, ESOR, PSD check, and QR using Householder reflections, as part of the Linear Numerical Algebra ΘΠ03 course.