machine-learning-math

Cholesky decomposition is a matrix factorization method that decomposes a symmetric, positive-definite matrix into the product of a lower triangular matrix and its transpose (i.e., ). LU decomposition for solving linear equations and is widely used in Monte Carlo simulations, Kalman filters, and econometrics. Solver

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