Numerical methods for linear least squares

Summary

Numerical methods for linear least squares are algorithms for the least squares problem. They compute a vector $x$ minimizing $\norm{Ax - b}_2$ for a given matrix $A$ and vector $b$. The main methods solve the normal equations by a Cholesky decomposition, use a QR decomposition of $A$, or use the singular value decomposition. They differ in cost and in how rounding errors are amplified, which depends on the condition number of $A$.