In numerical analysis, The Quasi-Newton Inverse Least Squares Method is a quasi-Newton method for finding roots in n variables. It was originally described by Degroote et al. in 2009.
Newton's method for solving f(x) = 0 uses the Jacobian matrix, J, at every iteration. However, computing this Jacobian is a difficult (sometimes even impossible) and expensive operation. The idea behind the Quasi-Newton Inverse Least Squares Method is to build up an approximate Jacobian based on known input-output pairs of the function f.
Haelterman et al. also showed that when the Quasi-Newton Inverse Least Squares Method is applied to a linear system of size n × n, it converges in at most n +1 steps although like all quasi-Newton methods, it may not converge for nonlinear systems.
The method is closely related to the Quasi-Newton Least Squares Method