Orthogonalization methods ( such as QR factorization ) are common, for example, when solving problems by least squares methods.
12.
The method of least absolute deviations finds applications in many areas, due to its robustness compared to the least squares method.
13.
At the next step, the initial segmentation is refined by fitting quadratics whose coefficients are calculated based on the Least squares method.
14.
It is common to make the additional hypothesis that the ordinary least squares method should be used to minimize the " residuals ".
15.
Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares.
16.
The idea behind the Quasi-Newton Least Squares Method is to build up an approximate Jacobian based on known input-output pairs of the function.
17.
In ( Tiwari et al . 2001 ), this method has been applied to a grid free framework with the help of the weighted least squares method.
18.
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.
19.
Using more than four involves an over-determined system of equations with no unique solution; such a system can be solved by a least-squares or weighted least squares method.
20.
The method of iteratively re-weighted least squares ( IRLS ) is a numerical algorithm for minimizing any specified objective function using a standard weighted least squares method such as Gaussian elimination.
