The following is a summarization of articles in [62,63], a good
introduction on this topic can also be found in [134]
and [135]. A modern description for curves and surface fits is given
in [61].
Linear systems can be represented in matrix form as the matrix equation
, where
One possible way is to find an approximate solution of the linear system
given by Equation (B.4) by means of a least squares approximation.
In mathematical terms,
![]() |
(B.5) |
![]() |
(B.7) |
The two mixed terms are identical. The minimum is found at the zero of the
derivative with respect to
,
![]() |
(B.8) |
Therefore, the minimizing vector
is a solution of the normal equation
where
denotes the transposed form of
, and
equation (B.9) corresponds to a system of linear equations. The
matrix
on the left hand side is a square matrix, which
is invertible, if
has full column rank (that is, if the rank of
is
). In that case, the solution of the system of linear equations is unique
and given by
![]() |
(B.10) |
The matrix
is called a pseudo
inverse of
, since the true inverse of
(that is
)
does not exist as
is not a square matrix (
).