Typically, the number of frequency constraints is much greater than
the number of design variables (filter coefficients). In these cases, we have
an *overdetermined* system of equations (more equations than
unknowns). Therefore, we cannot generally satisfy all the equations,
and are left with minimizing some error criterion to find the
``optimal compromise'' solution.

In the case of least-squares approximation, we are minimizing the
*Euclidean distance*, which suggests the geometrical
interpretation shown in Fig.4.19.

Thus, the desired vector
is the vector sum of its
best least-squares approximation
plus an orthogonal error
:

(5.42) |

In practice, the least-squares solution can be found by minimizing the sum of squared errors:

(5.43) |

Figure 4.19 suggests that the error vector is

(5.44) |

This is how the

(5.45) |

In matlab, it is numerically superior to use ``

We will return to least-squares optimality in §5.7.1 for the purpose of estimating the parameters of sinusoidal peaks in spectra.

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