Model fitting by least squares |

(107) |

It is worth noticing that the residual updating (109) in a nonlinear application is the same as that in a linear application (55). If you make a large step , however, the new residual is different from that expected by (109). Thus, you should always re-evaluate the residual vector at the new location, and if you are reasonably cautious, you should be sure the residual norm has actually decreased before you accept a large step.

The pathway of inversion with physical nonlinearity
is well developed in the academic literature,
and Bill **Symes** at Rice University has a particularly active group.

There are occasions to change the weighting function during model fitting.
Then,
one simply restarts the calculation from the current model.
In the code,
you would flag a restart with the expression `forget=true`.

Model fitting by least squares |

2014-12-01