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For most of the time, this is due to me being lazy (and mathematically unapt)
and not coding a more efficient
maxent algorithm. In the rest of the cases, it is either your data, or that the decision made
by the program about the *F*_{000} value was completely wrong (see section 8.1).
Now, if the problem is the value of *F*_{000} being too small, you should be seeing an
unjustifiably sharp map. If that is the case, stop the program, edit the
` MAXENT_AUTO.IN` file, add a line with the ` F000` keyword (see 7.1.4), and
re-run the program with ` GraphEnt MAXENT_AUTO.IN`.

If you are calculating a * 3D isomorphous difference Patterson function* you can
improve the convergence properties (hopefully without loosing much of the signal) by
giving ` LIMIT 1.0` or ` LIMIT 2.0` in the ` MAXENT_AUTO.IN` file and re-running
the program (see discussion in section 7.3.9).

If you are calculating a native Patterson function, see page .

You can get problems with slow convergence even when you calculate something as
simple as a two-dimensional projection. To my experience, slow convergence indicates the
presence of some signal (to take the extreme view, if your data are consistent
with a uniform map, * GraphEnt* will stop immediately), but this is
not necessarily the signal you expect, or wish to have^{19}.
In other cases, it indicates the
presence of outliers in your data (which makes it difficult to find a solution that
satisfies the constraints they impose). This last case is easily identified from the
contributions to table (and the normal probability plot in the case of
difference Patterson functions).

#### Footnotes

- ... have
^{19}
- For example,
you may have measured very good quality native and derivative
data, but if they are not isomorphous,
* GraphEnt* will be fitting their (very accurately)
measured differences, the only trouble being that the result will appear to be just
noise to us.

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NMG, Nov 2002