<html>
<body>
<font size=3>Dear Saang-Yoon <br><br>
As usual I replied all to your message so it would go immediately to you
and eventually to the list after moderation. But the list one
bounced because you sent to google groups and apparently I am not
subscribed to that version of users but I am to the list at
admb-project.org. I think the google list is archaic and forwards
to the other. Johnnoel is this something to be cleaned
up?<br><br>
You are asking a very generic question related to numerical searches for
best fitting parameters that is not really specific to admb. The
result you are seeing reflects something about your specific model and
data and specifics of how you did the search. The basic results you
seem to be reporting suggest that the admb program thinks you have found
a local minimum in the sense that the gradient is nominally zero
(absolute values all below a theshold) and estimated Hessian is positive
definite. If starting with different starting values leads to
different numerical answers there seem to be two possibilities.
First that for your problem there really are multiple local minima or
second you really are not at a local minima just something that
numerically appears to be and each time you do a search you end up at a
different false minima. The simplest thing you could try is
to make the criterion for gradients to be assumed to be zero more
stringent and see if the problem goes away (check manual on how to
change default gradient values). It could be the case the problem
does not go away even if you make the criterion so small that it takes
very long time (many iterations to meet). Then you may still have
either of the problems but there would seem to be a problem with your
model given the data that are available. It could be that the model
is poorly parameterized (scale of parameters very different from one
another or parameters highly correlated) or it could be still that there
are really local minima. It is hard to give any specific advice
without actually seeing code and data. I suspect that there are a
number of people on the list who would jump in with suggestions if you
provided that.<br><br>
Another thing you could do to try to diagnose what is going on is to take
a look at the different solutions you are getting and compare your
objective functions at the solutions. If you are getting almost
identical values for the objective function but the parameters are
varying enough that you are concerned this suggests there really is a
model problem in the sense that there really does not appear to be a
unique minimum to the objective function.<br><br>
Hope this helps <br><br>
At 07:23 PM 6/15/2012, Saang-Yoon wrote:<br>
<blockquote type=cite class=cite cite="">Hi.<br>
With small changes in initial guess values of parameters, different
results are generated. Interestingly different results are satisfactory
in terms of Hessian matrix calculation, and stability (i.e., maximum
gradient = almost zero). Although a difference between two results
are small, the episode indicates a failure to regenerate the same
results. Given the same data, the same results must be
generated. Also it is not good that the results are *very*
sensitive to initial guess values. Appreciated is any
suggestion/advice about how to avoid the problem?<br>
Saang-Yoon <br>
_______________________________________________<br>
Users mailing list<br>
Users@admb-project.org<br>
<a href="http://lists.admb-project.org/mailman/listinfo/users" eudora="autourl">http://lists.admb-project.org/mailman/listinfo/users</a></blockquote>
<x-sigsep><p></x-sigsep>
Jim Bence<br>
Dept. of Fisheries and Wildlife <br>
Michigan State University<br>
<a href="http://www.msu.edu/user/bence/" eudora="autourl">http://www.msu.edu/user/bence/</a>
</font></body>
</html>