[ADMB Users] non-convergence and "inner maxg = 0"

Ian Fiske ianfiske at gmail.com
Thu Mar 18 21:00:00 PDT 2010

Dear ADMBers,

Thank you for freely providing such an amazing piece of software.  I'm super
excited about finally having access to all of ADMB's power and flexibility
and hope to eventually incorporate it into my R package, unmarked, to
replace R's optim as the engine.  Now, my question...

First, I fit a fairly complex model in ADMB and it works great.  Next, I
tried to add random effects and use ADMB-RE, but ADMB-RE appears to not
converge.  The oddity is that when the program prints out (via cout
statements) the parameters, they appear to stabilize to the same values as
in the non-RE version.  And (omitting the parms), the program prints

... [I think phase 2 starts here] ...

  Inner second time = 5.48908e+30  Inner f = -3.14161e+61

Newton raphson 1

 inner maxg = 5.48908e+28

 inner maxg = 0

 inner maxg = 0

 inner maxg = 0

 inner maxg = 0

which continues seemingly indefinitely (at least several minutes), printing
"inner maxg = 0".  If the inner maximum gradient is 0, then why isn't
another outer step taken or convergence declared?  Instead, it looks as if
the inner optimization just repeats indefinitely.  That is, I never get to
Newton raphson 2.

I appreciate any ideas as to what is going on here or why the optimizer is
stuck with maxg = 0.  I'm using ADMB 9.1 on Ubuntu 9.10, 64bit.  Let me know
if you could use any more details regarding the model or my system.


Ian Fiske
PhD Candidate
Department of Statistics
North Carolina State University
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