[ADMB Users] When and how to use set_scalefactor()?

[Shanae Allen - NOAA Affiliate]

Hi ADMB users!

I'm trying to understand when and how to scale parameters. The likelihood
surface for the problem I'm working on is a narrow, curved valley (when
varying the two most sensitive parameters), leading to high gradient
components and non positive definite Hessians for some simulated data sets
and starting values. According to Nocedal and Wright (1999), when the
likelihood function is highly sensitive to a given parameter (x), you
should set the scaling factor (d) to some large number such that a new
parameter z is equal to x/d.

I'd like to use the built-in function in ADMB, set_scalefactor(s), but I'm
unsure of how it treats the scaling factor. The ADMB site (
http://www.admb-project.org/examples/function-minimization/parameter-scaling)
suggests the new parameter z is equal to s*x, thus s=1/d. However, for my
problem I obtain much better convergence when s is large (thus d is close
to zero), which makes me wonder how ADMB is treating the scaling factor and
whether I'm using it appropriately. Any tips would be greatly appreciated!

Thanks!
Shanae Allen



Nocedal, J., & Wright, S. (1999). Numerical optimization. New York:
Springer.

-- 
Shanae Allen-Moran
National Marine Fisheries Service
110 Shaffer Rd.
Santa Cruz, CA 95060
Phone: (831) 420-3970
Email: shanae.allen at noaa.gov
Website: http://swfsc.noaa.gov/SalmonAssessment/
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