[SciPy-User] optimize.fmin_cg terminates when w - grad*1e-10 yields lower obj & grad
Mon Oct 5 11:56:27 CDT 2009
- "Warning: Desired error not necessarily achieved due to precision loss"
- I'm passing objective (obj) and gradient (grad)
- I checked that obj and grad are correct using my python equivalent of
- I have the same problem whether I use norm=2 or no norm argument
- Termination objective and 2-norm of grad are 2.484517e+06, 2.644732e+07
- Subtracting grad*1e-10 to parameter vector yields 2.417658e+06,
2.413900e+07 obj and 2-norm of grad, respectively
I did an implementation of CG in matlab/octave a few years ago and realize
that the problem could be as simple as me needing to set a different epsilon
value or some such. Any suggestions? Nothing jumped out at me when I gave
a careful read to the argument list and glanced over the code, but I could
easily be missing something. My current call:
wopt = scipy.optimize.fmin_cg(f = ser.obj, fprime = ser.grad, x0 = w0, norm
= 2, callback = cb)
OTOH, is it possible that fmin_cg needs additional tuning? I don't have
much understanding of how solid the fmin_cg code is. Has it seen tons of
use/testing, or is it relatively fresh code?
FYI, I'm using 0.7.0---the version that comes with the current Ubuntu. My
parameter vector is length 12; I have ~50 data points. I've seen CG work
quite nicely on data of a million dimensions...
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