On Aug 16, 2010, at 12:50 PM, Greg Nicholas wrote:
It is often useful for diagnostic or tracking purposes to print out
information about the progress of an optimization at each
iteration. Some libraries (e.g. pylab.optimize) allow for a
callback function to be defined for that should handle any per-
iteration processing like this.
For some simpler algorithms, putting this code in the objective
function makes sense, but for others that might make many
"exploratory" calls to the objective function without taking an
actual step (e.g. gradient algorithms), this will produce a large
amount of undesired noise that is often indistinguishable from the
desired output.
Are there any work-arounds that I'm unaware of, and if not, any
plans for this kind of functionality in the future?
If the purpose is just to track whether the optimization is making
progress, and how fast, just using the objective function for this
purpose seems reasonable enough to me. After all, if you are tracking
progress, you probably want to know if the algorithm is just cycling
around some point for a long time without making much progress. And
if you want to filter it so that you only show points that improve
upon the objective, it is easy for the user to do that herself.
Can you more precisely define a situation in which the objective
function itself does not have enough information to do the desired
processing?
The only thing I can think of is if you have nonlinear constraints,
and you want to know the sequence of "best so far" points (which count
only feasible points, or points that at least improve feasibility), in
which case you would have to keep track of what both the objective
function and the constraints returned, which would be somewhat
annoying. I can certainly add another callback function that is
called for each "best so far" point. But this is different from the
"iteration" metric that you suggest (to the extent that "iterations"
is at all a well-defined concept).
Steven
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