R help - Nov 2018 - Optimization with Parallel Processing Functions/Packages

More of a general query, but looking to see if others have successfully used
something like the foreach package (or other parallel style functions) with
certain functions that minimize likelihood or objective functions (e.g.,
optim/nlminb).

I have had great success with embarrassingly parallel problems and my R packages
have benefited greatly. However, optimization doesn't fit as nicely within
that context as the values at iteration t depend on the values found at
iteration t-1 and such. So, I'm assuming the cost of splitting and combining
might be more expensive in this context that simply doing minimization on a
single core.

If others have experiences or even possibly R-specific resources that implement
this that I would be able to study, I would appreciate seeing how this might be
implemented.

Regards
Harold

This is highly problem dependent... and you appear to already know the answer.
Note that some differential evolution solution approaches may benefit from
parallelizing evaluation of generations since within that sub-problem the
optimization dependencies don't apply.

A theoretical discussion forum such as stats.stackexchange.com might be better
for this... though even they may complain that the question is too vague.

On November 7, 2018 10:24:33 AM PST, "Doran, Harold" <HDoran at
air.org> wrote:
>More of a general query, but looking to see if others have successfully
>used something like the foreach package (or other parallel style
>functions) with certain functions that minimize likelihood or objective
>functions (e.g., optim/nlminb).
>
>I have had great success with embarrassingly parallel problems and my R
>packages have benefited greatly. However, optimization doesn't fit as
>nicely within that context as the values at iteration t depend on the
>values found at iteration t-1 and such. So, I'm assuming the cost of
>splitting and combining might be more expensive in this context that
>simply doing minimization on a single core.
>
>If others have experiences or even possibly R-specific resources that
>implement this that I would be able to study, I would appreciate seeing
>how this might be implemented.
>
>Regards
>Harold
>
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-- 
Sent from my phone. Please excuse my brevity.