On Thu, 18 Jul 2013 11:47:03 -0400, Konstantin Berlin wrote:
I appreciate the comment. I would like to help, but currently my
schedule is full. Maybe towards the end of the year.
I think the first approach should be do no harm. The optimization
package keeps getting refactored every few months without much
thinking involved. We had the discuss previously, with Gilles
unilaterally deciding on the current tree, which he now wants to
change again.
As I said,
as Luc said,
as Phil said,
again and again and again,
we are not optimization (as a scientific field) experts here,
but we do use Commons Math in scientific code that is pretty compute
intensive (and yes, maybe not in the same sense as you'd like it to
be for your comfort).
Current code has, and may still have problems, but we see them only
through running unit tests, running our applications, running
code examples submitted by issue reporters.
We improve what we can, given time and motivation constraints.
Other than that, there is nothing.
Yes, we already had that asymmetrical conversation where _you_ declare
what _we_ should do.
As someone who uses optimization regular I would say the current API
state (not just package naming) leaves a lot to be desired, and is
not
amenable to the various modification that people might need for
larger
problems. So if you are going to modify it, you should at least open
up the API to the possibility that different optimization steps can
be
done using various techniques, depending on the problem.
We should also accept that not everything can fit neatly into a
package tree and a single set of APIs. A good example is least
squares. Linear least squares does not require an initial guess at a
solution, and by performing decomposition ahead of time you can
quickly recompute the solution given different input values. However,
an iterative least squares method might not have these properties.
There are probably countless of other examples.
Because optimization problems are really computationally hard all the
little specific differences matter, that is why Gilles approach of
sweeping everything under the rug and into some rigid not thought out
hierarchical API forces these methods to adapt (or drop) numerical
aspects that should not be there (e.x. polynomial fits). This has
*huge* performance implications, but the issue is treated as some OO
design 101 class, with the focus on how to force everything into a
simple inheritance structure, numerics be damned.
I would gladly help with the feedback when I can. Ajo and I provided
code for adaptive integration, yet the whole issue was completely
ignored. So I am not sure how much effort is required for the
developers to take an idea or mostly completed code and make a
change,
rather than reject even the most basic numerical approaches that are
taught in introduction classes as something that needs to be
benchmarked.
As usual, you are mixing everything, from algorithms to
implementations,
from proposing new features to denigrating existing ones (with
non-existent or inappropriate use-cases), from numerical to efficiency
considerations...
[On top of it, you blatantly affirm that this issue has been ignored,
even as I provided[1] an analysis[2] of what was actually happening.
People like you seem to ignore that we work benevolently on this
project!]
Not even speaking of derogatory remarks like "sweeping [...] under the
rug"
and "not thought out" and insinuating that everything was better and
more
efficient before. Which is simply not true.
It's an asymmetrical discussion because you declare that half-baked
code is good enough and _we_ have to work even more than if we'd have
to implement the feature from scratch.
Gilles
[1] In the spare time I do _not_ have either.
[2] Which dragged me to the implementation of the Gauss-Hermite
quadrature
scheme (although I had no personal use of it), which seems to be
the
appropriate way to deal with the improper integral reported in the
issue which you refer to.
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