In article <11**********************@f14g2000cwb.googlegroups .com>,
<we******@gmail.com> wrote:
A lot of people posting here, are doing their best to discourage you
from attempting to optimize for some reason. Indeed if you feel you
are not able to balance multiple constraints on code development, and
need to make coding as simple a problem as possible, then optimization
may not be for you. I'm not sure why these other posters automatically
assume that you are not capable of putting more constraints on your
coding, but if they are right and you are a low skilled programmer,
then indeed, just stay away from optimization.
I do not feel that that is a fair representation of what the other
posters such as myself have been saying.
What we have been saying is not really "Don't optimize", but rather
closer to "Don't set out to -write- an 'optimized' program: write
a good program instead and then fine-tune it."
Learn from my mistakes: don't optimize so much. Especially on
science programs, I have a tendancy to spend months on figuring
out better ways of doing things, boundary conditions within which
one can use simpler algorithms, and so on. And then only a few people
end up using the program, and the value of the time they save is
a fraction of the value of the time that I expended in my quest
for perfection. Cheaper for my bosses to buy a faster machine than
to have me make the program 5, 10, or 100 times faster.
RealPolitics: cleaning up the obvious GUI bugs is more appreciated
than speeding up the code, and cleaning up the more common code crashes
is generally more appreciated than getting the "right" answer,
particularily if no-one knows what the "right" answer -is-.
(Oh sure, if you polled the scientists about whether some minor
cosmetic fixes were more important than reducing the calculation error
by 10%, they would say the calculation error comes first -- but
you'll get 20+ complaining "The label on the graph is too close
to the axes" for each one that complains that "In this dataset,
the error bounds could be greatly improved.")
--
"Mathematics? I speak it like a native." -- Spike Milligan