Comments (6)
That's an interesting direction... From an ECOS point of view there shouldn't be a fundamental issue with higher precision types, but I am not sure about the linear solver under the hood (SparseLDL). All code is there so you might give it a try and see where it fails? :)
Just curious, from which field is the optimization problem requiring more than double precision arithmetics?
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Alright, I will give it a shot :)
Essentially, I am trying to optimize the coefficients $ \boldsymbol \alpha \in \mathbb R^{N+1} $ of a complex polynom
$$ \sum_{i=0}^N \alpha_i z^i = \sum_{i=0}^N \frac{\widetilde \alpha_i}{i!} z^i , \quad z \in \mathbb C $$
where $ \vert z^i \vert \sim \mathcal O (10)$. Already for $N \approx 20 $ I run into stability issues since the
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Switching from double
to long double
is actually not that difficult, it can be done with moderate changes. However, some adjustments have to be made, i.e., fabs
becomes fabsl
and printing requires usually also an "L", e.g. %f
becomes %fL
.
Thus, a fully type-generic version would demand some more effort.
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Ok great to hear, so did it run with long double after those changes and did you see an improvement on numerical stability?
from ecos.
I called ECOS
through the Julia interface where there is, unfortunately, within the Julia MOI no support for long double
or other types (like Double64
) but only for the standard C double
.
I will now take a look at the Matlab & Python interfaces.
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Based on the EiCOS
project which allows fairly easy specification of constraints I forked a long double
version.
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