Are you suggesting a clarification in the documentation or new
functionality? The docs clearly explain when ForwardDiff is used and what
the performance drawbacks are. If you know of a package in Julia which we
could use to perform reverse mode AD on user-defined functions, we'll
gladly accept
I suggest clarification in the documents regarding which mode of automatic
differentiation since this can have a large impact on computation time.
It seems like this 'ForwardDiff is only used for used-defined functions
with the autodiff=true option. ReverseDiffSparse is used for all other
On Wednesday, March 9, 2016 at 12:52:38 AM UTC-5, Evan Fields wrote:
>
> Great to hear. Two minor questions which aren't clear (to me) from the
> documentation:
> - Once a user defined function has been defined and registered, can it be
> incorporated into NL expressions via @defNLExpr?
>
Yes.
I was also recently seeing NLopt hang during some of JuMP's tests, so I
don't think it's just you.
On Tuesday, March 8, 2016 at 9:52:38 PM UTC-8, Evan Fields wrote:
>
> Great to hear. Two minor questions which aren't clear (to me) from the
> documentation:
> - Once a user defined function has
Great to hear. Two minor questions which aren't clear (to me) from the
documentation:
- Once a user defined function has been defined and registered, can it be
incorporated into NL expressions via @defNLExpr?
- The documentation references both ForwardDiff.jl and
ReverseDiffSparse.jl. Which is
This is a bug, I've opened an issue
here: https://github.com/JuliaOpt/JuMP.jl/issues/695
As a workaround, if you replace sqrt(y0) with 0.0 then the NaNs go away.
Clearly it shouldn't affect the result since y0 is a constant.
On Tuesday, March 8, 2016 at 2:46:12 AM UTC-5, JP Dussault wrote:
>
>
a review of the REQUIRE sets for each package gives some high level sense
of the differences:
https://github.com/JuliaOpt/JuMP.jl/blob/master/REQUIRE
https://github.com/JuliaOpt/Convex.jl/blob/master/REQUIRE
enjoy !!!
cdm
Very cool that you added user-defined functions (and AD). Congrats on the
new version.
On Saturday, February 27, 2016 at 11:14:16 PM UTC+1, Miles Lubin wrote:
>
> The JuMP team is happy to announce the release of JuMP 0.12.
>
> This release features a complete rewrite of JuMP's automatic
>
