Cool:)
No idea what this paper is about, but great that they actually cited Optim
Nice to see that it's being used:) maybe we should add some text in the reader and ask people who use optim for research to give us some feedback on what they used, why, and how they found it?
(at least in the beginning before it becomes too popular to handle the traffic ;) )
oh, it's autocorrect for readme :)
Re https://discourse.julialang.org/t/optimize-performance-comparison-optim-jl-vs-scipy/12588/8 I remember we discussed changing the default linesearch to backtracking but can't find an issue for this, do I remember wrong?
think it wqas just in this channel
Asbjørn made a pr but closed it again
@cortner and @ChrisRackauckas ; I don't know if the papers on deflation by Patrick Farrell mention his code, but in case you're interested in playing with an existing implementation for producing the bifurcation diagrams with his method you can check out https://bitbucket.org/pefarrell/defcon
Can't look unless it's actually open sourced
Damn GPL
Is LGPL equality problematic?
Interesting to see Robert Kirky on the contributors list
Do you know him @pkofod ?
To link
But looking at the source still has a derived work clause
I was part of a panel discussion on open source software development
He has a project called "VFI toolkit" that he was talking about. It's a Matlab project, so there was some discussion about being able to call a matlab toolbox open source :)
(I'm currently on leave working for an open source economics related project [in python], and the PI on that project was also part of the panel, and the one talking against a matlab toolbox as being meaningfully free and open source)
@pkofod yeah, the reason ishttps://github.com/JuliaNLSolvers/Optim.jl/blob/master/test/runtests.jl#L240
So the version must be updated to 1.0
We set it up like that to copy the approach by @fredrikekre and @KristofferC in Literate.jl and JuAFEM.jl
But I see that they have now moved to a new approach using the new Julia 1.0 Projects
(actually, maybe it will take more objective evals but fewer iterations :p )
Hello all, I'd like to implement a projected gradient descent algorithm with Optim, where, after a gradient step, the state vector is modified to fit some constraint. I see that there are some reference that this is possible in the Manifold optimization section of the docs, but don't understand how to implement the
retract! function. If the current state is x, and I have a function y = project(x) that I want to use to project the state, how would that work?
