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- Apr 02 22:37GiggleLiu commented #200
- Apr 02 22:27thisrod commented #775
- Apr 02 22:26thisrod commented #775
- Apr 02 21:03pkofod commented #775
- Apr 02 09:03pkofod closed #737
- Apr 02 09:03
pkofod on master

closes #737 by offering only_* … (compare)

- Apr 02 09:03pkofod closed #742
- Apr 02 09:03pkofod closed #738
- Apr 02 09:02pkofod commented #742
- Apr 02 09:02codecov[bot] commented #742
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- Apr 02 07:06
github-actions[bot] on v0.20.6

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?
Just to provide a bit more information: what I'm actually doing is minimizing an energy defined over closed curves in the plane. The way I handle curves numerically is to store a list of points and assume the curve is a polygon whose vertices are the stored points. I already implemented functions to compute the gradient of my energy, and conducted some experiments by coding my own gradient descent loop. Everything behaves nicely provided that after each gradient step I somehow resample points along my curve to ensure they are regularly spaced (with respect to the curvilinear abscissa). That's the step I would be able to perform using Optim.jl's gradient descent (resampling points along the curve, i.e. modifying the state vector right after a gradient step). Sorry for posting such a long message!