Tangi Migot

@geoffroyleconte I think you did some tests regarding this, do you think we could reuse that?

Thanks @geoffroyleconte for the feedback! The last release of NLPModelsModifiers, 0.7.2, should have a SlackModel GPU-compatible, if you can check? Adding unit tests for this seems even more a good...

Note guys that we are still at the beginning of this move and only at stage 1-2 of this workflow: https://cuda.juliagpu.org/stable/usage/workflow/#UsageWorkflowScalar so it is to be expected that we still...

@geoffroyleconte I noticed you made a tutorial on the jso-docs website https://jso-docs.github.io/introduction-to-quadraticmodels/ Is it possible to make a link to the tutorial in the doc of this package? Thanks!

Hi @kiranshila ! Thanks for the suggestion. Tbh, I haven't completely followed last updates of Enzyme. Currently, we only have the reversed gradient, but for sure it would be great...

In general, in ADNLPModels we are interested in computing derivatives of: - f(x): 1st and 2nd order derivative. - Directional derivatives of `f`, i.e. something like `ForwardDiff.derivative(t -> ForwardDiff.gradient(f, x...

Hey @gdalle @adrhill ! I love the idea and agree with @amontoison the sparse Jacobian/Hessian is a good starting point. For the DifferentiationInterface.jl, the best would be if you could...

Hi @amontoison @gdalle ! Thanks for adding this new feature. Can you make it that we don't remove the Symbolics-way of computing the sparse matrices? I am fine having both...

> @tmigot What do you want to keep Symbolics.jl? Why not? I don't think that the debate is 100% clear that the SparseConnectivityTracer is better than Symbolics, and it doesn't...

Hi guys! Thanks @gdalle and @amontoison for this. Why did you end up droping Symbolics in the same PR? Have you guys run some performance benchmark?