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LBFGS approximation of the inverse Hessian #39
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I am running into a similar issue. I think we could build something directly in the Hessian callback by wrapping the LBFGS operator implemented in JuliaSmoothOptimizers: |
Thanks for bringing this up @mohamed82008 @frapac. I agree that MadNLP should have a quasi-Newton option. The linear operator implementation for LBFGS in JSO looks great; maybe we can use this. It seems to me that this may take a good amount of time though. I'll try to implement this over the summer but can't promise anything yet. I'll keep this issue open and update the progress here. |
LBFGS has been implemented in this PR: #221
This is similar (with slight differences) to what is currently implemented in Ipopt. |
Solved by #221 |
Been admiring this work from a distance. Glad it finally made it in 🎉 |
Thank you for your support! We would love seeing MadNLP integrated in NonConvex.jl in the medium term :) |
Maybe I'm missing something but, should it means that user-defined function with JuMP + MadNLP should work now ? I'm still getting the Related to : #115 edit : I'm not sure that you still receive notification after the issue is closed so : @frapac @sshin23 Thanks |
@francis-gagnon indeed, I haven't received your comment before. MadNLP is currently not supporting user-defined operators inside MOI. This is resolved with this new PR: #322 |
Hi, thanks for this fantastic package. I have a question. I couldn't see any way to not pass the Hessian in and just rely on an LBFGS approximation of the (inverse) Hessian. Is there a way to do this in MadNLP now? If not, is it too much work to add?
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