Linear constraint optimisation on GPU

I currently use JuMP and Ipopt to solve Linear Constraints Optimisation problems, primarily with linear equality and inequality constraints. I would like to either solve many of these problems in parallel, or sequentially but with GPU acceleration. Are there any packages that would allow this kind of computation?

Not a specialist of mathematical optimization but I would guess that most of underlying algorithms are strongly divergent (branching all the time) which is not a good fit for GPUs.

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SCS has GPU support: GitHub - jump-dev/SCS.jl: Julia Wrapper for SCS (https://github.com/cvxgrp/scs)

If you’re using Ipopt, you should try a different linear solver: GitHub - jump-dev/Ipopt.jl: Julia interface to the Ipopt nonlinear solver

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Check GitHub - sshin23/MadNLP.jl: A solver for nonlinear programming with cuSOLVER.

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Thank you very much for the suggestion. I was wrong, I was actually using Tulip, which is reasonably fast for solving one problem at a time, but not at scale.

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Thank you! this is what I will probably try first, since it interfaces with JuMP :slight_smile:. There does not appear to be a lot of documentation as to how to get the cuSolver extension however?