Support for matrix style model construction (MathProgBase/LinearQuadratic style) in modern Julia + MOI

[lpaizs]

I’m reaching out about support or example workflows for constructing optimization models from raw matrix data in modern Julia, similar to how it was done with MathProgBase’s LinearQuadratic interface.
A bit of background on my situation:
• Years ago I implemented a large scale unit commitment MILP in Julia 0.6.4, using MathProgBase.jl and specifically the LinearQuadratic models interface, together with Gurobi as the solver.
• The model is truly large: on the order of ~2 million variables and constraints. I built the model from big matrices and vectors (A, b, c, etc.) in a Matlab style fashion and handed that directly to the solver.
Now I’d like to move the codebase to a current Julia version, but without changing the overall program structure—i.e., keeping a MathProgBase / LinearQuadratic style formulation rather than rewriting everything in JuMP or another high level modeling layer.
Question / request:
Is there any officially supported or recommended way in the current Julia / MOI ecosystem to construct a large LP/MILP directly from matrices and pass it to a solver, similar to the old MathProgBase LinearQuadratic approach?
Thanks for any pointers or suggestions. Even a small example or outline of how to plug matrix data into MOI, or a note on current limitations, would be very valuable.

[blegat]

We've been advising people to just go with @constraint(model, A * x <= b) and check if that's really a performance issue. We also developed this replacement for that specific use case of MathProgBase in this package: https://github.com/jump-dev/MatrixOptInterface.jl. Let us know if you get into any trouble with it.

[lpaizs]

Hi, thanks so much for the quick reply!

Really appreciate the pointer. I’ll give MatrixOptInterface.jl a try and see how it works with my big model.