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

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.

未指定任何文件或测试。首先检查当前的 MatrixOptInterface.jl 和 MathOptInterface 构造接口，然后将其与所描述的 A、b 和 c 矩阵工作流进行比较；对于大型 LP/MILP 模型，如果有记录完善的受支持工作流，或明确说明了限制，则可视为完成。

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