convex-optimization

Currently one cannot just do -SomeLoss(). Of course it could be hacked by doing (-1) * SomeLoss(). We want to implement the first syntax via __sub__.

Now that Convex.jl is backed by MOI, we can take advantage of its ability to write to file formats (https://jump.dev/MathOptInterface.jl/stable/apimanual/#File-formats-1). It's actually pretty easy to do this already, e.g.

using Convex, COSMO
x = HermitianSemidefinite(2)
p = minimize( real(tr(x)), tr(x * [1.0 im; -im 0]) == 0, x[1,1] == 1)
solve!(p, COSMO.Optimizer)

using MathOp