Oscar Dowson

Closing in favor of https://github.com/jump-dev/Convex.jl/issues/297 since this is really the same thing: add various power cone atoms.

This is because of a fairly deep design decision in Convex that I don't think we'll be resolving anytime soon. It would likely require a substantial rewrite. The issue is...

Now that https://github.com/jump-dev/Convex.jl/pull/590 is merged, this issue can be resolved by adding support for atoms that create the various `GenericConstraint{MOI.PowerCone}` constraints. See also https://github.com/jump-dev/Convex.jl/issues/90

So there's a path forward here: If the objective is a quadratic atom, then instead of calling `conic_form!` on it, we could convert into a `ScalarQuadraticFunction` if supported. Here's the...

Closing because this appears to be fixed: ```julia julia> using Convex julia> A = [ 0.47213595 0.11469794+0.48586827im; 0.11469794-0.48586827im 0.52786405] 2×2 Matrix{ComplexF64}: 0.472136+0.0im 0.114698+0.485868im 0.114698-0.485868im 0.527864+0.0im julia> B = ComplexVariable(2, 2)...

Now that https://github.com/jump-dev/Convex.jl/pull/590 is merged, this issue can be resolved by adding a `logdet` and `rootdet` atom that map to [`GenericConstraint`](https://github.com/jump-dev/Convex.jl/blob/master/src/constraints/GenericConstraint.jl)s with `MOI.LogDetConeSquare` and `MOI.RootDetConeSquare`.

I'm going to close. I've been adding tests https://github.com/jump-dev/Convex.jl/pull/577 and I've found no issues. We could always do more, and we've significantly refactored how we get duals since the original...

No pointers on where to start. I think this is pretty difficult to do at the moment because Convex doesn't hook directly into MathOptInterface, so we can't pass `MOI.GeometricCone`. So...

> it is definitely quite involved for a beginner like me It sure is :smile:

> but I still have a soft corner for DCP and Convex.jl We have a goal to slowly move JuMP towards a DCP interface.