Convex.jl defines an abstract type AbstractExpr, from which all important concrete types in the package are descended. We've defined a bunch of functions that allow users to treat AbstractExprs as though they were matrices: indexing, hcat, vcat, multiplication, addition, subtraction, etc. Can we use these to automatically get more advanced functions, like (for example) kron?
The code for kron <https://github.com/JuliaLang/julia/blob/master/base/linalg/dense.jl#L134> uses only indexing and multiplication operators. But I'm not sure how to make AbstractExpr (and its subtypes) subtype AbstractMatrix{Float64} or subtype AbstractArray{Float64,2} to make this transition seamless. Simply defining abstract AbstractExpr <: AbstractArray{Float64,2} results in a method error: (Variable is defined as a subtype of AbstractExpr) julia> kron(A,B) ERROR: MethodError: `kron` has no method matching kron(::Array{Float64,2}, ::Convex.Variable) Closest candidates are: kron(::Any, ::Any, ::Any, ::Any...) kron{T,S}(::Array{T,2}, ::Array{S,2}) kron(::Union{Array{T,1},Array{T,2}}, ::Number) 1) Is it possible to have Convex's AbstractExpr inherit from AbstractArray? What other methods would we need to implement to make this work? 2) Is it a terrible idea, for reasons I haven't thought of? Thanks! Madeleine
