Regression.jl does not aim to replace or provide an alternative to Optim.jl. It is primarily to do regression, and optimization algorithms are encapsulated as details.
However, there are certain aspects in Optim.jl that make it not very suitable for Regression.jl at this point. For example, we need to work with 2D/3D solutions directly, and need the support of proximal operator etc. Also, it is desirable to work with functors instead of Functions (which comes with less overhead). I think eventually some of the design in Regression.jl can be merged into Optim.jl, and Regression.jl can be made into a package that build on top of both EmpiricalRisks.jl and Optim.jl. I may open an issue at Optim.jl to propose some refactoring to begin with. Dahua
