Hi all, I’m refactoring some old R code into Julia. In R I was using constrOptim: Linearly Constrained Optimization which lets us use constraints like U \theta - c \geq 0, where \theta is our parameters to be optimized, U is a matrix and c is a vector. The objective function involves an ODE solve within it, so I’m not able to use JuMP to specify the model structure. I see Optim.jl has Interior point Newton · Optim but it requires a Hessian evaluation as well. Are there any adaptive barrier methods available that would let me use something like BFGS but including linear equality/inequality constraints conveniently without the JuMP modeling language?
1 Like
There are lots of libraries out there for nonconvex local optimization with inequality constraints that only require you to supply gradients, and don’t require you to use JuMP. For example, Ipopt.jl, NLopt.jl, Nonconvex.jl, and more…
(They don’t necessarily work by the specific barrier algorithm you mentioned, but do you care?)
1 Like