I’m solving a linear optimization problem with JuMP (0.19.2) using GLPK.jl (0.10.0) as the solver. I’m struggling with the situation where the lower and upper bounds of a variable are equal (and because it’s in a vector of inequalities, I can’t just simply remove the variable). Here is a MWE:
using JuMP #Call the modeling language using GLPK # Call the solver probm = Model(with_optimizer(GLPK.Optimizer,)) @variable(probm, x[1:2]) xmax = [10., 0.] @constraint(probm, 0. .<= x .<= xmax) @objective(probm, Min, 0) optimize!(probm)
The output is:
glp_simplex: row 2: lb = 0, ub = 0; incorrect bounds
I found related discussion on GLPK mailing list, and a slightly similar thread here but with Ipopt. However, I wonder if it’s possible to use GLPK here (with Clp it works). Maybe it’s possible to switch to using GLPK’s interior point solver, but I don’t know how. And maybe someone would have a better trick! Thanks for your help!