Hello, I trying to optimize using JuMP with GLPK. The model has the vectors of variables:
y is a binary vector. Each y[ j ] represents when thefeature is actived or deactived.
x is float vector. Each variable x[ j ] is the weight thatwe associate to a feature y[ j ].
the variable are defined in Julia in this way:
@variable(model, y[1:73], Bin)
and I have this constraint that I’m trying to implement as following:
for i in 1:73
@constraint(model, -y[i] <= x[i] <= y[i] )
I’ve tried some approaches to implement this constraint but I did not get it yet.