I am new to programming, so I hope that the description of my probleme is accurate:

Based on knapsack, I used JuMP and GLPK to come up with a solution for the unbounded knapsack problem.:

```
function unbounded_knapsack(validparam::Vector::{Float64})
weight = [validparam[i]^2 for i in 1:length(validparam)]
model = Model(GLPK.Optimizer)
@variable(model, x[1:(length(validparam))],Int)
@objective(model, Max, weight' * x)
@constraint(model, paramlist'*x == 20)
@constraint(model,x.>=0)
Jump.optimize!(model)
unbounded_knapsack([1.0,2.0,3.0,4.0,5.0])
```

The Function returns the Vector [0.0,0.0,0.0,0.0,4.0].

For my purpose the code works fine.

Now I want to have additional constraints:

```
@constraint(model,paramlist'*x==20)
@constraint(model,paramlist'*x==30)
@constraint(model,paramlist'*x==40)
...
```

I am getting this error:

ERROR: LoadError: Result index of attribute MathOptInterface.VariablePrimal(1) out of bounds. There are currently 0 solution(s) in the model.

The constraints represent additional knapsacks with weight constrictions = 20,30,40,â€¦

The code should return the number of items of the parameterlist to fill the 3 knapsacks effectively.

Is there a way to implement these addtional constraints?

Do I even use the right Package for the varied unbounded knapsack problem?

Thanks.