Create arrays of variables in loop GEKKO - optimization

Question

I'm wondering if it is possible to create arrays of variables with different lenghts in a loop in GEKKO.
Below is just a simple example of what I mean. Parameters in the list "lengths" define what length each GEKKO array should have:
lengths = [10,20,30]
m = GEKKO()
for i in lengths:
# something...
So from this I would like to get something like:
array1 = m.Array(m.Var,10)
array2 = m.Array(m.Var,20)
array3 = m.Array(m.Var,30)
In the real problem that I'm trying to solve there will be quite many arrays that I want to include in the optimization, and they might be different depending on the situation. So it is not a good option to manually create them every time.

Answer 1

There is no problem to define arrays in a loop. Here is an example:
from gekko import GEKKO
model = GEKKO(remote=True)
lengths = [10,20,30]
m = GEKKO()
x = []
for i in lengths:
x.append(m.Array(m.Var,i))
for j in range(i):
m.Minimize((x[-1][j]-j)**2)
m.solve()
for xi in x:
print(xi)
This gives a unique solution where the value is equal to the index.
This is Ipopt version 3.12.10, running with linear solver ma57.
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 60
Total number of variables............................: 60
variables with only lower bounds: 0
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 0
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.1310000e+04 0.00e+00 5.80e+01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 0.0000000e+00 0.00e+00 0.00e+00 -11.0 2.90e+01 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 1
(scaled) (unscaled)
Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00
Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00
Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00
Number of objective function evaluations = 2
Number of objective gradient evaluations = 2
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 1
Total CPU secs in IPOPT (w/o function evaluations) = 0.001
Total CPU secs in NLP function evaluations = 0.000
EXIT: Optimal Solution Found.
The solution was found.
The final value of the objective function is 0.000000000000000E+000
---------------------------------------------------
Solver : IPOPT (v3.12)
Solution time : 4.600000000209548E-003 sec
Objective : 0.000000000000000E+000
Successful solution
---------------------------------------------------
[[0.0] [1.0] [2.0] [3.0] [4.0] [5.0] [6.0] [7.0] [8.0] [9.0]]
[[0.0] [1.0] [2.0] [3.0] [4.0] [5.0] [6.0] [7.0] [8.0] [9.0] [10.0] [11.0]
[12.0] [13.0] [14.0] [15.0] [16.0] [17.0] [18.0] [19.0]]
[[0.0] [1.0] [2.0] [3.0] [4.0] [5.0] [6.0] [7.0] [8.0] [9.0] [10.0] [11.0]
[12.0] [13.0] [14.0] [15.0] [16.0] [17.0] [18.0] [19.0] [20.0] [21.0]
[22.0] [23.0] [24.0] [25.0] [26.0] [27.0] [28.0] [29.0]]