Incorrect Solution to simple model with inequalities

[wingmanzz]

Below is an APM file

Model
Parameters
    p1 = 200
    p2 = 100
    p3 = 50
    p4 = 1.4
    p5 = 6.1
End Parameters
Variables
    v1 = 0
    v2 = 0
    v3 = 0
    v4 = 0
    v5 = 0
    v6 = 0
    v7 = 0
End Variables
Equations
    v1>=p3
    v2>=p3
    p1>=((0+v1)+v2)
    v1<=p2
    v2<=p2
    v3=((0+v1)+v2)
    v4=((v1)*(p4))
    v5=((v2)*(p5))
    v6=(v4-v1)
    v7=(v5-v2)
    ((0+v6)+v7)>0
    v3<((0+v4)+v5)
    v4>((v3)*(2.3))
    v5>((v3)*(2.3))
    minimize (-((0+v6)+v7))
End Equations

and here is the result:

{ "time" : [0.00], "p1" : [ 2.0000000000E+02], "p2" : [ 1.0000000000E+02], "p3" : [ 5.0000000000E+01], "p4" : [ 1.4000000000E+00], "p5" : [ 6.1000000000E+00], "v1" : [ 5.0000000000E+01], "v2" : [ 1.1475409847E+01], "v3" : [ 3.0434782609E+01], "v4" : [ 7.0000000000E+01], "v5" : [ 7.0000000065E+01], "v6" : [ 2.0000000000E+01], "v7" : [ 5.8524590219E+01], "slk_1" : [ 1.4507972462E-24], "slk_2" : [ 9.1279503106E-09], "slk_3" : [ 1.3852459015E+02], "slk_4" : [ 5.0000000000E+01], "slk_5" : [ 8.8524590153E+01], "slk_11" : [ 7.8524590219E+01], "slk_12" : [ 1.0956521746E+02], "slk_13" : [ 0.00 ], "slk_14" : [ 6.5440496894E-08] }

Notice the inequality for v2 (v2 >= p3, where p3 = 50). The solution comes up with v2=11.47. Why is this equation not honored? Did I setup the model incorrectly? It seems to happen all the time.

[APMonitor]

All solvers report that they fail to find a solution to this problem with IPOPT reporting that the problem may be infeasible. Infeasible means that the problem is over-specified and all of the constraints cannot be simultaneously satisfied.

EXIT: Converged to a point of local infeasibility. Problem may be infeasible.

 An error occured.
 The error code is            2

 ---------------------------------------------------
 Solver         :  IPOPT (v3.12)
 Solution time  :   2.610000000277068E-002 sec
 Objective      :   -78.5577369813809     
 Unsuccessful with error code            0
 ---------------------------------------------------

I recommend that you check m.options.APPSTATUS (for Gekko) when the solver exits to make sure it is a good solution (=1) or a bad solution (=0). Here is additional information on the APPSTATUS parameter. You can retrieve 'infeasibilities.txt' from your local run directory to help identify the infeasible equation(s).

[wingmanzz]

oh go it. I was under the impression GEKKO threw an exception if the solution was infeasible. Not sure where I got that from. Thanks.

[APMonitor]

It should throw an exception if it is infeasible. Could you post the original Gekko code instead of the APM model if this is still an issue?

[loganbeal]

@APMonitor It doesn't throw an exception because the results can still be useful (eg plotting to diagnose the problem). Scipy.optimize also does not throw exceptions on unsuccessful optimizations. I still think we should leave it to the user to read the solver output, check the status attribute and/or analyze the results. If you really want another indication, a Warning should suffice.

[APMonitor]

@loganbeal Thanks for the insights. I agree that infeasible solutions can useful. I like the idea of using a warning. Otherwise many will think a solution is successful. I've been misled many times by Scipy.optimize returning a solution with no indication that it is a bad solution. In each case, it took some troubleshoot to realize that it was not solved. The raise Warning seems to block the program from finishing. If we do implement a warning, any objections to using the warnings package?

import warnings
warnings.warn("Infeasible solution")

[APMonitor]

I'll create a new Issue (Feature Request) to add a warning message for failed solutions.