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I am trying to run a differential equation system solver with GEKKO in Python 3 (via Jupyter notebook).

For bad initial parameters it of course immediately halts and says solution not found. Or immediately concludes with no changes done to the parameters / functions.

For other initial parameters the CPU (APM.exe in taskmanager) is busy for 30 seconds (depending on problem size), then it goes back down to 0% usage. But some calculation is still running and does not produce output. The only way to stop it (other than killing python completely) is to stop the APM.exe. I have not get a solution.

When doing this, I get the disp=True output me it has done a certain number of iterations (same for same initial parameters) and that:

No such file or directory: 'C:\\Users\\MYUSERNAME\\AppData\\Local\\Temp\\tmpfetrvwwwgk_model7\\options.json'

The code is very lengthy and depends on loading data from file. So I can not really provide a MWE.

Any ideas?

Swift
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1 Answers1

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Here are a few suggestions on troubleshooting your application:

  • Sequential Simulation: If you are simulating a system with IMODE=4 then I would recommend that you switch to m.options.IMODE=7 and leave m.solve(disp=True) to see where the model is failing.
  • Change Solver: Sometimes it helps to switch solvers as well with m.options.SOLVER=1.
  • Short Time Step Solution: You can also try using a shorter time horizon initially with m.time=[0,0.0001] to see if your model can converge for even a short time step.
  • Coldstart: For optimization problems, you can try m.options.COLDSTART=2 to help identify equations or constraints that lead to an infeasible solution. It breaks the problem down into successive pieces that it can solve and tries to find the place that is causing convergence problems.

There are additional tutorials on simulation with Gekko and a troubleshooting guide (see #18) that helps you dig further into the application. When there are problems with convergence it is typically from one of the following issues:

  • Divide by zero: If you have an equation such as m.Equation(x.dt()==(x+1)/y) then replace it with m.Equation(y*x.dt()==x+1).
  • Non-continuously differentiable functions: Replace m.abs(x) with m.abs2(x) or m.abs3(x) that do not have a problem with x=0.
  • Infeasible: Remove any upper or lower bounds on variables.
  • Too Many Time Points: Try fewer time points or a shorter horizon initially to verify the model.

Let us know if any of this helps.

John Hedengren
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    Hello, trying the solutions out at the moment! Thank you very much for the answer! Appreciate your whole effort a lot!! In the meantime: is there nowadays a smarter way when solving a PDE, than to do the spatial derivative manually: `m.Equations([u[i].dt() == v[i]for i in range(npx)])` `m.Equations([v[i].dt() == c**2*(u[mod(i+1,npx)]-2.0*u[mod(i,npx)]+u[mod(i-1,npx)])/dx**2 for i in arange(0,npx,1)])` this is taking from your original paper from 2018 (with some minor changes to have it in two lines) on Gekko. Thanks! – Swift Apr 12 '20 at 20:39
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    Here is additional information on PDEs: https://apmonitor.com/do/index.php/Main/PartialDifferentialEquations Yes, you'll need to do the spatial discretization yourself. – John Hedengren Apr 13 '20 at 00:18