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Let's say I have a pytorch-model describing the evolution of some multidimensional system based on its own state x and an external actuator u. So x_(t+1) = f(x_t, u_t) with f being the artificial neural network from pytorch.

Now i want to solve a dynamic optimization problem to find an optimal sequence of u-values to minimize an objective that depends on x. Something like this:

min sum over all timesteps phi(x_t)

s.t.: x_(t+1) = f(x_t, u_t)

Additionally I also have some upper and lower bounds on some of the variables in x.

Is there an easy way to do this using a dynamic optimization toolbox like pyomo or gekko?

I already wrote some code that transforms a feedforward neural network to a numpy-function which can then be passed as a constraint to pyomo. The problem with this approach is, that it requires significant reprogramming-effort every time the structure of the neural network changes, so quick testing becomes difficult. Also integration of recurrent neural networks gets difficult because hidden cell states would have to be added as additional variables to the optimization problem.

I think a good solution could be to do the function evaluations and gradient calculations in torch and somehow pass the results to the dynamic optimizer. I'm just not sure how to do this.

Thanks a lot for your help!

I'm trying to solve a problem using Johnson's rule in GEKKO but unfortunately with no luck. It can easily be solved in excel, but I am trying to do it in python.

The problem:

there are two machines (Machine T and Machine K) that work one after the other. A product must go through machine T first and after that through Machine K. both machines can work simultaneously, but each machine cant work on more than 1 product at a time. the factory needs to minimize the time of production of all given products (i.e. minimize the idle time of machine k)

Products time for each machine:

...

I am trying to simulate 3D plane flight. I have an issue with gamma value (Flight-path angle). It gets out of its bounds and then, the simulation stops. The gamma value is being calculated by this equation:

I turned it into this: m.Equation(gamma.dt()==tf*((L*m.cos(Mu)-mass*g*m.cos(gamma))/mass*V))

The target of the simulation is for the plane to reach certain X an Y values(m.Minimize(w*final*(x-pathx)**2) and m.Minimize(w*final*(pathy-y)**2)), while minimizing fuel consumed m.Maximize(0.2*mass*tf*final).

The solver controls gamma value, by controlling lift coefficient Cl, which affects the lift value L, which, in turn, affects gamma value. The equation that calculates lift L value looks like this: m.Equation(L==0.5*Ro*(V**2)*(Cl)*S). But in the end the solver can not control gamma value till the plane gets to its destination.

What could be messing with it?

My code:

I am trying to optimize a summation function. From a similar question, I found half an answer for my problem(I have made my objective function similar to the one in that question).

Problem :

Attempt :

I am trying to solve a MINLP problem using first the IPOPT solver to get an initial solution then the APOPT to get the mixed integer solution. However, I get the following error when calling the APOPT solver:

Error: Exception: Access Violation At line 359 of file ./f90/cqp.f90 Traceback: not available, compile with -ftrace=frame or -ftrace=full Error: 'results.json' not found. Check above for additional error details Traceback (most recent call last): File "optimisation_stack.py", line 244, in Optimise_G(t,ob, jofbuses, q, qc, s, oa, k, l, T, G_previous, C, Y, G_previous, G_max, G_min) File "optimisation_stack.py", line 134, in Optimise_G sol = MINLP(xinit, A, B, A_eq, B_eq, LB ,UB, t, ob, jofbuses, q, qc, s, oa, k, l, T, G_previous, C, Y, G_previous) File "optimisation_stack.py", line 215, in MINLP m_APOPT.solve(disp = False) File "C:\Users\Zineb\AppData\Local\Programs\Python\Python37\lib\site-packages\gekko\gekko.py", line 2227, in solve self.load_JSON() File "C:\Users\Zineb\AppData\Local\Programs\Python\Python37\lib\site-packages\gekko\gk_post_solve.py", line 13, in load_JSON f = open(os.path.join(self._path,'options.json')) FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Zineb\AppData\Local\Temp\tmptdgafg1zgk_model1\options.json'

My code is the following, I tried to simplify it as much as possible :

I want to maximise a concave function with two inputs

max 2 * x1 ** .8 + 1.4 * x2 ** .9

st x1 + x2 == C

using Gekko, but I get a error code -2.

So I am simulating plane flight. The plane flies certain distance (pathx and pathy variables) and then the simulation stops, when certain pathx and pathy values are reached. The solver is trying to minimize the fuel consumption (m.Maximize(mass*tf*final), by maximizing the mass value. The solver controls the accelerator pedal position (Tcontr).

Right now the termination condition is defined like this:

For X axis:

I cannot seem to figure out why IPOPT cannot find a solution to this. Initially, I thought the problem was totally infeasible but when I reduce the value of col_total to any number below 161000 or comment out the last constraint equation that contains col_total, it solves and EXITs with an Optimal Solution Found and a final objective value function of -161775.256826753. I have solved the same Maximization problem using Artificial Bee Colony and Particle Swamp Optimization techniques, and they solve and return optimal objective value function at least 225000 and 226000 respectively. Could it be that another solver is required? I have also tried APOPT, BPOPT, and IPOPT and have tinkered around with the tolerance values, but no combination none seems to work just yet. The code is posted below. Any guidance will be hugely appreciated.

I am trying to do a dynamic simulation of ammonia reactor using GEKKO. Unfortunately, my code error says that it can't reach a solution. The equations I am trying to solve are steady-state in terms of species continuity equations but dynamic in terms of heat balance. I used the code shown below to first solve for the steady-state condition, which it does successfully by setting the IMODE = 1. However, when I import the steady-state results and used it to initialise my dynamic-state code, no solution is achieved. I do not understand how my code is not working when solving for the dynamic model since it worked fine for the steady-state model. I have tried IMODE = 4 and 7 but neither of them worked. Does anyone have any idea why my code is not working for the dynamic simulation?