MiNLP | XiaoMi Natural Language Processing Toolkits | Natural Language Processing library

I am generating the following gams program with my c++ program

I am using pyomo and Baron to solve a MINLP. I declared a NonNegativeIntegers variable but when Baron solves the problem, the variable is: 2e-06 !!

How is it possible?!

There are 25 candidates on Neo blockchain that receive votes. Every voter except me has voted. The candidates ranked 1st to 7th each will give 200 dollars, proportionally to people who vote for them. And the 8th to 21st will each distribute 100 dollars. For example, I cast 100 votes to the currently 8th candidate, pushing him/her to 7th position, and other people voted 900 votes for him, so I get 200*100/(900+100)=20 dollars from the candidate.

I know the voting result yielded by all the other voters, and I can additionally cast N votes to n candidates at maximum, because I have only n agents as my delegates. That is, my voting vector should have no more than n nonzero values, and the sum of my voting vector should equal to N. How should I vote for highest profit?

("Dollar" is written as "GAS" in the following codes. And the number of votes I can cast is written as "NEO" in the codes)

These codes makes an error: Endogenous $ operation not allowed
(I have not figured out how to deal with the change of position because of my vote. This may be very difficult. ) (LINDO solver gives the best results for now)

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 am trying to solve a MINLP problem using gekko. My code is the following:

I have a function with an indefinite hessian and I would like to make sure GEKKO can support quasiconvex optimization. Note that I am formulating an MINLP model and those functions are present in the objective while the constraints are linear for the parameters.

Are there any special conditions to achieve a global optimum when dealing with quasiconvex function optimization using GEKKO? Additionally, are there any performance differences with a quasiconvex function as opposed to a convex problem?

Thank you for your time.

I am trying to solve a MINLP problem using GEKKO. My code is the following:

I am studying the batchdes.lst file for MINLP model batchdes in GAMS library. The objective function is

Defining objective function obj.. cost =g= sum(j, alpha(j)*(exp(n(j) + beta(j)*v(j)))); However, in the list of equations in the .lst file is presented as ---- obj =G= objective function definition

obj.. - (25141.1498186984)*v(mixer) - (64131.2769053431)*v(reactor) - (49066.7923833869)*v(centrifuge) - (41901.9163644973)*n(mixer) - (106885.461508905)*n(reactor) - (81777.9873056449)*n(centrifuge) + cost =G= 0 ; (LHS = -230565.365179047, INFES = 230565.365179047 ****)

What kind of operation has been applied here? How the exp() translated? Is this a feature of GAMS or the Solver selected?

I implemented the same model to Pyomo and solve with the same solver from GAMS, however the Obj does not look the same in the .lst file.

Thanks!

I'm using gekko to solve a MINLP problem. I'm using the APOPT solver since is the only one that can provide integer solution, which are strictly needed in my case.

The issue I have is that every time I run the solver I have a different solution, even for very small cases, so I can't be sure about the optimality of the solutions. From some solutions to others there are important differences in the objective final value.

I've noticed that only 1 iteration takes place, and I don't know if it should be like this. Also, it runs for less than 1 second when it could run longer and find better solutions.

Below the output of one of this runs: