pyomo

How to set the timeout for Pyomo solve() method ? More specifically, to tell pyomo, after x seconds, return the optimal solution currently found ? So I was able to find the answer via pyomo documentation and I thought it would be helpful to share. To set the timeout for Pyomo solve() method: solver.solve(model, timelimit=5) However this will throw pyutilib.common._exceptions.ApplicationError: "Solver (%s) did not exit normally" % self.name ) if the solver is not terminated. What I really want is to pass the timelimit option to my solver. In my case of cplex solver, the code will be like this:

I am currently trying to solve this problem. I need to maximize the profit of this company. That s the code I currently have: from pyomo.environ import * from pyomo.opt import * opt = solvers.SolverFactory("ipopt") model = ConcreteModel() model.x1 = Var(within=NonNegativeIntegers) model.x2 = Var(within=NonNegativeIntegers) model.y1 = Var(within=NonNegativeIntegers) model.y2 = Var(within=NonNegativeIntegers) model.b1 = Var(within=Boolean) model.b2 = Var(within=Boolean) model.c1 = Constraint(expr = model.x1 + model.x2 + model.y1 + model.y2 <= 7000) model.c2 = Constraint(expr = 2*model.x1 + 2

I wish to optimize a Fortran function using Pyomo. Both the objective function and the constraints are are written in Fortran. Based on the answer given here , we can use ExternalFunction expression object. But I am not able to get the results even for the simplest function. Given below is a reproducible example which consists of the Fortran function, the python (Python 2.7.12) script, the commands executed for optimization and the error. Fortran function file ( funcs.f ) - SUBROUTINE OBJ1(ARG,OBJ) DOUBLE PRECISION, INTENT(IN) :: ARG(2) DOUBLE PRECISION, INTENT(OUT) :: OBJ OBJ = ARG(1)+ARG(2)

I am writing because I am having trouble installing Pyomo using pip on Python 3.6 on Mac OSX 10.14 I'm following the installation instructions here . I can successfully execute the first command: pip install pyomo But when I run into trouble when I'm supposed to run the following command to install additional dependencies: pyomo install-extras Running this command produces the following error: Traceback (most recent call last): File "/usr/local/bin/pyomo", line 11, in <module> sys.exit(main()) File "/usr/local/lib/python3.6/site-packages/pyomo/scripting/pyomo_main.py", line 82, in main retval

So I asked a question a month ago. I had a really nice answer to that question. I wanted to test if the answer works right now. But I am getting ModuleNotFoundError . I did following before testing: conda install -c conda-forge pyomo conda install -c conda-forge pyomo.extras I want to run this script in my code (copy/pasted from the other question): from pyomo.util.infeasible import log_infeasible_constraints ... SolverFactory('your_solver').solve(model) ... log_infeasible_constraints(model) Error I encounter: N:\urbs>python runme.py Traceback (most recent call last): File "runme.py", line 9,

I want to be able to define an integral in pyomo as part of an objective function. I cannot figure out what kind of expression is needed for the integral. Here's my best guess: model = ConcreteModel() model.t = ContinuousSet(bounds = (0,1)) model.y = Var(model.t) model.dydt = DerivativeVar(model.y, wrt=(model.t)) def myintegral(model,i): return model.dydt[i] model.n = Integral(model.t, wrt=model.t, rule=myintegral) # this line is the trouble def myobjective(model): return model.n model.obj = Objective(rule=myobjective) The error is: TypeError: A callable type that is not a Pyomo expression can

I am working on an algorithm in python that needs to modify concrete (mixed-integer nonlinear) pyomo models. In particular, I need to know which variables are present in a general algebraic constraint . E.g. for a constraint model.con1 = Constraint(expr=exp(model.x_1) + 2*model.x_2 <= 2) I would like to make a a query (like model.con1.variables ) which returns (a list of) the variables ( [model.x_1,model.x_2] ). In this documentation I find that for linear constraints, the parameter variables exactly serves my purpose. However, the models I'm working with will also contain general algebraic

I want to use "pyomo" for my studies. I installed pyomo via easy_install coopr install instructions , Pyomo needs a solver to work so I wanted to install the (GNU Linear Programming Kit) glpk_webpage , pyomo seems to be installed just right because I can import it in spyder (i am using WinPython-64bit-2.7.5.3) with import coopr.pyomo However, I cannot do anything without glpk I guess... I downloaded glpk-4.52 (latest version) from the ftp server but I do not know what to do with the batch files I found in the "w64"-folder I should use(?) according to " Installing GLPK " I do not have Visual