pyomo

问题 Pretty basic question, but where can I find solver log files from Pyomo? I have a local installation of the COIN-OR solvers on an Ubuntu machine. This is happening in a Jupyter notebook, but I'm getting the same error message when I run the .py file from terminal. solverpath_exe='~/COIN-OR/bin/couenne' opt = SolverFactory('couenne', executable = solverpath_exe) opt.solve(model,tee=True) --------------------------------------------------------------------------- ApplicationError Traceback

问题 How can I install ipopt solver to use it with pyomo in ubuntu??? I have already download the zip file of ipopt but I don'n find any specific instructions how to install it and use it with pyomo. 回答1: If you downloaded a binary then you just have to make sure that the ipopt executable is in your PATH environment variable. If you are trying to install from source code you should follow the instructions here. Your last option is to use anaconda to install an ipopt binary using the command: conda

问题 I use pyomo to write the script, but use external solver to do the optimization. The external solver return a *.sol file. My question is that if pyomo or any package of python can decode the sol file, and pass the decision variable result into the original model. Therefore, we can do some post analysis. 回答1: I wanted to add this as a comment but I dont have enough reputation. I was facing a similar problem with CPLEX. See this question. You have to basically store the variable map and use it

问题 I am solving an ILP with Pyomo and GLPK. Running on the command line, pyomo outputs the time in seconds it takes for each step. Now, when I look into the results.json file that is output, I get a section that looks like this: "Solver": [ { "Error rc": 0, "Statistics": { "Branch and bound": { "Number of bounded subproblems": "13", "Number of created subproblems": "13" } }, "Status": "ok", "Termination condition": "optimal", "Time": 2.9578073024749756 } ] What is the meaning of "Time" in this

问题 Using the PATH solver directly, i fail to solve the problem presented below. The original problem was sourced from https://prod.sandia.gov/techlib-noauth/access-control.cgi/2015/155584.pdf , which seems to claim the problem was solved. Using a nonlinear formation it is possible to solve. Whether this is a versioning issues in pyomo or PATH, it is difficult to tell. I am running pyomo 5.5.x and pathampl sourced from http://pages.cs.wisc.edu/~ferris/path.html from pyomo.environ import * from

问题 I want to operate on abstract sets. But it dosen't work. from pyomo.environ import * m = AbstractModel() m.A = Set(initialize=[0,1,2]) m.B = Set(initialize=[0]) m.C = m.A-m.B instance = m.create_instance() for c in instance.C.value: print(c) TypeError: 'NoneType' object is not iterable 回答1: Based on what you told to Qi Chen, here is a working example of your code if you used the AbstractModel formulation. The thing, with abstract models, is that it doesn't do much more than delay the

问题 I have a pretty big model (around 5 million variables and constraints). The building time is a few minutes and the solving time is a few minutes too (with gurobi) But it takes very long to write the model (about 2 hours) This is the time if I use model.write('model.lp', io_options={'symbolic_solver_labels': True}) to be able to record it It's about the same time if I use SolverFactory and solve directly the model from pyomo here is a little sample, I understand that this model is trivial for

问题 I am trying to use pyomo to solve TSP problem. I have successfully implemented using python and Gurobi but my Gurobi license expired so I want to now use pyomo and GLPK to implement the TSP problem. This is what I could come up with so far. It is not working the objective value is 0. Can you please help. from pyomo.environ import * from pyomo.opt import SolverFactory import pyomo.environ n=13 distanceMatrix=[[0,8,4,10,12,9,15,8,11,5,9,4,10], [8,0,7,6,8,6,7,10,12,9,8,7,5], [4,7,0,7,9,5,8,5,4,8

问题 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

问题 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)